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I
Ben-Gurion University of the Negev The Jacob Blaustein Institutes for Desert Research
The Albert Katz International School for Desert Studies
Assessment of potential energy savings in Israel through
climate-aware residential building design.
Thesis submitted in partial fulfillment of the requirements for the degree of
"Master of Science"
By Morel Weisthal
Under the Supervision of: Prof. David Pearlmutter
Adjunct Advisor: Aviva Peeters
Department of Man in the Desert / Environmental Studies
Author's Signature Date 23/3/2014
Approved by the Supervisor… Date …23/3/2014
Approved by the Adjunct-Advior…
Approved by the Director of the School ……………
II
Acknowledgment
I would like to express my deep gratitude and appreciation to my thesis supervisor, Prof.
David Pearlmutter for the patient guidance, constructive advice and the significant time
he dedicated.
Many thanks to Dr. Aviva Peeters, my co-advisor, for providing guidance and wise
advice that helped greatly with the GIS section of the study.
My gratitude is also addressed to Prof. Abraham Yezioro for providing essential access
to ENERGYui software capabilities and useful advice. Thanks also to Mr. Hanoch
Levin for sharing his insights regarding thermal analysis and modeling of buildings.
I thank Mrs. Yael Paaran the CEO of Israeli forum for renewable energy and Dr. Shahar
Dolev for providing funds and helpful advices for promoting the research.
Finally, many thanks to the faculty members of the environmental studies department:
Prof. Evyatar Erell, Prof. Isaac Meir, Prof. Hendrick Bruins and Prof. Yodan Rofe for
the significant intellectual contribution that was provided to me during and between the
courses of the degree completion.
III
Abstract Energy usage is growing in most regions of the world, alongside population growth and
development processes that are intended to improve standards of living. However, given
basic resources limitations and negative impacts on the environment, present energy
consumption trends are unsustainable.
Buildings consume a significant proportion of primary energy. Worldwide about 30% of
energy is channeled into the residential sector (IEA, 2006; Griffith, 2007). Many studies
indicate that increasing efficiency through building design can feasibly yield significant
energy savings, and realizing this potential through bio-climatic design has been widely
scrutinized at the scale of the individual building. At the same time, there is a lack of
quantitative research assessing the potential for energy savings through climate-
conscious building design at a national scale, and this is considered to be one of the
obstacles preventing a wider implementation of bio-climatic design in many countries.
This study focuses on Israel, which has not realized its potential for energy conservation
through efficient, climate-conscious residential buildings. This is despite the existence
of relatively high technological capabilities with regard to solar energy, high awareness
which has even been translated into design guidelines and voluntary standards for
climate-conscious green building that is adapted to the local conditions in Israel and
studies and reports that highlight the potential for energy savings in green buildings.
In light of these concerns, this study sets as its main objective to quantitatively assess
and predict the potential for energy savings in residential buildings in Israel,
hypothetically assuming that newly designed and constructed buildings would be
designed according to commonly known and publicized best practices.
The objective of this is pursued through a synthesis of multi-scale quantitative
assessments and analyses that were carried out in four separate stages: in the first stage,
the potential for operational energy savings was estimated on a per-unit basis in
buildings, using thermal simulation techniques. This energy savings assessment takes
into account four different climate zones in Israel and different residential building
typologies common in Israel. The second stage includes an innovative assessment, by
using advanced GIS techniques, of the mutual climatic influence among buildings in an
urban environment and its influence on the energy consumption in those buildings. The
results of this stage are used for refinement of the results obtained in the first stage. In
the third stage, spatial analysis techniques and data on current building construction
patterns are used to estimate and forecast future rates of residential construction in the
IV
different climate zones in Israel. These stages are all used as building blocks for
constructing, in the fourth and final stage, a predictive model for estimating the overall
potential for energy savings through climate-conscious building design in Israel, as
projected for the near future according to three different population growth scenarios.
According to this prediction model, in 2035 the energy savings potential in Israel is
projected to reach between 1,700 and 3,500 million kWh, and the annual average of
savings range between 920 million kWh (low growth scenario) and 1700 million kWh
(high growth scenario). These savings are on the order of magnitude of the output
generated by a large power plant in Israel, whose construction could theoretically be
avoided by realizing these savings. Further, it is shown through the basic thermal
simulation analyses that the savings allowed by improved building design approaches
50% when compared with a business-as-usual scenario based on the existing mandatory
standard and common practice.
V
Table of contents
1. Introduction. .................................................................................... 1
1.1. Research objectives. ...................................................................................................... 2
1.2.2. The Green-Buildings context. ........................................................................... 5
1.2.3. Standards for evaluating the energy efficiency of green buildings in Israel. ............ 6
1.2.4. Climate-aware building design influence and energy efficiency. ............................ 7
1.2.5. Climate zones in Israel – the planning perspective. .............................................. 8
1.2.6. Urban influence on energy consumption of buildings. .......................................... 9
1.3. Research strategy and outlines ......................................................................................10
2. Assessment of energy savings potential through climate-conscious
design of buildings. ............................................................................ 12
2.1. Assessment of energy saving potential at a “per unit” building scale. ............................12
2.2. Simulation software – background ................................................................................14
2.3 Modeling Characteristics in the Simulation Procedure....................................................16
2.3.1. Building Model Characteristics. ...................................................................... 16
2.3.2. Simulation Software Characteristics................................................................. 17
2.4. Characteristics of the Reference Building......................................................................18
2.5. Modeling characteristics of energy efficient building according to SI 5282. .................20
2.6. Thermal simulations methods. ......................................................................................20
2.6.1. Assessment the energy consumption gap between reference and thermally improved
buildings. .............................................................................................................. 21
2.7. Simulation results. ........................................................................................................22
2.7.1 Effect of climate zone and apartment location on energy consumption. ................. 22
2.7.2. Simulation results – Assessing the Saving Potential in Buildings According to
Climate zones. ....................................................................................................... 23
2.7.3. Results – Distribution of Electricity Consumption for Heating and Cooling in
Improved Buildings According to Climate Regions. ................................................... 24
2.7.4. Potential Electricity saving according to Building Types and Climate zones. ........ 25
2.7.5. A Reference Building versus an Improved Building - Comparison between Annual
Data of Consumption for Acclimatization According to Israeli Different Climate zones. . 26
2.8. Summary of Simulation Results for Assessing Energy Savings at the Level of the Single
Building. .............................................................................................................................27
3. Saving in Electricity Consumption According to Design Elements. 28
3.1 Methods for Estimating Energetic Streamlining of a Building by Modeling the Effect of
the Design Elements. ...........................................................................................................28
3.2 The Results of the Sensitivity Analysis Simulations. ......................................................29
VI
3.2.1. Recognition of relative saving potential by design parameters. ............................ 33
4. Urban influence on buildings consumption. ................................... 36
4.1. Background ..................................................................................................................36
4.1.1. Microclimate in Israel. ................................................................................... 38
4.1.2 Assessment of shadow influence in the urban context – scientific background ....... 38
4.2. Methods – urban shadow geometry assessment .............................................................41
4.2.1. Shadow footprint model development – guiding considerations .......................... 41
4.2.2. Case study features ........................................................................................ 42
4.2.3. GIS Database input –buildings geometry as a vector layer .................................. 43
4.2.4. Solar geometry input ...................................................................................... 45
4.2.5. Automatic identification of building facets ....................................................... 46
4.2.6. Shadow footprint and geometry calculation in a 2D plane .................................. 48
4.2.7. Application of the SFM on the case study area .................................................. 50
4.3. SFM results analysis .....................................................................................................53
4.4. Validation of the SFM. .................................................................................................58
4.5. SFM possible applications. ...........................................................................................60
4.6. Assessment of shadow area cast on walls – Yearly trend analysis. .................................61
5. Energy saving potential assessment: National-scale perspective. .... 67
5.1. Introduction – National scale assessment of energy saving potential. .............................67
5.2. A national-scale spatial and temporal construction analysis: methodological outline. ....67
5.2.1 Recognition of Building Completion Trends According to Climate zones in Israel. 69
5.2.2. Identifying Building Trends by Apartment Floor-Height According to Climate
Regions. ................................................................................................................ 72
5.3. Estimating a Future Change rate of construction completion. ........................................73
6. National-scale Energy Savings Forecast Model. ............................. 76
6.1. Forecast Model Results – Annual Savings Potential for Electricity Consumption in
Buildings.............................................................................................................................77
6.1.1 Scenarios According to Climate Zones – A Forecast of Annual Cumulative
Electricity Savings.................................................................................................. 77
7. Conclusions and discussion. ........................................................... 80
7.1. Points for possible improvement and future recommended research. .............................83
8. References ..................................................................................... 84
9. Appendix ....................................................................................... 90
9.1 Building plan upon which the model building for the simulations was based. The plan is
of typical floor in buildings in “Ramot” neighborhood in Beer-Sheva. .................................90
9.2. Thermal Simulation software EnergyPlus system of operation (EERE, 2010). ...............90
VII
9.3. Building material used for the simulation models according to climate zones. ...............91
9.4. Simulation results of electricity consumption and potential savings in residential units as
function ...............................................................................................................................92
of floor location and climate zones. .....................................................................................92
9.5. Buildings design parametric analysis of energy saving potential simulation ...................96
9.6. Shadow footprint model (SFM) as created using the ArcGIS ModelBuilder. .................97
9.7. Annual analysis of shadow area (Sq.m.) cast on walls in case study zone. (Based on SFM
results). ............................................................................................................................. 100
9.8. Shadow influence on energy savings – Simulation analysis results (Refinement factor for
national-scale analysis). ..................................................................................................... 101
9.9. Construction completion distribution 1995-2012 by climate zone and residential unit type
– results of a GIS analysis. ................................................................................................ 102
Figure Index.
Figure 1: Breakdown of electricity consumption in Israel by end-use sectors.......................... 4
Figure 2: Breakdown of electricity production based on fuel type in Israel (Agmon, 2008) ...... 4
Figure 3: Simplified schematics of the research outline. .................................................... 10
Figure 4: Schematic description of the simulation procces ................................................. 13
Figure 5: Climate zones in Israel as classified for building’s planning and design according to
compulsory standard SI 1045. ......................................................................................... 15
Figure 6:Typical floor plan of an apartment building used for the simulation. ...................... 16
Figure 7: Example of external wall section sample comparison. ......................................... 17
Figure 8: Electricity consumption as function of the floor height. ....................................... 22
Figure 9: Electricity consumption as function of the climate zone ....................................... 22
Figure 10: Average Annual Energy Consumption typical residential units ........................... 24
Figure 11: Simulation results showing the potential of electricity saving in different climate
zones. .......................................................................................................................... 25
Figure 12: Simulation results analysis of comparison of Total Energy Consumption for
Acclimatization ............................................................................................................. 26
Figure 13: The Results of the Simulation that Show the Overall Potential for Possible Energy
Saving.. ........................................................................................................................ 30
Figure 14: Relative energy savings achieved through the improvement of different design
parameters, compared to the reference building in Tel-Aviv (Zone A). ................................ 33
Figure 15: Relative energy savings achieved through the improvement of different design
parameters, compared to the reference building in Beer-Sheva (Zone B). ............................. 33
Figure 16: Relative energy savings achieved through the improvement of different design
parameters, compared to the reference building in Jerusalem (Zone C). ............................... 34
Figure 17:Relative energy savings achieved through the improvement of different design
parameters, compared to the reference building in Eilat (Zone D). ....................................... 34
Figure 18: The case study area in Tel-Aviv city.. .................................................................. 43
Figure 19: The GIS building layer representing the case study area.. ................................... 45
VIII Figure 20: A 2D projection of the sun path as reflected by the yearly and daily sun cycle in
Israel ........................................................................................................................... 46
Figure 21: A model output demonstration of the building facades classification according to
azimuth angle ............................................................................................................... 47
Figure 22: Demonstration of mathematical issues that were considered in the SFM building
process.. ....................................................................................................................... 48
Figure 23: Schematic sketch demonstrating principals of shadow geometry calculation used by
the SFM. ...................................................................................................................... 50
Figure 24: Schematic workflow of the SFM mode of operation from the input to output stage.
................................................................................................................................... 52
Figure 25: Demonstration of the SFM results: shadow projection of each building facet type.. 53
Figure 26: Shadow footprint polygons generated by the model according to four time
configurations. .............................................................................................................. 55
Figure 27: Shadow footprint polygons in open area of the case study, automatically generated
by the model... .............................................................................................................. 56
Figure 28: A sample of the validation of the test zone demonstrating the comparison between
an image and classified image within the case study area.................................................... 59
Figure 29: Demonstration of 2D Analysis of wall segments influenced by shadow cast in urban
built environment by neighboring building based on SFM results. ....................................... 61
Figure 30: Schematic model of spatial analysis process conducted using GIS for characterizing
construction spatial and temporal national scale trends.. ..................................................... 68
Figure 31: Spatial distribution and linkage of average construction completion area to climate
zones in settlements of Israel........................................................................................... 70
Figure 32: Distribution of Average Building Completion Area during the Years 1995-2012 as
function of climate zone.. ............................................................................................... 71
Figure 33: Distribution of Average Building Completion Area during the Years 1995-2012 as
Function of residential unit height position.. ..................................................................... 73
Figure 34: Comparison between the Annual Population Growth Trends and the Increase in the
Annual Building Completion, 1957-2011.. ....................................................................... 74
Figure 35: A Statistical Analysis of the Level of Correlation between the Annual Building
Completion Area and the Annual Population Growth Rate. ................................................ 75
Figure 36: Distribution of the Annual Saving potential Rate. .............................................. 78
Figure 37: Prediction of the accumulated Annual Electricity Saving Potential as a Result of
Improved climate-aware Building design. ........................................................................ 79
IX
Table index.
Table 1:Thermal characteristics of a reference building according to climate zones.. ............. 19
Table 2: Features and values of design elements for a reference building, in accordance with
standard SI 1045 requirements. ....................................................................................... 19
Table 3: Summarize of recommended building’s design features for an thermally improved
building. ....................................................................................................................... 20
Table 4: Modeling design attributes comparison between SI 1045 reference and SI 5282. ...... 21
Table 5: Summary of Simulation Results. ........................................................................ 27
Table 6: Summary of Simulation Results – Percentage of Saving in Annual Consumption for
Acclimatization.. ........................................................................................................... 27
Table 7: Classification of design parameters into 4 fields of building design interest. ............ 30
Table 8: Isolated Effect of the Design Elements on Energy Saving ..................................... 31
Table 9: Minimal Shadow area cast on walls of buildings in the case study area. .................. 62
Table 10: Maximal possible Shadow area cast on walls of buildings in the case study area
during 9:00, 12:00 and 15:00 during summer and winter solstice days. ................................ 63
Table 11: The relative % of shadow area cast on the facets out of the total facet area classified
by their direction. .......................................................................................................... 64
Table 12: The influence of shadow on the yearly energy savings in a basic thermal performing
building according to SI 1045. ........................................................................................ 65
Table 13: Distribution of Average Building Completion Area during the Years 1995-2012.. .. 70
1
1. Introduction.
It is estimated that about 30% of worldwide energy usage is concentrated in residential
buildings as an end-use sector consumer (IEA, 2006; Griffith, 2007). The proportion of
energy consumed in buildings overall is about 40% in the EU (Steemers, 2003) and in
the US (DOE, 2006), and a further increase in energy demand is expected in many
regions of the world due to the increasing growth rate of the population and
development processes (Omer, 2008).
The energy used today involves two major concerns: the first is an over-reliance on
fossil fuels, with their growing scarcity and the prospects of their eventual depletion
(Bently, 2002). The economic and substantive consequences of this were brought to
public awareness first in the early 70’s due to the oil crisis, which emphasized western
society's great dependence on these non-renewable resources (Golov & Eto, 1996,
Newman, 1996). The second problem is environmental: in the process of transforming
these resources into useful energy, pollutants which harm human health and greenhouse
gasses (GHG) which contribute to global warming processes (particularly CO2) are
emitted to the atmosphere (Boswell, 2010; IPCC, 2007). This anthropogenic impact has
been recognized since the early 70s, and was stated in the well-known Bruntland report,
which emphasized the responsible management of energy resources as a major aspect
within a broader multi-disciplinary approach for sustainable development. Sustainable
development is composed of economic, social and environmental processes, each of
which can benefit from improved energy efficiency (Erell, 2008). Effective energy
management, which includes the improvement of building energy performance, can
reduce the emission of greenhouse gases (GHGs), increase energy savings in general
(Huberman & Pearlmutter, 2008; Rieche et al., 2004), and improve society's economic
well-being, stability and security (NSTC, 2008; DEO, 2006).
2
Following many studies that have been done to assess energy conservation in buildings
that highlight potential benefits incorporated in climate-aware building and design,
practical measures to improve energy savings in buildings are reflected in various
international standards and codes. Yet the actual implementation of these measures is
limited (EuroACE, 2009).
Climate conscious building construction (CCBC) in Israel is held back by the lack of
quantitative knowledge on a national scale regarding spatial and temporal patterns of
energy saving potential. This is probably one of the main reasons that a proper cost
effectiveness analysis does not exist which results in reduced promotion and investment
to boost climate conscious construction in Israel
1.1. Research objectives.
In light of the concerns mentioned above, the primary objective of this research is to
quantitatively assess the nationwide potential for energy savings that can be
achieved through improved, climate-conscious design of residential buildings in
Israel. In order to accomplish this objective, the research was carried out in three
consecutive stages, each with its own scale of analysis and specific methodology:
Stage 1 (“per-unit” scale): In this stage two goals were pursued, the main one being the
assessment of potential energy savings in individual buildings, using state-of-the-art
thermal simulation software for estimating energy consumption, and a second one being
the assessment of the relative influence of specific design features on this energy
savings potential.
Stage 2- (“urban” scale): As a refinement to the assessment of potential savings in
single buildings, an analysis was performed at this stage to assess the influence of
surrounding buildings in an urban environment – particularly in terms of mutual shading
– on the energy consumption of the individual building.
3
Stage 3- (“national” scale): The temporal and spatial patterns of housing construction
were analyzed nationwide, including historical rates and geographical distribution of
building heights in different climate zones. To forecast future housing production in
Israel, up-to-date statistical databases of all urban settlements, GIS software and
advanced techniques for spatial analysis were applied to investigate historical trends in
population growth and how they relate to construction.
Stage 4 - The results of these analyses were used to generate a prediction model of the
potential for national-scale energy savings compared to“business as usual” energy
consumption in buildings up to the year 2035.
The goals, methods and results of each of these stages of the work are presented in
separate chapters, with each chapter offering a unique perspective derived from
different methods of analysis.
1.2. Research Background
1.2.1. Energy saving potential in buildings - Israel as a case study
Awareness in Israel of the need for sustainable development has increased over the past
decades, especially following the Kyoto Protocol (1998) and Israel's subsequent
commitment to reducing greenhouse gas (GHG) emissions by 20% from a "business as
usual" forecast. More recently this awareness has attained national priority (Government
Decision 2508, 2010).
About 60% of electrical energy in Israel is used in buildings, about half of this by the
residential sector and half by the commercial sector (Figure 1). This demand is
estimated to increase, on average, by some 3.5% per year (IEC, 2011), and finding ways
to save energy in buildings is a significant concern (IEC, 2011; MOEP, 2011;
McKinsey, 2009). Given the high percentage of electricity production in Israel that is
4
based on fossil fuels, (Figure 2), reducing energy consumption holds the potential for
reductions of GHG emissions and conventional pollution as well.
Many studies point out the potential for saving energy through a climate-aware "Green
Building". Furthermore, analyses and forecasts done in Israel have emphasized the
influence of energy consumption on greenhouse gas emissions and the potential of
Green Building as a central and cost-effective means to reduce emissions through
environmentally conscious design (Gabbay, 2011; McKinsey, 2009, Becker, 2008). Yet
the available studies regarding energy savings potential in buildings are not quantitative,
or lack a clear methodology upon which conclusions can be drawn at a national scale.
Figure 1: Breakdown of electricity consumption in Israel by end-use sectors. The residential building is highlighted and presents estimations of the heating and cooling loads % out of the total electricity consumption. Based on analysis of IEC (2011) data.
Figure 2: Breakdown of electricity production based on fuel type in Israel (Agmon, 2008)
Electricity production by fuel type distribution
Coal
Gas
Diesel
Mazut
5
1.2.2. The Green-Building context.
A green-building can be defined as: “The practice of creating structures and using
processes that are environmentally responsible and resource-efficient throughout a
building's life-cycle, from a building’s site planning to design, construction, operation,
maintenance, renovation and deconstruction”. The green building standard refers to
“efficient use of energy along with water, materials and other resources while protecting
the health of occupants” (EPA, 2007).
The awareness of building “green” is rising due to the increased understanding of the
need to develop in a sustainable way. Green building embodies a holistic approach for
improving the sustainability of a building according to the local conditions. The
definition of what is green varies between different countries, and is encoded differently
by various standards that define the requirements for designing a green building.
The leading green building programs in the world include the American LEED rating
system, with about 6,000 buildings certified by the U.S. Green Building Council
(USGBC, 2011), the British BREEAM with about 10,000 certified, and dozens of other
standards in different countries such as “Three Star” in China, “CASBEE” in Japan and
the Australian “Green Star”. The standards are differentiated in terms of the relative
weights which different factors (such as energy efficiency or water recycling) have on
the score which the building receives in the certificate, the stages that are considered in
the life cycle of the building, and the flexibility that is allowed in the planning process
of the building. In all the leading standards, the energy efficiency factor has a major
weight on the score (DOE, 2012; Liu et al, 2010; Wang, 2012).
It should be noted that even though many standards are available, green building has yet
to be adopted officially by governments in most cases. Standards are promoted
especially by private organizations, and compliance with them is usually voluntary.
6
1.2.3. Standards for evaluating the energy efficiency of green buildings in
Israel.
In Israel, a number of standards have been adopted by the Standard institution of Israel
which are related to energy efficiency in buildings. The first is SI 1045, which is a
mandatory standard that stipulates the minimum level of thermal insulation required in
buildings in the country's various climate zones. While this standard has been part of the
required building code since the 1980's, the level of thermal resistance which it
mandates is considered to be low, and it does not mandate any other measures of energy
efficiency other than thermal insulation.
In recent years, two voluntary standards have been adopted: SI 5281, which is a
standard for "green buildings," and SI 5282, which establishes an energy rating for
buildings. For its specific requirements regarding energy efficiency, the green building
standard (SI 5281) refers to the energy rating standard (SI 5282), which stipulates
minimum requirements not only for thermal insulation but for a range of other design
features such as thermal mass, window orientation and shading, ventilation, and system
controls. Its requirements for thermal insulation (at the highest level of compliance) are
considerably more stringent than those mandated by SI 1045, and a building designed to
meet SI 5282 is intended to be much more energy-efficient overall than one which
merely meets the legally-binding requirements of SI 1045. It should be emphasized that
energy efficiency through building design has a major role in the Israeli green building
standard and accounts for about 20% of the general rating (ILGBC, 2010).
7
1.2.4. Climate-aware building design influence and energy efficiency.
The design of modern buildings typically does not take into account local climatic
conditions to the extent that is needed for minimizing cooling and heating needs. This
kind of energy-inefficient design can be improved by the use of passive or active
building elements which can significantly reduce the thermal loads and improve the
performance of the building (Pearlmutter et al, 2010; Erell, 2008). The regional climate
zone and specific conditions of the building site determine the gap between outdoor and
indoor conditions. The higher the gap, the higher the potential of saving energy by
designing appropriate building elements that can reduce power-consuming systems such
as air-conditioning (Erell et al. 2002). Different climate zones also require different
solutions for adjusting the building design to the climate in order to achieve optimal
thermal performance (Levine et al, 2004; Yang, 2009; Givoni, 1997).
Different approaches can be taken to design a building to be energy efficient, and these
are sometimes divided into active and passive methods. With active methods the
building requires an external energy source to heat or cool the indoor space and
maintain a desired comfortable climate, while with passive methods the building itself is
designed and built to use natural climate agents for the benefit of acclimatization of the
space. Both methods are being continuously refined, with the aim of improving the
energy efficiency of the building stock and reducing acclimatization load requirements
(Henze, 2004; Troy et al., 2003; Hadley, 2004).
The potential for energy savings through climate-aware building design is higher when
the climate conditions are extreme, such as in arid zones (Harvey, 2009). Another factor
is the building’s patterns of usage; for instance residential buildings will typically
require different acclimatization then commercial or industrial buildings, due to
different user needs and expectations.
8
This research focuses on largely passive design strategies for improving the building’s
energy efficiency for acclimatization, with an emphasis on residential buildings.
1.2.5. Climate zones in Israel – the planning perspective.
Israel is located between 30˚ and 33˚ north latitude, and its land area is about 21,500
square kilometers. Even though Israel is relatively a small country, its climate is
variable for its size and can be divided into multiple climatic zones. In general it is
characterized as a sub-tropical region, with hot dry summers and short but cold and
rainy winters, and at least four sub-divisions of climate according to the updated
“Koppen-Gaigan” climate classification map (Kottec et al., 2006). Local variations in
climatic conditions are influenced by distance from the sea, elevation above or below
sea level, and latitude.
This climate variability has to be taken into consideration in climate-conscious planning
and design of buildings. The compulsory standard SI 1045 for insulation of buildings
takes into account the climatic variability in different zones, based on temperature
extremes as the principal factor. This standard stipulates insulation levels in different
areas of the country based on a map which divides Israel into four climatic zones
(described briefly here, based on “Bio-climatic building guidelines in Israel"1):
Zone A - Western coastal strip: Mediterranean climate, high heat stress during summers
and relatively mild demand for heating during winter.
Zone B - Inner hilly strip: Significant internal temperature variance between the north
and south part of the strip; the southern part has higher temperatures and is less
moderate in its daily and seasonal cycles, and generally has lower humidity than the
coastal strip (due to the greater distance from the sea).
1
(Pearlmutter et al, 2010) –) http://www.bgu.ac.il/CDAUP/guidebook.pdf(
9
Zone C - Mountainous heights: Relatively comfortable temperatures on average during
summer (though with intense fluctuations between day and night), large heating load
requirements during the cold winter season.
Zone D - Inner rift valley strip: Hot and dry relative to other zones – northern part is a
bit more humid then the southern part.
1.2.6. Urban influence on energy consumption of buildings.
Another aspect to be considered when assessing the potential for energy savings is the
effect of local climatic conditions in an urban environment. A building’s heating and
cooling loads are influenced by the local micro-climate, which differs from the
“natural” climatic conditions outside of the built-up area (Oke, 1984; Golani, 1996).
Different urban physical features at different scales are responsible for the modified
climatic conditions, such as the density of the buildings in the urban settlement, the
height-to-width ratio of urban canyons, the building materials and the spaces between
the buildings, and water sources and vegetation abundance. Those urban features are
known to modify the climatic agents within its boundaries alongside with anthropogenic
features (Grimmond et al, 2010; Kruger et al, 2009; Sailor, 2004; Steemers, 2003).
Quantifying the overall urban influence on the local microclimate is difficult due to the
high complexity of assessing temporal and spatial patterns of multiple variables
(Littlefair, 1998; Elliason, 2000; Erell, 2008; Estiri, 2012).
This research considers an important effect of the urban environment on the energy
consumption of individual buildings by addressing the mutual shading of neighboring
buildings in a densely-built urban environment. Chapter 3 of the thesis presents a more
detailed background regarding urban influences on building energy consumption, and
particularly the assessment of shadow effects on building energy consumption within
urban areas.
10
1.3. Research strategy and outlines
For achieving the main goal of the research – that is, the synthesis performed in the final
stage – analysis at three different scales is performed using different methodologies and
sets of tools, as presented schematically in Figure 3.
In the first stage, presented in Chapters 2 and 3, two goals were set at the “per-unit”
scale:
1. Assessing the energy saving potential at the level of the individual building for
typical residential buildings in different regions of the country.
2. A sensitivity analysis of the elements and strategies of design that improve the
thermal performance of the building, and a quantitative assessment of design strategies
that could be used as feasible and worthwhile levers for improving energy consumption
in buildings.
The assessment of energy consumption in buildings and the sensitivity analysis were
performed using thermal simulation software. The ENERGYui tool, which was
developed in Israel by Yezioro et al. (2010), was used as an interface to the EnergyPlus
Figure 3: Simplified scheme of the research outline incorporating three stages in different scales for achieving a national–scale prediction of energy savings in buildings in the fourth stage.
Stage 4
Stage 2 Stage 3 Stage 1
11
software engine to calculate the thermal loads and energy investments needed in order
to heat or cool a building.
In the second stage, presented in Chapter 3, the influence of shadows cast by adjacent
buildings in an urban environment on acclimatization loads was assessed. The
assessment was based on advanced and innovative GIS analysis. The analysis was
performed on an urban case-study area with mid-rise compact buildings, creating a
dense urban fabric. The results of the analysis were used as a refinement factor in the
national-scale analysis.
In the third stage, presented in Chapter 4, detailed data from the ICBS were analyzed
regarding construction rates and the height of buildings built every year in every
settlement in Israel between 1995 and 2012. These data were analyzed spatially in order
to geographically ascribe the data of building rates, location of the apartment height
within the building, and the number of residents in each settlement to the four climate
regions in the country, using GIS mapping and analysis.
In the fourth stage, presented in Chapter 4, a synthesis of results from earlier stages is
performed with a model created to assess and predict the potential savings in
consumption of electricity for acclimatization in future building according to a climate-
conscious design standard. The rate of change in the level of the future annual savings
as calculated by the forecast model is mainly based on the rate of population growth, as
forecast by the ICBS. Based on that, three possible future scenarios of potential energy
savings relative to "business as usual" are proposed.
12
2. Assessment of energy savings potential through climate-
conscious design of buildings.
2.1. Assessment of energy saving potential at a “per unit” building
scale.
An assessment of the "energy gap" was made between a reference building which meets
the requirements of SI 1045 for thermal insulation and has other standard features, and a
thermally improved building which meets the highest demands of the Israeli standard
for energy rating of buildings – SI 5282. This comparison was made by conducting a
series of computer simulations of the thermal performance of buildings with different
characteristics, and the simulations were carried out for different climate zones that
express the range of Israeli climatic variability.
This process of assessing individual buildings at a “per unit” scale is described in the
schematic diagram below (Figure. 4). As a result of this process we derive the potential
for energy savings through climate-conscious design of a particular building, in
accordance with SI 5282. Building models and typologies which represent typical
residential building design in Israel were chosen. The attributes of the building models
were inserted as input in the thermal simulation software that was found to be suitable
for the research purposes. In addition to the simulations of general thermal performance
for different building types and climatic zones, a sensitivity analysis was conducted to
isolate and deepen our understanding of the influence of specific design parameters on
the thermal performance of the buildings. The sensitivity analysis simulations sets will
be detailed in the following chapter.
13
The modeling of building attributes for performing the simulations was conducted
according to the two goals that the simulation study intended to achieve. The first and
main goal was assessing, at the single building scale, the energy savings potential as a
result of the climatic zone in which the site is located and the relative height of the
apartment within the building, given minimal and maximal thermal performance.
Results of this basic analysis are described in this chapter, and results of the sensitivity
analysis are described in the following chapter.
Figure 4: Schematic description of the process conducted to simulate and assess individual building energy savings potential through climate-conscious design, meeting the requirements of SI 5282.
Schematic description of the process conducted to assess the
energy saving in single building scale
14
2.2. Simulation software – background
In order to model the savings in energy consumption, and perform a sensitivity analysis
to quantify the effects of particular elements and strategies of design on energy savings,
we used the ENERGYui2 interface, which facilitates the calculation of thermal loads on
buildings using the EnergyPlus thermal simulation software. This program, developed
by the U.S. Department of Energy, is considered to be an industry standard and the most
comprehensive modeling software that exists in this field (Crawly, 2008). It has been
extensively tested and is widely used in the context of thermal simulation of buildings
(DOE, 2010;).
Based on EnergyPlus as a computational platform, the ENERGYui interface allows the
user to calculate the thermal balance of a building as a function of its schematic design
and material composition, and the climatic features of the building site. The climatic
data used are representative of Israel's four different climatic zones, and the data files
that ENERGYui uses to describe these conditions are based on Typical Meteorological
Year (TMY) datasets extracted from meteorological stations of the Israeli
Meteorological Service (IMS). The climatic data include hourly air temperature, relative
humidity, global and diffuse radiation, cloud coverage and wind speed.
After simulating the thermal balance, ENERGYui can provide a summary of the annual
energy demand in kWh per square meter of building, required in order to maintain a
desired design temperature within the inhabited space of the residential building. The
thermal balance is set by a threshold temperature that is preset for summer and winter
seasons, based on ASHRAE standards. Other attributes, such as the shading percentage
over the building’s windows, are determined by a selection of fixed values.
The ENERGYui interface is considered, for the purpose of this study, to be an efficient
and useful tool for its goals, for two important reasons. One is that it includes the
2 http://www.technion.ac.il/~cela/index_files/ENERGYui_Install.htm
15
climatic information for each of the climate zones in Israel as referred to by the
compulsory building insulation standard SI 1045. (A mapping of the climate zones
referred to in the SI 1045 guidelines is shown in Figure 5.) Therefore, one can calculate
the energy consumption for acclimatization for every building site by using the thermal
simulation according to a specified climate zone. A second reason is the adaptation of
the building techniques and materials included in the software to typical conditions in
the Israeli construction industry. Among other things, the library of building materials is
adapted to the mandatory requirements of SI 1045, as well as to the recommended
requirements of SI 5282.
Figure 5: Climate zones in Israel as classified for planning and design according to the compulsory standard SI 1045 (Pearlmutter et al, 2010).
Climate zone
classification in Israel
according to SI 1045
Zone A – Coastal strip.
Zone B – Inner hills strip.
Zone C - Mountainous area.
Zone D - Inner valley strip.
16
2.3 Modeling Characteristics in the Simulation Procedure.
2.3.1. Building Model Characteristics.
First a design was chosen for a typical building in Israel that would be used as a
prototype model (A typical floor plan design can be seen in Figure 6). Three types of
buildings were chosen to represent the distribution of residential construction in Israel: a
single-story detached house, a 4-story apartment building and an 8-story building. The
single-story detached house has a floor area of 220 square meters, while each floor of
the other buildings consists of four apartments; each apartment area is 110 m2 in area.
The height of each floor in the simulation was set as 3 meters. Each type of building
model was examined in each of the climatic regions with the unique material
composition meeting the compulsory standard (SI 1045) and improved (SI 5282)
requirements. The design fitted as well to optimize the thermal performance of the
buildings in accordance with the mentioned above specific design features. An example
of different external wall materials used for the building models sited in Tel-Aviv,
which represents zone A, can be seen in Figure 7.
In total this series of simulations included 12 possible design options for the reference
building, and 12 for the improved building.
Figure 6: Typical floor plan of an apartment building used for the simulations: each of the four apartments has a floor area of 110 m² and a 3 meter floor-ceiling height. The stair/elevator core is shown in the middle of the floor.
17
2.3.2. Simulation software characteristics.
The simulations provide an output that represents the energy required to cool or heat a
space in a residential building to maintain a desired temperature predetermined in a
design procedure (the energy required for acclimatization is received in units of kWh
per square meter per year). The threshold values from which the acclimatization loads
are derived were set to 24°C for the summer months and 20°C for winter. The model
was used both for the simulations that examined the potential savings by different types
of buildings, and for sensitivity analysis of design strategies.
The design of the building in the simulation interface is flexible, and allows for
modeling of more than one floor. An apartment is considered the smallest independent
calculation unit and the thermal calculations are performed on a space defined only as
an "apartment”. The building materials are chosen according to the Israeli standards and
it is possible also to choose design elements with a pre-determined composition of
materials. The full list of materials used for modeling and simulation purposes is
detailed in the appendix.
A specific example of the composition of wall building materials is seen in Figure 7.
The thermal performance is calculated according to the design of the building, the
building materials, the geometry of the sun, and the climatic conditions (that can be
changed), along with predetermined loads (a permanent load of 1 watt per square meter,
Figure 7: External wall sections representing reference building (left) and improved building (right) as defined in the simulation for Tel-Aviv.
SI 1045 SI 5282
18
and a temporary load of 8 watt per square meter during the day). The efficiency of the
air conditioning system was set as COP=3 (for heating and cooling).
The calculations are performed on an hourly basis according to the data of the climatic
zone in which the simulation is performed. The results are totaled in a final output as the
annual energy amount in kWh needed to heat\cool the space (the energy consumption
for cooling and heating are presented separately in the GUI results report). The interface
is set to perform as a default comparing between the building for which the results of
the simulation were received and a reference building with the same area and height, but
with basic thermal functioning as specified in detail in SI 5281.1. As a result of the
comparison, an output is generated with the expected savings percentage in the planned
building in comparison to the reference building.
2.4. Characteristics of the reference building in the research.
The residential building used as a reference for the simulation conformed to the
obligatory thermal requirements of the Israeli standard SI 1045, Part 1 – "Thermal
Insulation of Buildings: Residential Buildings" (2003). The standard (as can be seen in
Table 1 for exterior walls of medium mass) specifies for each climatic zone the specific
compulsory requirement for the thermal performance of the envelope in terms of overall
thermal resistance and conductivity values. The overall thermal resistance (R) values
relate to the physical properties of the combined envelope material layers, and their
ability to resist the flow of heat. The overall thermal conductivity (U) values are the
reciprocal of the overall thermal resistance values, (R = 1/U) and relate to the physical
properties of the layers that enable thermal conduction of heat (in W/m²C˚).
19
Climate zones Location of Meteorological Station R
(C˚m²/W)
U
(W/m²C˚)
Zone A Beit-Dagan 0.77 1.3
Zone B Beer-Sheva 0.87 1.54
Zone C Jerusalem 0.97 1.03
Zone D Eilat 1.07 0.97
The higher the R value, the higher the insulation properties required by SI 1045 to be
implemented in the building. In addition to the values presented in Table 1, other
significant design parameters of the reference building are detailed in Table 2. The
window size and material were determined specifically for each climatic zone. The air
infiltration is low relative to the typical situation in actual buildings, but technically
could not be modified in the simulation interface.
Design parameter Simulation reference building
Characteristics
Orientation of windows Equal window area in all directions
Wall color (albedo) Medium (0.65)
Roof color (albedo) Medium (0.65)
Glazing Single glazed, (U-value =5.44,
SHGC = 0.7)
Night Ventilation Minimal
Air infiltration 1 ACH
Ratio of Window Area To Floor Area (%)
Zone A ~20%
Zone B ~25%
Zone C ~30%
Zone D ~15%
The glazing properties were defined as typical for existing buildings in Israel. The
window orientation was set to be equal in all directions by dividing the total window
Table 1: Thermal characteristics of reference building according to climatic zone . The values are based on the minimal insulation values required in residential buildings by SI 1045 Part 1 (2011) for a non-absorbent exterior wall with a mass of over 300 kg per square meter.
Table 2: Features and values of design elements for reference building, in accordance with SI 1045 requirements.
20
area into four equal areas facing north, south, east and west. The design made use of the
ENERGYui library of building materials that are set as a default to meet SI 1045
requirements in the different climatic zones. Wall and roof external albedo were set to
0.65.
2.5. Modeling characteristics of energy efficient building according to
SI 5282.
For comparison with the reference building, improvements were made in the simulation
design parameters to fit the recommended characteristics at the highest rating in the
Israeli standard for energy savings in buildings, SI 5282. The improved design elements
are summarized and detailed in Table 3.
2.6. Thermal simulations methods.
Climate zones
Design parameters Detailed
Description D C B A
Thermal mass Heavy building with semi-heavy walls
Average thermal conductance (U-value)
(C˚m²/W)
External walls <0.4 <0.4 <0.5 <0.5
Roof <0.3 <0.3 <0.3 <0.45
Fenestration <3.57 <3.57 <3.57 <3.57
Total thermal resistance (R) (W/m²C˚)
External walls 2.5 2.5 2 2
Roof 3.3 3.3 3.3 2.2
Window area to floor area ratio (%)
(With seasonal shading)
South oriented < %10 < %20 < %15 < %15
North oriented < %6 < %5 < %5 < %5
East oriented < %2 < %3 < %3 < %3
West oriented < %2 < %3 < %3 < %3
Total %8 < %10 %8 < %20 %8 < %15 %8 < %15
Albedo 0.65 0.65 0.65 0.65
Air infiltration (Air changes per hour)
<1 <1 <1 <1
Compactness (Envelope area to floor area
ratio)
Internal Apt. <0.5 <0.5 <0.5 <0.5
Corner Apt. <0.75 <0.75 <0.75 <0.75
Single house <1.2 <1.2 <1.2 <1.2
Mechanical ventilation (Air changes
per hour) 40 10 20 30
Fenestration type (classified by aspect)
North\South DG low-e DG low-e DG low-e DG low-e
East\west DG DG DG DG
Orientation South South South South
Table 3: Summary of recommended building design features for thermally improved building (based on Shaviv et al. 2002: “….design guidelines for residential buildings”)
21
2.6.1. Assessment of the energy consumption gap between reference and
thermally improved buildings.
The improvements in the design elements that were applied for comparison with the
reference design are detailed in Table 4. The improved building design models were
oriented with their main window openings towards the south, with the window area
determined according to Table 3. It may be seen that the east-west oriented windows
were set with the lowest window-to-floor-area ratio, while the south-facing windows
had the highest ratio. The glazing materials for the improved building were chosen to fit
the SI 5282 recommendations as emphasized in Table 3.
For the simulation purposes the reference building model was designed to meet the
minimal standards of SI 1045. It was compared in the simulation to a building with the
same geometry with a design and building materials that meet the optimal efficiency
possible with standard 5282 and the design and material solutions that are relevant for
the Israeli construction conditions.
Zone A Zone B Zone C Zone D
1045 5282 1045 5282 1045 5282 1045 5282 Design
parameters
0 00 0 00 0 03 0 00
Wall
insulation (mm)
33 33 33 33 33 00 33 00
Roof
insulation (mm)
00% 00% 00% 00% 00% 01% 03% 00%
Window-to-
floor-area ratio (%)
None Seasonal None Seasonal None Seasonal None Seasonal Shading devices
minimal Optimal minimal Optimal minimal Optimal minimal Optimal Night
ventilation
Med. Light Med. Light Med. Light Med. Light Wall/roof color
Table 4: Modeling design attributes: comparison between reference and thermally improved buildings, as simulated.
22
2.7. Simulation results.
2.7.1 Effect of climate zone and apartment location on energy consumption.
As expected, the results of the simulation showed only minor differences in electricity
consumption in apartments that are located on different floors of the building – except
for a significant increase in those on the top floor, due to their exposed roof, and a slight
reduction in those on the ground floor due to their reduced exposure (Figure 8).
A significant factor influencing the energy consumption of all the buildings is the
climatic zone (see Figure 9). Zone D, represented by Eilat in the simulations with the
most extreme climate (hyper arid conditions), shows higher electricity loads for
acclimatization.
Figure 8: Electricity consumption as function of the floor height (F1 to F8) within a typical 8-story building in Tel-Aviv (reference design).
Figure 9: Simulated electricity consumption as function of climate zone, in a typical single-story house (reference design).
23
2.7.2. Simulation results – assessing the saving potential in buildings
according to climatic zones.
The results of the simulation show both the total consumption of electricity for
acclimatization and the distribution of energy use for heating and cooling of the
building. The results received are in units of energy per unit area (kWh/m2) consumed
in a year. In order to calculate the energy consumption for the acclimatization of an
entire building, this value was multiplied by the number of square meters in the
building, not including the common space (such as the stairwell) in order to derive the
acclimatization results for each building.
This chapter presents different kinds of analysis based on the simulation:
1- The annual energy consumption for heating and cooling in each building type, in
each of the climate zones, in a thermally improved building according to SI 5282. This
analysis shows the internal distribution between heating and cooling loads.
2- Total potential energy savings according to building type and climatic zone. The
potential saving is the calculated difference between the energy consumption in the
reference building and the improved building, for each of the building types and
climatic zones.
3- Energy consumption comparison between reference building and thermally improved
building by building type and climate zone.
24
2.7.3. Results – distribution of electricity consumption for heating and
cooling in improved buildings according to climatic regions.
A.
B.
C.
01000200030004000500060007000
Tel Aviv (A) Beer-Sheva (B) Jerusalem ( c) Eilat (D)
Heat consumption
cooling consumption
Annual energy consumption for heating and cooling (single-story house)
(Kw
h)
Figure 10: Average annual energy consumption in a single-story house (A), a 4-story building (B), and an 8-story building (C), divided into cooling and heating in a thermally improved design (SI 5282) by climatic region.
0
20000
40000
60000
80000
100000
Tel Aviv (A) Beer-Sheva (B) Jerusalem ( c) Eilat (D)
Heat consumption
cooling consumption
(Kw
h)
Annual energy consumption for heating and cooling (8 story building)
0
10000
20000
30000
40000
50000
Tel Aviv (A) Beer-Sheva (B) Jerusalem ( c) Eilat (D)
Heat consumption
cooling consumption
Annual energy consumption for heating and cooling (4 story building)
(Kw
h)
25
2.7.4. Potential electricity saving according to building types and climatic
zones.
A.
B.
C. Figure 11: Simulation results showing the potential of electricity saving that can be achieved through a thermally improved building design following SI 5282 requirements, in a single-story house (A), a 4-story building (B), and an 8-story building (C). Results are by climatic zones in Israel.
3041
4,004
2,780
3,435
0
1000
2000
3000
4000
5000
Tel Aviv (A) Beer-Sheva (B) Jerusalem ( c) Eilat (D)
Acclimatization electricity consumption savings (single-story building)
(K
wh
) El
ect
rici
ty s
avin
g
24,458
29,414
20,924
31,055
0
10000
20000
30000
40000
Tel Aviv (A) Beer-Sheva (B) Jerusalem ( c) Eilat (D)
Acclimatization electricity consumption savings (4-story building)
Ele
ctri
city
sav
ing
(K
Wh
)
48,537
60,171
43,839
63,822
0
20000
40000
60000
80000
Tel Aviv (A) Beer-Sheva (B) Jerusalem ( c) Eilat (D)
Acclimatization electricity consumption savings (8-story building)
Ele
ctri
city
sav
ing
(K
Wh
)
26
2.7.5. A Reference building versus an improved building - comparison
between annual data of consumption for acclimatization according to Israeli
different climatic zones.
Figure 12: Simulation results comparing total energy consumption for acclimatization between thermally improved buildings (SI 5282), and reference buildings in a single-story house (A),a 4-storey building (B), and an 8-storey building (C) by climate zones in Israel.
0100020003000400050006000700080009000
10000
Tel Aviv (A) Beer-Sheva (B) Jerusalem ( c) Eilat (D)
Reference - 1045
Improved 5282
Electricity consumption for acclimatization: Reference vs. improved design (single-story house)
Elec
tric
ity
con
sum
pti
on
(K
Wh
)
0
10000
20000
30000
40000
50000
60000
70000
80000
Tel Aviv (A) Beer-Sheva (B) Jerusalem ( c) Eilat (D)
Reference - 1045
Improved - 5282
Elec
tric
ity
con
sum
pti
on
(K
Wh
)
Electricity consumption for acclimatization: Reference vs. improved design (4-story building)
020000400006000080000
100000120000140000160000180000
Tel Aviv (A) Beer-Sheva (B) Jerusalem ( c) Eilat (D)
Reference - 1045
Improved- 5282
Elec
tric
ity
con
sum
pti
on
(K
Wh
)
Electricity consumption for acclimatization: Reference vs. improved design (8-story building)
A.
B.
C.
27
2.8. Summary of simulation results for assessing energy savings at the
level of the single building.
The results of the simulations for the different types of climates and buildings show that
in total, there is a potential for saving in energy consumption for acclimatization that
ranges from 36% for a single house in Eilat to 56% for an 8-storey building in Tel Aviv.
In Eilat, the relative saving is lower than in the other regions, but the actual saving (in
kWh) is greater.
One can notice that the higher the building, the greater the proportional saving. This is
because the relative portion of apartments that are on intermediate floors is larger than
in low-rise buildings. The less exposed the apartment is to outdoor conditions, the less
energy it consumes.
Building Type
Zone A –
Tel Aviv
(kWh)
Zone B
Beer Sheva
(kWh)
Zone C –
Jerusalem -
(kWh)
Zone D –
Eilat
(kWh)
single-story
house 3,041 4,004 2,780 3,435
4-story
building 24,458 29,414 20,924 31,055
8-story
building 48,537 60,171 43,839 63,822
Building Type Zone A –
Tel Aviv
Zone B –
Beer Sheva
Zone C –
Jerusalem
Zone D –
Eilat
Single House 52% 55% 48% 36%
4-storey
building 56% 54% 50% 41%
8-storey
building 56% 56% 53% 42%
Table 5: Summary of simulation results – energy savings for acclimatization in thermally improved buildings, by building type and climatic zone.
Table 6: Summary of simulation results – percentage energy savings for acclimatization in thermally improved buildings, by building type and climatic zone.
28
3. Saving in electricity consumption according to design elements.
In addition to the overall energy savings enabled by an energetically efficient building
design, the study examined the relative effect of the different design elements of the
building in this context. This examination was performed through a sensitivity analysis
directed by "expert knowledge" in the field, for focusing the analysis on the main
building design elements. This second series of simulations examined in detail the effect
of each design element separately on the energy savings potential. This chapter
examines the most efficient design strategies, some of which consist of more than one
design element, and presents potential levers for reducing energy consumption and
greenhouse gas emissions – which can be useful for energy-related policy
recommendations.
3.1 Methods for Estimating Energetic Streamlining of a Building by
Modeling the Effect of the Design Elements.
In order to assess the potential for energy saving of different design elements, several
sets of thermal simulations were performed so as to estimate the consumption of
electricity for cooling and heating a typical residential building. Using the simulations,
we compared the reference building (which conforms to the requirements of SI 1045) to
a building in which various design elements were changed according to design
strategies that lead to an improvement in energy consumption. The sensitivity analysis
through modeling was performed specifically for each separate design element (for
example, external shading of a window) and also through a few parameters that are part
of a design strategy (for example, overall treatment and design of the window).
The building used as a prototype for modeling the sensitivity analysis is the typical
residential building whose plan was presented in Figure 2, but at this stage the analysis
is of a 3-storey building. We performed simulations for the three apartment locations –
29
ground floor, middle floor, and upper floor – in order to reflect the different conditions
of the apartment's ground and roof exposure. After calculating the loads for the
acclimatization of each apartment we performed an averaging to estimate the energy
consumption of a typical apartment made up of these characteristics. The thermal
characteristics of the typical reference apartment are the same as those detailed in the
previous chapter.
3.2 The Results of the sensitivity analysis simulations.
In Figure 13 we can see the results from the simulations that present the overall
potential for energy savings in a typical residential building through improved design
elements. The results are given as percentage savings relative to the reference building
with standard thermal performance in each of the four climatic zones (Zones A - D),
which are represented by the climatic data taken, respectively, from the meteorological
stations Beit Dagan, Beer Sheva, Jerusalem, and Eilat.
For each climatic region we compared two kinds of results: one result represents the
potential saving in energy for acclimatization by combining all the design parameters
applicable for new residential building, the second is result is the potential saving from
a partial set of improved parameters that are considered feasible for the renovation of
existing buildings (this can be seen in Table 8 below).
We can see in Fig. 13 that the saving in new buildings after the improvements is
significantly greater than the saving that can be reached by renovating an existing
building, and ranges between 40% and 55% depending on geographic location. The
savings reached through the limited set of parameters applicable for existing buildings
(suitable for renovation) is modest but substantial, ranging between 17% and 27%.
30
The parameters for which estimation simulations were performed classifieds into four
categories expressing four different design fields that may contain more than one design
parameter, as detailed in Table 7.
Classification of Design
Parameters
Number of
Parameters
Description of
Parameters
Windows 4
Orientation (enlarging the area of the
southern window), Ratio of window/floor
area, Type of glazing, Shading
Ventilation 1 Natural ventilation during the night
Insulation (Wall/Roof) 2 Insulation characteristics of walls and
roofs
Color (Wall/Roof) 2 Albedo of external walls and roof
Table 7: Classification of design parameters into four fields of building design interest. For each field the relevant design parameters are ascribed for the simulation analysis purposes.
Figure 13: Simulation results showing overall potential energy savings. The results are based on the full set of improvements applied to new building, compared with a more limited set of improvements that are applicable in the renovation of existing buildings (specified next page).
55% 54% 49%
40%
23% 27%
17% 19%
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Zone A Zone B Zone C Zone D
New construction
Existing (retrofit)
Total energy savings potential in thermally improved buildings
Climate zones
31
In order to isolate the effect of each parameter within each category, we performed
sensitivity analysis simulations, both of single parameters and a combination of
parameters in each field and for each of the climatic regions. The individual effects and
division of categories containing more than one parameter are displayed, along with the
cumulative effect of parameters suitable for retrofit, in Table 8.
Figures 14-17 show the energy saving potential in each field of improvement compared
to the reference building. We can see that for new buildings the "windows" category
provides the highest level of energy saving, which ranges between 25% and 40% when
all the parameters in the category are improved. Within the category of window
improvement, the greatest potential lies in the parameter of changing the area of the
windows compared with the floor area, with the largest window area oriented south and
the windows in other directions smaller. One must emphasize that this design strategy is
suitable for application in the design of new buildings, but less suitable for the
renovation of existing buildings. The use of improved windows (glazing) and the
Particular influence of design parameter component on energy
savings (Kwh) Zone A Zone B Zone C Zone D
Wall insulation 1.0 2.0 1.3 0.0
Roof insulation 0.0 0.3 0.0 0.1
Window area and orientation 3.3 0.0 10.0
3.0
* Windows: double glazed 0.0 0.1 4.0 6.0
* Windows: Low-E fenestration 0.0 0.0 5.0 0
* External shading - Optimal 1.6 0.1 0.1 0.0
Natural ventilation 0.3 0.1 1 1.0
* Wall hue: Bright 0.1 0.2 1.0 1.0
* Roof hue: Bright 0.0 0.2 0.0 0.0
Retrofit potential – combined from the design parameters suitable for
retrofitting (marked with an Asterisk)
2.7 8.4 01.. ...
Table 8: Isolated effects of design elements on energy savings (in kWh), both the specific effect of each element and the Overall combined Effects of Elements that Can Be Applied in a Renovation (Marked with an Asterisk*).
32
shading of windows also contribute to energy savings, and these changes can often be
applied in a practical way in existing buildings.
The relative contribution of other means of improvement, like improved insulation,
ventilation, and finishing material, depend mostly on the local climate. The results of
the simulation set show that an initial design of a new building, which combines proper
building geometry features, can enable a significant saving in a residential building.
33
3.2.1. Recognition of relative saving potential by design parameters.
0.0%
5.0%
10.0%
15.0%
20.0%
25.0%
30.0%
35.0%
40.0%
Windows Ventilation Insulation Color
Roof color
Wall color
Roof insulation
Wall insulation
Night ventilation
Window shading
WindowFenestrationWindow area
: Energy savings by design parameterA Zone
0.0%
5.0%
10.0%
15.0%
20.0%
25.0%
30.0%
35.0%
40.0%
45.0%
Windows Ventilation Insulation Color
Roof color
Wall color
Roof insulation
Wall insulation
NightventilationWindowshading WindowFenestrationWindow area
: Energy savings by design parameterB Zone
Figure 14: Relative energy savings achieved through the improvement of different design parameters, compared to the reference building in Tel-Aviv (Zone A).
Figure 15: Relative energy savings achieved through the improvement of different design parameters, compared to the reference building in Beer-Sheva (Zone B).
34
0.0%
5.0%
10.0%
15.0%
20.0%
25.0%
30.0%
35.0%
40.0%
Windows Ventilation Insulation Color
Roof color
Wall color
Roof insulation
Wall insulation
Night ventilation
Window shading
WindowFenestrationWindow area
: Energy savings by design parameterC Zone
0.0%
5.0%
10.0%
15.0%
20.0%
25.0%
30.0%
35.0%
40.0%
Windows Ventilation Insulation Color
Roof color
Wall color
Roof insulation
Wall insulation
Night ventilation
Window shading
WindowFenestrationWindow area
: Energy savings by design parameterD Zone
Figure 17: Relative energy savings achieved through the improvement of different design parameters, compared to the reference building in Eilat (Zone D).
Figure 16: Relative energy savings achieved through the improvement of different design parameters, compared to the reference building in Jerusalem (Zone C).
35
As reflected in the results, the relative contribution of design strategies (or "levers") for
potential energy savings changes significantly between climatic regions in Israel,
despite the relatively small area of the country.
In these zones we can see, according to the results of the parametric sensitivity analysis,
that the highest percentage of saving comes from windows, and this applies for all the
climatic zones. We can notice two significant design parameters: one is the natural
ventilation in the summer nights the second and the most influential is the window
related design elements. This should be emphasized since, from a design perspective, it
is associated with window design. This part of the research results indicates that
window-related design parameters, with all its aspects, are a central lever to be
encouraged in order to increase the energy saving in buildings. This field is particularly
prominent in zone B.
In order to put the relative importance of these levers on a national scale perspective, it
is important to take into account the actual volume of building in the different climatic
zones. Based on the GIS analysis presented in detail in Chapter 5 (Table 9), we can see
that around 85% of the residential building every year is built in climate regions A and
B (60% of the total construction completion is situated in zone B). In these zones the
potential percentage of energy saving is also the greatest according to the simulation
results.
36
4. Urban influence on buildings consumption.
4.1. Background
The previous chapter presented energy consumption assessment at a “stand-alone”
building scale in different climatic zones in Israel. However, a building is usually not an
isolated entity: it interacts with the surrounding environment, whether with the natural
or man-made environment. Energy consumption in buildings is a function of the
building’s surrounding climate, and since the climate in an urban area is different than
the surrounding area (Landsberg,1981, Oke, 1981, Santamouris, 2001), a high level of
academic interest has been dedicated to the study of the city and the climatic conditions
within it (Grimond et al 2010). This interest increased when the need for sustainable
urban development came to the public's awareness in the early 70’s, following the oil
crisis (Golov & Eto, 1996; Newman, 1996). Nowadays most buildings are situated in
cities and in the urban built environment, encompassing more than 50% of the world's
population. The percentage of urban population is expected to grow further as the
urbanization rate increases (DESA, 2007). In Israel 85% of the population is located in
cites and urban settlements (ICBS, 2012). Therefore, attention has to be given to this
issue. Since this research deals with national-scale assessment of energy consumption in
buildings, the urban factor has to be brought to attention – and as such it offers some
innovative tools and insights regarding the influence of the urban fabric on the local
climate and on energy consumption in buildings.
Two main climate agents are influenced by buildings and other solid objects in the
urban fabric: solar radiation and air flow. Those environmental agents can be modified
by urban texture and density (Oke, 1981, Grimmond et al, 2010), and together with
anthropogenic heat from transportation, buildings and other human activity, contribute
to the urban heat island (Sailor, 2004), as detailed in Sec. 4.1.1. The relation of these
factors to the local climate in an urban area is very complex, and thus many quantitative
37
studies on urban climate modification have focused narrowly on thermal comfort and
energy consumption at small scales such as single buildings and urban canyon units
(Grimmond et al 2010; Pearlmutter, 2007; Compagnon, 2004). One important way of
characterizing the climatic influence of the urban fabric over larger scales is to examine
its complex effect on the exposure of individual buildings to solar irradiation (Erell,
2008; Yezioro et al., 2006). In fact, the lack of this type of quantitative research and
tools at a larger urban scale for climate-conscious urban planning has been cited as one
reason for the absence of climatic consideration in urban planning and design practice
(Eliasson, 2000).
This chapter presents a quantitative method of assessing and analyzing the shadows
projected on the walls of buildings in an urban environment, integrated over a large
urban scale. This assessment method is based on an innovative, fully automated
parametric GIS model that calculates and generates a GIS layer of predicted shadow
footprints projected by buildings in an urban environment according to their geometrical
attributes. The Shadow Footprint Model (SFM) is designed to process any configuration
of solar geometry angles, and based on the GIS building attribute database, provide the
shadow geometry features as an output. By using the shadow analysis to quantify the
average overshadowing of walls and windows in adjacent buildings, this approach
allows for refinement of the energy consumption assessment at the single-building
scale.
It should be noted that this analysis is limited only to the obstruction of direct radiation
by buildings, and does not consider indirect radiation or the effects of vegetation and
other urban elements that in some cases can be a significant source of overshadowing in
addition to the buildings themselves.
38
4.1.1. Microclimate in Israel.
A well-known and documented phenomenon demonstrating one of the possible effects
an urban environment has on the local climate is the urban heat island (UHI) (Oke,
1972). The UHI is defined as a relative high temperature within a city compared to the
rural neighboring area. The UHI is measured in many cities especially in nocturnal
hours and\or in stable air conditions (Hassid et al., 2000). In Israel the UHI has been
measured and assessed to some extent in cities located in different climatic zones,
including Tel-Aviv (Saaroni et al., 2000), Beer-Sheva (Saaroni, 2010) and Eilat (Sofer
& Potchter, 2006). The results in those studies showed a maximum UHI intensity that
varied between 1.0˚C and 5˚C, with Tel-Aviv showing the highest UHI values. The UHI
was observed especially at night time and in early morning hours, and showed a
negative UHI (i.e. a "cool island") during daytime hours. At the same time, the
quantitative assessment of urban microclimate has not yet developed to the extent that it
can provide a detailed understanding of how temperature is affected temporally and
spatially (i.e. at specific locations in the city, at specific times). Thus it is difficult to
assess the energy consumption of buildings in an urban environment given the extra
influence a city has on its climate. This chapter will present a tool for enhancing the
capability to do this, by modeling in detail regarding one of the climate agents whose
influence on building energy consumption is especially pronounced: the availability of
direct sun within the urban canopy, as a function of the obstruction by urban buildings.
4.1.2 Assessment of shadow influence in the urban context – scientific
background
Solar radiation is a key determinant of microclimate in a city (Pearlmutter et al., 2010).
Buildings, which create the texture of the urban surface, also are responsible for
daylight availability and obstruction of light in the urban canopy (Compagnon, 2004).
The effects of shadow on the architectural and urban development are well
acknowledged (Olgyay & Olgayay, 1977; Shaviv & Yezioro, 1997). At the single
39
building scale, the design of a building has to take into consideration the exposure to
sunlight in the planning and design phase in order to plan the envelope shape, direction,
materials and window openings for enabling optimal exposure to sun light and radiation
(Urbikain, 2009; Gasparella, 2011). Studies have examined the beneficial values of
selective solar exposure in streets (Knowles, 1981) and buildings (Gupta, 1984) in an
urban context, and provided insights regarding appropriate street orientation to optimize
solar exposure according to local climatic conditions. From the perspective of building
energy efficiency, Gupta (1984) stated that east-west and north-south grids are the most
suitable, though according to Littlefair (1998) the reality is more complex.
The development of computational power and software has expanded the possibilities
for the calculation, visualization and analysis of shadow projection. In the last 15 years,
major advances have been made in the quantitative aspects of calculating the three-
dimensional geometry of mutual shadowing between large numbers of complex objects.
For single buildings, CAD programs such as the industry standard software AutoCAD
(Autodesk Inc., Sausalito, CA, USA), that is commonly used by design practitioners,
can configure the geometrical attributes of a shadow volume created by single buildings
for visualization purposes, given the geographical location and time.
Quantitative studies regarding the penetration of direct solar radiation at larger scales
than a single building in an urban environment are less common. Some notable steps
towards simulating shadows and radiation availability in the urban canopy include tools
such as "Shading" (Yezioro & Shaviv, 1994), in which a model for calculating shadows
of a group of buildings for visualization purposes other methods has been developed by
using image processing techniques of urban Digital Surface Model (DSM) data. Richens
(1997) assessed shadow cast by buildings, Ratti and Richens (2004) demonstrated an
ability to predict the solar radiation values on a horizontal plane in an urban area and
Morelo (2008) presented a method for modeling solar envelopes (shadow volumes), 2D
40
shadow footprints, solar rights and access to solar collectors. These studies, although
significant, are limited to visualization purposes and lack the ability to automatically
analyze geometrical shadows as discrete entities that are topologically coincident with
built surfaces. The image processing methods are limited to a single temporal
perspective, but on the other hand they allow the advantage of analyzing irradiation
intensity on spatially continuous surfaces of buildings, as demonstrated by Reinhart
(2013).
The accuracy of the calculations greatly depends on the spatial resolution of the DSM
and can vary greatly based on the resolution of input pixels.
As of today, development in the field of assessing shadow influence in the urban
context is being carried out by the GIS software developers for commercial purposes.
For example, the 3D Analyst extension, developed within the ArcGIS software package
(ESRI, Redlands, CA, USA), consists of tools which enable the generation of a vector-
based layer for creating 3D shadow entities (shadow envelopes) and 2D shadow
footprints (shadow maps).
This software extension is costly in terms of the resources, computer power and
professional knowledge required to operate it. To create the 3D shadow entities,
extensive manual work is required to first build sophisticated 3D urban models. This
makes the tools more suitable for the analysis of a small group of buildings than for
large scales such as the urban scale. In addition, these tools require extensive computer
power for representing 3D intersections of 3D envelopes for recognizing shadow areas
on building walls, especially for a large urban complex.
41
4.2. Methods – urban shadow geometry assessment
4.2.1. Shadow footprint model development – guiding considerations
Since understanding the nature of solar exposure and obstruction in a large urban
complex is based on variability in spatial and temporal attributes (Knowles, 2003), GIS
can serve as an adequate system for achieving the research objectives. The developed
model was built using the GIS software ArcGIS 10.1 (ESRI, Redlands, CA, USA). The
developed model takes advantage of an existing GIS layer that represents the
geometrical attributes of the buildings. This layer, produced by the Survey of Israel
(SOI), is a product of well-developed remote sensing techniques for extracting building
outlines and heights. Elevation data is based on laser scanning (LiDAR) – a remote
sensing technology which results in clouds of points representing the elevation of the
surface and 3D objects (Maas, 1999; Sohn, 2007; Yong, 2013) - and on image
processing techniques, which together produce a Digital Surface Model (DSM). Based
on these technologies, which are continuously being improved and refined, the database
of the urban buildings is automatically and manually updated using GIS by the SOI.
This database is widely used by security agencies, governmental offices, private
companies, architects, urban planners and academic researchers. The integration of the
buildings layer with the capabilities of GIS for spatial analysis provided the tools for
developing the Shadow Footprint Model (SFM) presented and demonstrated in the
current research, for the purpose of analyzing a large urban complex with various sun
angles.
The model was built using the ArcGIS Model Builder (ESRI, Redlands, CA, USA),
which is a programming GUI for building geoprocessing workflows and customized
tools. To develop a highly applicative tool a few guiding principles were adopted that
imposed some limiting factors on the model's development, yet allowed for sufficient
computational power to run the model. These were: simplicity in terms of input
42
requirements, simplicity in model operation and the option to adapt the model to various
architectural design and urban planning requirements. One of the implications was the
use of only the basic ArcGIS software license and the Spatial Analyst extension (ESRI,
Redlands, CA, USA), and to avoid any third party GIS software and expensive, less-
used extensions which require advanced knowledge or training to operate.
The main model output layers include the shadow geometry footprints of the shadows
cast by buildings. The model calculates the shadow footprint for a planar surface.
Therefore this model is applicable for cities with a relatively moderate slope of less than
4.5%. With a higher average slope, the accuracy of the model drops under the accepted
statistical threshold (95% significance). The SFM input is simple in terms of database
inputs and does not require sophisticated 3D visualization or analysis tools. In addition,
using the GIS model may prove beneficial for creating a common language between
climatologists and urban planning professionals given the wide and varying use of GIS
in many fields of study, research and professions.
4.2.2. Case study features
In order to achieve the research goals and to demonstrate the model's applicability, a
case study area was chosen for the analysis. The case study zone is situated in the city of
Tel-Aviv, Israel. The city is located at 32°4′N 34°47′E and is considered to be the
largest city in Israel, with about 400,000 residents. The climate of the city is
Mediterranean with hot humid summers and rainy winters. The average population
density is around 7,800 people per square kilometer. The Tel-Aviv urban texture is
versatile in terms of buildings and population densities. Its variability is well analyzed
and reflected in the division of statistical zones done by the ICBS (2011). The case
study area chosen to demonstrate the model's performance is square shaped with an area
of 1.3 x 1.3 km. The area consists of about 1,200 buildings. The area of the case study
zone is marked by the yellow rectangle in Figure 18. The area is located in the northern
43
part of Tel-Aviv and includes ten statistical zones: nine of these are residential with two
typical urban densities of mid-rise residential buildings and a few high-rise residential
buildings, and one statistical zone with commercial land use. Ibn Gvirol is the main
street running from north to south in the center of the map (main roads are symbolized
as brown lines in Figure 18).
Building area density types consist of a relatively dense area in the western part and a
less dense area in the eastern part, based on the statistical zones defined by the ICBS
(2011).
4.2.3. GIS database input –buildings geometry as a vector layer
The database that the model was based on is a buildings footprint vector layer with
building geometry. The buildings GIS layer was acquired from the SOI, and is part of
the Israeli national geographic database. The data accuracy, in terms of building
geometry and coverage percentage of recognized buildings, is being updated and
improved on a constant basis using remote sensing techniques and field surveys, such as
by automated building extraction from aerial images. The database consists of
alphanumeric data that describes, among other things, the base elevation and the
Figure 18: The case study area in Tel-Aviv. The case study consists mostly of residential buildings (about 1200), and totals 1.69 square kilometers.
44
building heights. The accuracy of the buildings depends, of course, on the accuracy of
buildings extracted from the aerial images. Inaccuracies in the database consist, for
example of shifting of the features or building details which are missing. The shifting of
buildings is a result of the angle at which the aerial image was taken, which might cause
the roof outline to be shifted in the image in comparison to the actual base outline of the
building. This problem can be easily corrected in GIS. Regarding small-scale resolution
building elements, for example porches exceeding the structural outline, which do not
appear in the layer, these depend on the spatial resolution, the quality of the image and
the techniques used for the 3D image processing and building extraction. Since one of
the objectives of the developed SFM is to be able to process and asses a large scale
urban complex that can be used for urban planning purposes, the building accuracy and
precision are considered an acceptable compromise–and the database with the average
geometrical attributes is considered satisfactory.
45
4.2.4. Solar geometry input
Shadows cast by buildings in the urban context are determined by the object features
and by the location relatively to the position of the sun at a given point in time. The
location of the sun relative to an object is a function of the yearly and daily solar cycle.
This determines the sun's azimuth angle (measured from the north clockwise on a
horizontal plane and ranging between 0 and 360 degrees) and the sun's altitude angle
(measured between the sun and a horizontal plane and ranging from 0 to 90 degrees).
For predicting the shadows cast by buildings, the solar azimuth and altitude angles are
required as input. The solar geometry relative to an object can be presented as a 2D
projection that reflects the possible sun position according to yearly and daily solar
cycles as shown in Figure 20. The sun's projection presented below indicates the
azimuth and altitude angles for any given time during the yearly cycle in Israel. The
Figure 19: The building layer representing the case study area. The dashed line represents Ibn Gvirol Street, which marks a discontinuity in the built area density. The image was acquired from SOI, based on a 2008 Ortho-photo. The layer contains the elevation and height data for each building (the buildings height is classified to five levels represented by different colors in the figure).
46
developed SFM uses the angles of summer and winter solstice, during which the
shadow coverage is in its minimum and maximum extent.
4.2.5. Automatic identification of building facets
An important design consideration in architecture is the orientation of the wall facets
and their glazed openings in relation to the sun. Orientation has major implications on
building design since exposure of the building walls to the sun has to be taken into
consideration when planning the window elements and shading devices of the building.
This step in the SFM processing is the most time consuming in terms of computational
power, accounting for about 80% of the total SFM processing time. Due to its
importance for possible future applications, this step was included in the model.
In order to determine the orientation of the walls, the building outline layer, represented
as polygons, was “exploded” (partitioned) into linear wall segments according to the
vertices of the building’s outline. The partition of the building into its facets was
determined by calculating the azimuth angle perpendicular to each wall segment and
Summer solstice
Sun path (June 21)
Winter solstice
Sun path (Dec. 21)
Figure 20: A 2D projection of the sun's path as reflected by the yearly and daily solar cycles at latitude 32˚ taken From: Guide for Bioclimatic Building in Israel (Pearlmutter et al, 2010)
47
classifying each wall according to four directions: North, East, South and West, with
each direction covering a 90˚ quarter as described in Figure 21. The topological
identification of the facets in a layer is a precondition for the following step of the
model, which is the calculation of the shadows cast by each wall segment. This layer is
one of the model’s intermediate outputs, and may prove to be applicable for future
studies and analyses.
Figure 21: A model output demonstration of the building facet classification according to the azimuth angle perpendicular to the wall segment. The classification rules are presented as possibilities A-D.
N
W E
S
D
B
C
A 45˚
135˚ 225˚
315˚
Facets classification rules A. North facing facet: 315˚ - 45˚
B. West facing facet: 45˚ - 135˚ C. South facing facet: 135˚ - 225˚ D. East facing facet: 225˚ - 315˚
48
4.2.6. Shadow footprint and geometry calculation in a 2D plane
The attributes of a shadow cast by a simple object at a certain time of year and at
specific geographical latitude are based on simple trigonometry. For an automated
calculation of the shadows cast by a group of buildings, with varying structural features
in multiple spatial locations in a coordinate oriented plane, a set of rules and
calculations is required to account for various scenarios. An automated model has to
encompass, along with the building’s features, all possible sun positions relative to the
objects which cast the shadows. The sun's position relative to the spatial location of the
building can be located, at a specific point in time, in one of four possible spatial
circular quadrants of an orthogonal sphere, as illustrated in Figure 22. For each quadrant
in which the sun can be located, in relation to the shadow-casting object, the projected
shadow vectors can be differentiated in terms of their direction and values. The
mathematical basis for calculating the projected shadow caused by the obstruction of a
discrete point is described in detail in Budin and Budin (1981). Two adaptations to this
method had to be considered when calculating the shadow: first is the direction and
combination of the , of the shadow, which for each quadrant is different (as
demonstrated in Figure 22) and second is the normalization of the azimuth angle values
for calculating the shadow vectors, which is required as each vector is influenced by
both the altitude (ALT) and the azimuth (AZ) angles, both in degrees.
Figure 22: Demonstration of shadow vectors projected on a 2D plane (azimuth set as zero in the north), as considered by the SFM calculations.
49
For calculating the value, the azimuth (AZ) normalization applied is [sin (AZ)] –
while for the calculation the normalization applied is [cos (AZ)]. This normalization
provides the ratio that will be multiplied by the maximum possible length of each
vector. The maximum length of each vector is based on the building’s height (H) and on
the altitude angle: [H / tan (ALT)]. For adjusting the vector's direction, multiplying by a
factor of [-1] inverts the value to match the correct direction of the shadow in each
quadrant. Multiplying the three components mentioned above gives us the shadow’s
and while the maximum of the shadow is the building height, as described in the
following equations:
The subsequent calculation of the shadow length LS is:
LS = √
The calculation of shadow geometry carried out by the model is based on the
intermediate output layer which includes the building’s facet classification as presented
in chapter 4.2.5. The shadow geometry was built by projecting each building facet
attributes on a horizontal plane that represents the ground level. The next step was to
project the shadow's geometry area based on the shadow vertices. As a basis the facet
geometry, which is defined by the four vertices enclosing each facet of the building, is
used. The model identifies the spatial attributes of all the facet vertices belonging to
each building. The highest two vertices in each facet are recognized – these represent
the roof of each building (A1 and A2 in Figure 23), and a vector-based projection of the
= [-H cos (AZ) / tan (ALT)]
= [-H sin (AZ) / tan (ALT)]
50
shadow vertices is calculated by following the process described previously. The output
is a GIS layer which includes the projected vertices. Those vertices delimit the projected
shadow area cast on the horizontal plane (Figure 23). Based on the calculated shadow
vertices, the actual geometry of the shadow is built by the model for each of the
building’s facets and exported as a polygon layer. The projection of the vertices on an
orthogonal coordinate plane is calculated by the SFM using the solar geometry input
and the geometrical attributes of the building vertices. In addition to calculating the
shadow area projected on the ground, the 3D features of the solar envelope delimiting
vertices (A1, A2, A3, A4, A1’ and A2’ in Figure 23) were calculated by the SFM and can
be exported as a single vector layer.
4.2.7. Application of the SFM on the case study area
Based on the sun's position, the building’s 3D geometry (width, length and height) and
the spatial location of the buildings on the 2D plane, the model calculates the
coordinates of the shadow footprint area as described in detail in the flowchart below
(Figure 24).
Figure 23: Schematic diagram demonstrating the principles of shadow geometry calculation used by the SFM to calculate the shadow footprint of a single building facet.
Figure 4. Principals of shadow geometry calculation used by the SFM to calculate the shadow footprint
180˚
51
The altitude and azimuth angle values for a geographical location can be easily obtained
from various open-source sites; the angle values can be adjusted for the local time based
on the time zone of the site. Given the specific position of the case study area at latitude
32˚N and for the time zone GMT+2, angle values were obtained from an open source
site (NREL) for 9:00, 12:00 and 15:00 on the dates of the winter and summer solstice
(December 21 and June 21). This configuration encompasses the seasonal maximum
and minimum shadow lengths and areas of influence, as well as daily maximum and
minimum shadow lengths in hours of interest where the solar irradiance is highest
during the day. The shadow effect during these hours is significant, both for open space
planning and for building design.
The model was tested for its performance in predicting shadows cast by buildings
arranged in a regular form, as well as for irregular street formation and orientation, and
for both simple and complex building geometry.
In addition to calculating the footprint of the solar geometry of the shadow, the 3D
height attributes of the vertices delimiting the shadow volume are also calculated in the
process. This includes the height of the minimum and maximum vertices of the
enclosing solar envelope. The maximum height of the solar envelope is identical to the
building’s height, while the minimum height of the solar envelope is actually the point
of intersection with the ground. In the model’s case the intersection height is set to zero
above ground (though it can be set also to a height above sea level).
52
The Shadow Footprint Model (SFM) processing steps from the input phase to the output
phase are summarized in the following flow chart (Figure 24):
Input: Solar geometry
information
Input: Solar geometry
information
Input: Database
Input: Database
Creating GIS layers
Creating GIS layers
Output2
Output2
Output1
Output1
Shadow footprint
coordinates
Shadow footprint
coordinates
Solar envelope Attributes
Solar envelope Attributes
Calculating the shadow
projected by each one of
the building’s facets
Calculating the shadow
projected by each one of
the building’s facades
Shadow footprint
area influencing
open space areas
Shadow footprint
area influencing
open space areas
Shadow footprint
influencing
neighboring buildings
Shadow footprint
influencing
neighboring buildings
Urban Buildings Vector
layer
Urban Buildings Vector
layer
Sun Altitude angle
Sun Azimuth angle
Sun Altitude angle
Sun Azimuth angle
Data cleaning and Preparing
spatially unified GIS layers
Data cleaning and Preparing
spatially unified GIS layers
Building the shadow
geometry layers for each
wall segment
Building the shadow
geometry layers for each
wall segment type
Building facet Extraction and
Classification according to
their orientation
Building facades Extraction
and Classification according to
their azimuth facing
Building’s facet recognition
Building’s facade identification
Populating 3D attributes of
Solar envelope vertices
Data post-processing
Data betterment
Figure 24: Schematic workflow of the SFM model, from input to output stage.
Figure 4. Principals of shadow geometry calculation used by the
SFM to calculate the shadow footprint
53
4.3. SFM results analysis
Application of the SFM on the case study area, for a regular set of buildings and for
different time configurations resulted in a GIS vector layer of shadow footprints cast by
buildings. The shadow layer outputs (Figure 25) were calculated by the model based on
the principles explained previously. Shadow footprint layers generated by the SFM are
represented for each building facet (classified by the orientation of the facet). The solar
geometry configuration is for December 21st at 15:00. Each one of the facets is
represented in a different color according to the facet’s classified orientation. The
shadow projected by each facet is represented by the polygons in four hues of grey, with
respect to the facet's orientation.
Figure 25: Demonstration of SFM results: shadow projection for each building facet (north, south, east and west oriented facets). The shadow projection corresponds to the solar geometry angles at 15:00 on December 21st in the case study area.
Figure 5. planer quadrents
54
As can be seen in Figure 26, the shadow geometry output layers are represented by
polygons consisting of the shadow projected area in open spaces and polygons of the
shadow that affects the buildings. The difference in shadow coverage in the same hours
between the winter season (Figs. 26c and 26d) and the summer season (Figs. 26a and
26b) can be clearly seen in the results. On June 21st the inclination angle of the sun is
the highest during the year, i.e. the shadow length is the shortest. This also means that
during the year this represents the smallest extent of shadowed area as predicted by the
SFM (whereas the inclination angle is the lowest and the shadow length the longest on
December 21st). The shadowed areas in the summer provide shelter from the strong
direct radiation. However, in winter shaded areas in the open spaces are less desirable
since the absence of direct solar radiation will lower thermal comfort. The other output
of the SFM shown by these results is the shadow footprint in the built area that is
marked in the figure by a light-blue color. The shadow footprint polygons overlap with
the footprints of buildings. This does not represent the precise area of shadowed
surfaces on the building envelope, but it provides an important quantitative perspective
of the magnitude of the area of influence which shadows have on buildings (as will be
explained later).
55
Another point which can be noticed in Figure 26 is that during the winter season the
shadows are more influential on buildings, while during the summer the shadow
footprint which affects the built areas is smaller. The next chapter will present a more
detailed analysis using this output layer, for estimating the influence of shadows on the
building walls.
a.
a.
b.
b.
c.
c.
d.
d. Figure 26: Shadow footprint polygons generated by the model according to four time configurations a – d specified in the left part of the figure. The brown line represents an east-west oriented street (Jabotinsky).
Figure 5. planer quadrents
SFM date and time
configuration: a. June 21, 15:00 b. June 21, 9:00 c. December 21, 15:00 d. December 21, 9:00
56
Processing time of the 1233 buildings within the case study using the fully automated
model was 17 minutes (The shadow output is demonstrated in Figure 27). The SFM
generates 29 output layers; two of them are the final shadow footprint polygons for open
areas and built areas, while the others are intermediate outputs, some of which could be
used for future 3D shadow analysis – such as the facet orientation layer and detailed
shadow analysis for each facet type (orientation type). For running the SFM, in terms of
computational power, results were obtained using a standard PC with 2 GB RAM and a
single processor (out of two).
Figure 27 presents the SFM of shadow results for noon on December 21st according to
the sun altitude and azimuth angles.
Figure 27: Shadow footprint polygons in the open areas of the case study, automatically generated by the model based on the buildings database layer and the solar angles input configuration set for December 21st. The red dashed line represents Ibn Gvirol street (North-South oriented), and the black dashed line represents Arlozorov Street (east-west oriented).
Figure 5. planer quadrents
57
The shadow covers a relatively large area, as can be visually seen and can easily be
calculated by GIS. The total shadowed area summed up to 0.6 square kilometers out of
a total area of 1.69 square kilometers (the case study area), while the total building roof
area summed up to 0.37 square kilometers. Therefore the shadow projection on the
open-space area accounts for almost 50% of the total open space area during noon on
December 21st. For a sun configuration on June 21
st at 12:00, it was expected that the
shadow area extent will be at its lowest during the year. Based on the simulation done
with the SFM it was found (through the GIS( that the shadow area in the open spaces
accounts for almost 10% of the total open space area. An interesting pattern can be
visually observed in the results presented regarding the relation between the shadows
and the street orientation. While shadows in the east-west oriented streets (denoted by
the dashed black line in Figure 27) cover a large percentage of the area of the streets, the
north-south streets (denoted by the red dashed line in Figure 27) are almost free of
shadows during the noon hour. Patterns of shadows projected on open space areas can
have direct implications on temperatures in open spaces and also an indirect implication
on the energy consumption in buildings. With future research, the SFM can provide
some helpful insights by identifying patterns of open space shadows during the year, on
an urban-scale, for supporting analysis related to temperatures and thermal comfort in
outdoor and indoor spaces.
58
4.4. Validation of the SFM.
A validation of the SFM results was done by using an aerial image of the case study.
The area of interest which was selected consists of an area with relatively low tree
cover, captured from a high incidence angle (to avoid the appearance of shaded walls in
the photo). Another consideration in selecting the aerial image was the spatial
resolution. A resolution higher than 1.0 m pixel size should be satisfactory for the
purpose of shadow recognition and comparison with the SFM output. An aerial image
which matches the area of the first set of buildings tested by the SFM was selected for
validation. The image was acquired from an SOI open-source internet site3, and was
taken at noon on March 29, 2012. The spatial resolution of the image is 0.6 x 0.6 m. The
small shadow lengths in the image and the buildings low density allows us to observe
the entire shadow projection on the ground. The image was classified into three
categories: shadowed area built area and vegetated area, using a supervised
classification method in the ArcGIS software (ESRI). The SFM simulated the shadow
polygons based on the time the image was taken. The SFM results were compared to the
classified pixels in the image. A sample of the comparison between the SFM results and
the image classification can be seen in Figure 28. In the sample, the building-layer data
is superimposed on the aerial image and on the classification results and appears as
blue-outlined polygons, which allow viewing of the bright-colored pixels of the
buildings underneath. The GIS shadow footprints in the open spaces, generated by the
SFM, are marked by the boundaries of yellow polygons. Pixels which intersect and are
within the shadow polygon boundaries generated by the SFM were analyzed and
counted. Predicted shadowed area by the SFM shows the highest similarity to the
shadows in the image where vegetation is absent. An example of that can be seen in
buildings number 37, 20, 40, 18 and 42 in Figure 28. The level of similarity reaches
3
SOI, Survey of Israel, retrieved from: http://www.govmap.gov.il/
59
85.9% of the shadow pixels out of the predicted shadowed area, which are satisfying
results. The main reasons for un-matching between the image and the SFM results are
due to inaccuracies in the building database, especially in the building outlines. An
example for these inaccuracies can be clearly seen in buildings 18 and 20, where the
building outline in the image exceeds the building outline of the database input layer.
Another reason for these inaccuracies is the pixel-scale in which edges of objects in the
image do not conform to the vector layer. This inaccuracy can be considered as “noise”
that depends on the image's spatial resolution.
An important indicator of the shadows prediction quality lays in the spatial accuracy of
the shadows – the shadows polygons predicted by the SFM don't exceed the shadow
boundary cast by buildings by more than a single pixel level (can be best observed in
non-vegetated areas in the image). To conclude, based on visual and quantitative
analysis, the shadow polygons produced by the SFM simulate with high accuracy the
shadows cast by matching building shadows in the aerial image. The validation step
stresses the importance in having an accurate database as an input for calculating the
shadow geometry by the model.
Figure 28: A sample of the validation of the test zone, demonstrating the comparison between the aerial image and classified vector data within the case study area (classified aerial image – left and original aerial image – right).
Figure 5. planer quadrents
Pixels classified as
built area
Pixels classified as
Shadows cast by
the buildings
Pixels classified as
Vegetation within the
shadow polygon
boundary
Shadow polygons
(generated by the
SFM)
Buildings in the
image
Building outline of
the image
exceeding the
building GIS layer
60
4.5. SFM possible applications.
The shadow outputs can be easily analyzed by the GIS for various urban planning and
architectural purposes. The SFM final outputs can be directly and indirectly utilized for
the following applications:
1 – Open space walkability analysis – detection of areas exposed to direct solar
radiation, can be beneficial for open space planning.
2 - Shadow maps – an innovative, rapid method for generating shadow maps often used
for landscape planning purposes.
3 - Analyzing the solar exposure of urban vegetation to optimize planning of
vegetation in urban open spaces.
4 - Solar access – detection of buildings that are influenced by shadows. When the
shadow polygons intersect with the buildings in the database layer it simply means that
the solar rights of the buildings are violated regardless of the exact extent of the
envelope area (facets and roof) which is being influenced by the shadows.
5 - Building integrated solar photovoltaics – detection of “shadow free area” on
building roofs. By using the output polygon layer influencing the buildings, an area that
is fully exposed to the sun can be detected using the model.
6 - Thermal solar collectors – detection of roof surfaces that are not influenced by
shadows.
7 - Climate zoning of urban areas – The SFM's ability to produce shadow polygons at
an urban scale can be used as a support tool for integrating climate considerations in
city zoning.
8 - Assessment of vertical shadow projection on the walls of buildings – improving
energy consumption assessment of buildings in urban environments, since the model
provides insights on the influence of shadows on the built area.
In addition to the possible applications mentioned above, the SFM provides beneficial
outputs such as the 3D layer of shadow volumetric boundary vertices, which can be
used as a platform for accurate 3D analysis in parallel with ongoing developments in the
GIS field.
61
4.6. Assessment of shadow area cast on walls – yearly trend analysis.
Based on the SFM results presented previously, an analysis of shadows cast on walls
was carried out. First the building facets were classified according to their orientation in
relation to the sun's position. The classification was done by using a mini-model that
automatically selects the specific facets from the SFM outputs. The selection was done
by determining which facets are up to -/+ 90˚ of azimuth range. Then by reversing the
selection, one gets all of the shadowing facets. The next step was to intersect the SFM
shadow output with the layer of the facets facing the sun. The outcome of this
intersection is a layer output of wall segments that are influenced by the shadows cast
by surrounding buildings. An example of the results can be seen in Figure 29. This
process was applied to the entire case study area for the same time configurations which
were applied to the SFM calculations: December 21st and June 21
st at 9:00, 12:00 and
15:00.
Figure 29: Demonstration of 2D Analysis of wall segments influenced by shadows cast in the urban built environment by neighboring buildings, based on SFM results.
Figure 5. planer quadrents
62
The isolated wall segments that consist of shadows constitute the “playing field” in
which the shadow can potentially be projected. Since the exact intersection of the wall
segment and the 3D shadow entity can’t be topologically and automatically calculated
with the developed model, the exact shadowed area on the wall cannot be evaluated. Yet
a quantitative assessment can be made for the minimum and maximum area of shadow
influence over the wall segments.
For calculating the minimum influence, the layer of shadow footprint influencing the
buildings was used, assuming that the proportion between the building’s shading wall
area (ASW) and the total shadow footprint (SF) area projected is the same as the
proportion between the shadow footprint area influencing the buildings (SFB) and a
projected shadow on the walls at a specific point of time. The minimum shadow
influence (MSI) area on building walls was calculated in the case study as follows:
MSI = (ASW / SF) * SFB
The calculation applied by the GIS for the time configurations mentioned before is
shown in the following results for minimum shadow area projected on the walls:
Predicted minimum area of shadow influence on walls (sq. m.)
21-Dec 21-Jun
9:00 307135.0 28218.9
12:00 117096.2 262.4
15:00 305268.8 28076.7
Although the values are an underestimation, they provide a solid quantitative area of
minimum shadow effect on the buildings. The undervaluation is a result of the 2D
features that might “lose” a small part of the shadow in cases where the shadow
footprint exceeds the building's outline and in cases where the shadow area of influence
Table 9: Minimum shadow area cast on walls of buildings in the case study area at 9:00, 12:00 and 15:00 during summer and winter solstice days.
Figure 5. planer quadrents
63
on the wall can’t be distinguished automatically from the area of shadow influence on
the roof.
The maximum effect of shadow on the walls is the total area of wall segments that are
affected by the shadow. The GIS analysis regarding these results is shown in table 10.
Predicted maximum area of shadow influence on walls (sq. m.)
21-Dec 21-Jun
9:00 577,616 219,173
12:00 325,763 15,305
15:00 565,912 212,806
The results shown in Tables 9 and 10 can be used as a basis for a yearly interpolation of
the possible minimum and maximum shadow projection on the walls for each month
and for each hour between 9:00 and 15:00. After performing an interpolation (presented
in the Appendix) the average of the minimum and maximum results was calculated.
The average minimum projected shadow area on the wall is calculated as 120,500
square meters, while the maximum average is calculated as 282,200 square meters. The
uncertainty of the precise shadow area projection lays somewhere between the
minimum and maximum predicted values. At high altitude sun angles, the expected
shadow area should be closer to the minimum values while at lower sun angles the
values are expected to be closer to the maximum values. Another factor is the urban
density – the lower it is, in comparison to the buildings in the case study area, the closer
the expected shadow area will be to the minimum values presented. For the purpose of
this research, the average value between the maximum and minimum values was chosen
for analysis (a yearly average of 201,500 square meters of shadow area in the case study
area, for a given moment between the hours 9:00 and 15:00).
Table 10: Maximum possible shadow area cast on walls of buildings in the case study area at 9:00, 12:00 and 15:00 during summer and winter solstice days.
Figure 5. planer quadrents
64
The next step in the analysis was to perform GIS calculations to derive the breakdown
of the average yearly and hourly influence of shadowing on walls, according to the facet
classification direction calculated by the SFM. For each facet direction, the percentage
of average shadow projection on the facet out of the total facet area was calculated and
seasonally analyzed and is shown in Table 11. (The complete detailed results are shown
in the Appendix, Sec. 9.5) The calculations were done by calculating the proportions of
the wall segments containing the shadow (the maximum possible shadow) and applying
the same proportions to the yearly average shadow area projected on walls.
The results of this analysis were used as an input in the thermal simulation of individual
buildings, in order to refine the results of the energy assessment by accounting for the
effect of overshadowing in the urban environment. Since the main effect of shadows
cast on walls is the shading of window openings, the building descriptions were
modified to reflect this. The modification of the windows was done by adding shade in
accordance with the shadow percentage on the walls, according to the window direction
(assuming that window distribution within the area of the wall is random). For example,
a shade factor of 12.5% was added to all the east-facing windows in the building model
that served as input for the thermal simulation phase presented in Chapter 2. The same
principle was applied for the rest of the windows, and for both kinds of simulation sets:
Seasonal analysis of Shadow influence % on walls
Wall orientation
Winter Spring Summer Fall Total average
South 34.0% 18.7% 13.6% 28.9% 23.8%
West 20.6% 11.3% 8.2% 17.5% 14.4%
East 17.9% 9.8% 7.2% 15.2% 12.5%
North 2.2% 1.2% 0.9% 1.9% 1.5%
Table 11: The relative % of shadow area cast on the facets out of the total facet area, classified by their orientation and by season according to seasonal analysis of the SFM results.
Figure 5. planer quadrents
65
the SI 1045 reference building model, and the improved SI 5282 building model. The
simulations were run for the four climate zones with the shadow refinement component,
and compared to the energy consumption of the previous “per unit” results. The
influence of shadows on the energy savings was analyzed by taking into consideration
the pronounced seasonal variability between summer and winter. Two sets of
simulations were carried out, and maximal and minimal energy saving possibilities were
assessed by using the SFM analysis results (presented previously) to the EnergyUI.
A summarized comparison of results for the reference building is shown in Table 12.
The shadow refinement applied in the simulations for the thermally improved building
showed minor differences, and the total influence of shadows on the energy
consumption of the building was never higher than 1%. This can be explained by the
improved window-related design, which practically neutralizes the shadowing effect.
It can be observed in the results of the reference building model that the energy % of
savings for heating purposes are negative – which means that during the winter, the
shadows are responsible for an increase in heating energy requirements. On the other
hand, energy required for cooling is reduced – which means that shading contributes to
energy saving for cooling purposes.
Table 12: The influence of shadows on the yearly energy savings considering the annual variance of shadow area on the reference building (according to SI 1045).
Figure 5. planer quadrents
Energy saving % due to urban shadow influence
(summer period)
Energy saving % due to urban shadow influence (winter period)
Cooling Heating Total Cooling Heating Total
Zone A 3.00% -1.08% 2.24% -1.83% 2.05% 0.22%
Zone B 2.98% -1.83% 2.44% 0.01% -1.37% -0.21%
Zone C 4.21% -0.96% 2.71% -0.08% -2.32% -0.80%
Zone D 2.37% -4.39% 2.30% 1.12% -0.06% 1.07%
*Weighted
average 3.15% -1.57% 2.43% -0.19% -0.43% -0.17% -0.43% -0.17%
66
According to the results of the simulation comparison, the overall influence of urban
overshadowing on the energy consumption in buildings over a yearly cycle is positive,
and is lower than 2.5% of the total energy savings, in comparison to simulation results
that did not take into account the shadow influence. These results reflect a certain
balance between the influence of higher density in compact urban areas, and that of low
density in less dense suburban areas, on heating and cooling energy requirements in
buildings.
Although the overall shadow influence on energy consumption may appear to be low in
percentage terms, this is partly due to the nullifying values in separate seasons, for
heating and cooling (which if combined, could reach 7%).
Future research is needed to study the influence of various urban densities in more
detail, and to perform a seasonal analysis of the energy savings potential. The SFM can
be used as an efficient tool for that purpose.
These results are used as a refinement factor for assessing the energy saving potential
through climate-conscious design and building at a national scale, presented in the next
chapter.
67
5. Energy saving potential assessment: national-scale perspective.
5.1. Introduction – national scale assessment of energy saving
potential.
The stages presented so far described the assessment of the energy saving potential at
the level of the individual building, in accordance with climatic conditions and
modifying effects of the built surroundings.
These building-scale results highlight the potential for cost-efficient measures that can
promote energy savings and meaningful reductions in greenhouse gas emissions in
Israel (McKinsey Report, 2009). A major obstacle for the advancement of such
solutions is the lack of quantitative, transparent, and detailed estimates of the potential
for actual savings at the national level – which is also a function of the quantity of
newly-designed buildings in which such strategies can be implemented. Such an
analysis is presented in this chapter and can be used as a platform for national-scale
analysis in other places.
5.2. A national-scale spatial and temporal construction analysis:
methodological outline.
This stage of the research involves a detailed quantitative national-scale analysis of
residential construction trends in Israel. An understanding of these trends, including
construction rates, building heights, and geographical distribution among regional
climate zones, is essential for developing a predictive model to forecast the potential
penetration of improved building design. In order to quantify this potential, a number of
different elements are taken into account:
a. Temporal variance – the annual rates of housing construction and of population
growth, and their variation over longer periods of time.
b. Spatial variance – the geographic distribution of housing construction among
different climate zones.
68
c. Qualitative variance – the type of housing, particularly as described by the
number of floors in the building.
Based on the analysis of population data from the Israel Central Bureau of Statistics
(ICBS) and a geographic analysis of physical housing distribution (using GIS), we
obtained a sufficient level of information for assessing the trends in each of the above-
mentioned elements. The assessment process of the relevant temporal and spatial
construction trends are presented in Figure 30. The assessment contains the vast
majority of the settlements and population in Israel (more than 95%).
Eventually, using the national assessment as a platform combined with the results of
the previous stages, a model was assembled to provide a nationwide forecast of the
energy saving potential through climate conscious building for the near future.
Figure 30: Schematic model of spatial analysis process, using GIS to characterize the spatial and temporal national-scale trends describing construction rates and heights of residential units.
ICBS data
Settlement's coordinate
data
Yearly construction height in each
settlement
Geo-sectioning of settlements according
to climate zones
GIS spatial analysis
SI 1045 report
Climate zone map Yearly construction completion in each
settlement
Geo-sectioning of residential units by relative height in
settlements according to climate zones
Geo-sectioning of construction completion
area in settlements according to climate
zones
Spatial and temporal construction trend assessment
69
5.2.1 Recognition of Building Completion Trends According to Climate
zones in Israel.
The difference in energy consumption in buildings changes significantly from one
climate zone to another. Therefore, calculating the trend of building distribution in the
country according to climate zones is essential for making a forecast based on energy
demand for acclimatization.
In order to calculate the trend, we used detailed building completion data from the
ICBS. The data referred to residential building completion in square meters for every
settlement in Israel for each year from 1995 to 2012. These data were spatially linked
through the GIS to a map of climatic regions prepared on the basis of the climatic
regions according to the compulsory standard SI 1045.
The vast majority of the settlements were linked using the system. The information that
was spatially anchored includes the annual building completion of each settlement. In
addition to these data, we also linked information on the population of each settlement.
The settlements that were linked contain 97.5% of the population and around 95% of
the residential building completion.4 These settlements were sectioned according to the
climatic zones and the average annual trend of building completion in these settlements
was calculated, as can be seen in Figure 31. In the map we can see the average
distribution of building completion for each settlement according to climatic zones. The
greatest concentration of residential building is in the Gush Dan area, which stands out
clearly in this visual analysis. In Table 13 a summary of total construction area
distribution by climate zones based on the GIS analysis is presented.
4 The rest of the settlements were not spatially linked to the system because of a lack of spatial
information due to the small size of the settlement or because they were military bases with residential buildings, as well as illegal building area for which there is no spatial information. In these settlements, sectioning of the residential building completion according to climate regions was done according to the distribution of the rest of the settlements.
70
Table 13: Distribution of average annual building completion area during the years 1995-2012, as analyzed with GIS using ICBS raw data regarding building completion area in each settlement in Israel.
Climate zones
Zone A Zone B Zone C Zone D
Average annual area of
building completion, 1995-
2012 (sq. m.)
053305000 050305300 1035000 0015300
Annual average percentage of
building completion (%) 03% 31% 01% 0%
Figure 31: Spatial distribution and correlation between average construction completion area and climatic zones in Israeli settlements (more the 1150 settlements) between years 1995-2012. As analyzed by GIS
(Sq.M).
71
A detailed data analysis of the annual distribution of building completion in settlements
based on climatic regions can be seen in Figure 31.
In the graph which shows residential building completion (Figure 32) between 1995 and
2012, it is clear that most of the building occurs in climate region B followed with
climate region A. A statistically significant trend cannot be identified in the increase in
building rates in all climate regions, , between 1995 and 2012, though there is a
prominent increase in 1997. This increase can be explained following a long-term
examination made for residential building completion data between 1950 and 2011.
This analysis examined trends in population growth and indicated that this increase is a
reaction of the housing market to the massive immigration from the former Soviet
Union in the early nineties (see Figure 34).
0
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4000
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7000
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iden
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Sq.m
.)
(Th
ou
san
ds)
Years
Climate zone D
Climate zone C
Climate zone B
Climate zone A
Construction completion
sectioned by climate zone.
Figure 32: Distribution of average building completion area during the years 1995-2012, by climate zone. Analysis based on ICBS (2013) data.
72
5.2.2. Identifying building trends by apartment floor-height according to
climate regions.
Previous chapters explained and demonstrated, through the results of the thermal
simulations, that the location of the apartment in the building has an effect on the
consumption of energy for acclimatization. The housing units evaluated are divided into
four types: single-story house, ground floor in an apartment building, mid-level floor in
a building, and upper floor apartment. In each apartment type, the consumption of
electricity for cooling or heating the apartment is different and related to the scale of
exposure to the ground or to the air. The geographic location data that were sectioned
were taken from the ICBS database and provide detail, to the level of the individual
settlement, on the distribution of housing according to the number of floors in the
building.
Using the ICBS data, we first calculated the number of apartments according to floor
type in accordance with the category of energy consumption in each settlement that was
spatially linked to a climate region (the same settlements linked in the previous section).
The annual building completion rates were then sectioned by apartment type. The
sectioning performed is annual from 1995 to 2012, and its results are detailed in Figure
33.
From 2007 to present we can identify a trend of increasing built area on mid-level floors
of apartment buildings. This corresponds with a general upward trend in the ICBS data
regarding the total amount of built area for residential purposes during those years.
73
5.3. Estimating a future change rate of construction completion.
Along with the quantitative estimation of energy consumption required for
acclimatization by area (in square meters) for the different apartment types and climate
zones, which was carried out, an estimation of future residential building area is the
foundation for constructing the energy saving potential forecasting model. This serves
as the basis for linking the predictions performed at the level of the single building and
at the level of the urban scale, to predictions regarding the building distribution, based
on the elements specified in the previous sections, and analyzed, so far, at the national
level.
A common approach for estimating future rates of construction is to examine the
relationship of past housing trends with the rates of population growth, based on
historical data (Israel Master Plan 2020). According to the analysis of data from the
0
1000
2000
3000
4000
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6000
7000
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Res
iden
tial
co
nst
ruct
ion
en
d (
Sqm
)
(Th
ou
san
ds)
Years
Roof floor apartment
Middle floor apartment
ground floor apartment
Ground house
construction complition
sectioned by apartment type
Figure 33: Distribution of average building completion area during the years 1995-2012 by residential unit location (ground-level, mid-level, roof-level and detached house). Analysis based on ICBS (2013) data.
74
ICBS (2012) over the years 1957-2011 (Figure 34), we can see a strong connection
between the annual population growth and the annual rate of residential building
completion (i.e. the annual increase in built area). Especially prominent is the spike in
both population growth and construction at the beginning of the 1990s, which
corresponds with the major immigration wave from the former Soviet Union (1991),
and the even larger spike in construction in the late '90s. This highlights the typical time
lag between population increase and housing completion, as the market responds to a
rise in demand.
In order to establish the connection between population growth and building
completion, a statistical analysis of the correlation between these two variables over the
years 1957-2011 was performed (Figure 35). For the purpose of the statistical
examination, anomalous years in which extreme spikes were observed (all in all, three
years were removed from the statistical analysis) were discarded. The results of the
linear regression of the rate of increase in the annual built area as a function of the
population growth can be seen in Figure 35. The high correlation coefficient (R² = 0.63)
Figure 34: Comparison of long-term trends in annual population growth and housing construction, 1957-2011, based on data from the ICBS (2012).
75
and the level of significance that is larger than 0.95 (P-value < 0.05) reflect, statistically,
a good correlation between the annual population growth rate and the annual residential
building area.
Based on this correlation between the trends of annual population growth and increase
in built area, we performed an extrapolation of the future changes in built area based on
the ICBS population growth forecast. The ICBS refers in its forecasts to a natural
population growth rate and to the internal variance within it, due to differences in
population growth among various population sectors that are differentiated by religion,
culture, and nationality. The ICBS population forecast for 2035 provides three different
scenarios, which are the result of different weights given to these groups according to
their estimated population growth: a low growth-rate scenario, a medium growth-rate
scenario, and a high growth-rate scenario. The forecast is given in five-year intervals,
and the expected population in intermediate years was estimated through linear
interpolation.
Figure 35: Statistical analysis of the correlation between the annual rates of residential building construction (built area in sq. m.) and population growth, 1957-2011. Based on data from ICBS (2012).
y = 43.269x R² = 0.6267
P-value < 0.05 0
1,000
2,000
3,000
4,000
5,000
6,000
7,000
8,000
9,000
10,000
0 50 100 150 200
An
nu
al c
on
stra
ctio
n E
nd
(SQ
M)
(Th
ou
san
ds)
Annual population growth (Thousands)
y = 43.269x R² = 0.6267 P-value < 0.05
Relation between construction rate to population growth
76
In order to estimate the future built area we calculated the averages of the new built
areas per person for each year (using ICBS data since 1957 as a baseline). According to
the analysis of residential building completion as a function of population growth, the
annual mean is around 42 square meters per capita. The variance from year to year is
relatively high, yielding a wide range of between 11 and 66 square meters per capita,
but there is no significant trend of increase or decrease over the years (a statistical
analysis found that the building area per capita does not change significantly between
1957 and 2011). Therefore, this mean was used as a basis for the future building
forecast, which was then used to calculate the national energy savings potential.
6. National-scale energy savings forecast model.
The goal of this study is to provide a tool to forecast the energy saving potential through
climate conscious building. A model was developed based on an evaluation of the
relevant elements in order to perform an estimate that reflects the energy consumption
in residential buildings taking into account the variation, on a national level, of different
conditions in which the apartments were built. To achieve the main goal of the research
a synthesis of all findings to date is presented in the prediction model to achieve the
main goal of the research. The model is comprised of several distinct phases (in
accordance with the findings so far):
Phase A – Calculating the annual population growth using an interpolation based on the
data from the ICBS population growth forecast until 2035.
Phase B – Spatial sectioning of the expected annual population growth by climatic
region (according to three population growth scenarios).
Phase C – Calculating the distribution of the future residential building construction per
capita by climatic region.
77
Phase D –Annual calculation of the future residential building construction by climatic
region.
Phase E – Updating the model based on refined values.
Phase F – Calculating the consumption of electricity after sectioning and considering
the distribution of the built area according to building type, for all climatic zones.
Phase G – Calculating the expected annual energy savings according to the future built
area for each climatic region and for all the ICBS scenarios for population growth.
Phase H – Calculating the annual savings potential, forecast according to three
alternatives: high, medium and low.
The results of the forecast model for three scenarios of electricity savings are displayed
in Figures 36 and 37.
6.1. Forecast model results – annual savings potential for electricity
consumption in buildings.
6.1.1 Scenarios according to climatic zones – A forecast of annual
cumulative electricity savings.
(a)
0
500
1000
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(Mill
ion
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Zone A
Zone B
Zone C
Zone D
An
nu
al p
ote
nti
al s
avin
gs (k
Wh
)
High estimate scenario: Electricity saving potential by climate zones
78
(b)
From the scenarios presented in Figure 36, it can be seen that Climatic Zone B has the
most significant energy saving potential. This can be ascribed to the high rates of
construction in this region. In the "High" growth scenario this zone shows a potential
annual savings of over 2 billion kWh by the year 2035, which is twice that of the "Low"
growth scenario. The "Medium" growth scenario, which provides an averaged account
of potential savings, predicts an annual increase in savings of about 1.6% due to the
accumulation of efficient buildings in the national housing stock.
Figure 36: Forecast of annual energy savings potential (in millions of kWh) in climate-conscious buildings, by climatic zones in Israel. Separate estimations represent (a) high, (b) medium, and (c) low growth scenarios.
(c)
0200400600800
10001200140016001800
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(Mill
ion
s)
Zone A
Zone B
Zone C
Zone D
An
nu
al p
ote
nti
al s
avin
gs (k
Wh
)
Medium estimate scenario: Electricity savings potential by climate zones
0
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1200
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Zone B
Zone C
Zone D
An
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al p
ote
nti
al s
avin
gs (k
Wh
)
Low estimate scenario: Electricity savings potential by climate zones
79
A prediction of the nationwide annual savings potential is presented in Figure 37. All in
all, we can estimate the cumulative potential savings for the years 2014-2035 as
follows:
Low estimate: approximately 20,330 million kWh (average annual savings: 920 million
kWh).
Medium Estimate: approximately 28,800 million kWh (average annual savings: 1,300
million kWh).
High Estimate: approximately 37,500 million kWh (average annual savings: 1,700
million kWh).
Figure 37: Forecast of the accumulated annual energy savings potential in Israel as a result of improved climate-conscious building design, in millions of kWh by 2035, according to building construction scenarios based on (a) High, (b) Medium, and (C) Low estimates of future population growth.
0
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2029
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5
(mill
ion
s)
High estimatescenario
Medium estimateScenario
Low estimatescenario
Annual electricity saving potential through to climate-aware Green building design
A
nn
ual
po
ten
tial
acc
um
ula
tive
sav
ings
(kW
h)
Year
80
7. Conclusions and discussion.
This study utilizes a combination of methods and tools to provide a quantitative
national–scale assessment of the future potential for energy savings through climate-
conscious building design in Israel.
The predictive model developed allows future electricity savings to be estimated
according to three scenarios, assuming different rates of future construction based on
the statistical analysis of ICBS population and construction data (2012). The prediction
is a synthesis of all the findings and results of the analysis, performed at different scales
and stages presented in this study: the individual building-scale energy savings potential
per residential unit, a refinement factor reflecting the influence of urban overshadowing
of buildings on these per-unit energy savings, and a national-scale analysis of spatial
and temporal trends in population and construction related to the climatic zones in
Israel. The results of the analysis in these different stages have provided insights
regarding energy consumption in buildings at different scales:
1) Energy savings in new buildings as a function of building type and climatic region.
2) Recognition and assessment of significant levers (building design parameters) for
energy savings in new and existing buildings.
3) New method for quantitative assessment of shadows, cast by buildings in the urban
environment, on the energy consumption of adjacent buildings.
4) National-scale assessment of spatial and temporal trends in residential construction.
During all stages of developing the forecast model, a conservative approach was taken
in analyzing the data and the simulation results to create a safety margin for the forecast
values.
Analysis in each stage was carried out based on up-to-date official databases and
specialized software tools such as the thermal simulation software (EnergyPlus through
81
the ENERGYui interface) and GIS software (ArcGIS 10.2), both considered to be
industry standards.
The rate of increase in energy savings is a direct function of the estimated rate of
population growth according to ICBS projections, which is an average annual increase
of around 1.6% for medium growth estimation. The potential energy savings according
to the three scenarios vary quite widely from one another, with the difference between
scenarios reaching 1 billion kWh per year. According to the prediction model, in 2035
the savings potential is expected to be between 1,700 and 3,500 million kWh, and the
annual average savings ranges between 920 million kWh (low growth scenario) and
1700 million kWh (high growth scenario).
To compare, the "Reading" electric power station in Tel Aviv produced 978 million
kWh in 2011, while the "Eshkol" power station produced 3,100 million kWh that year
(Statistical Report 2011, Israel Electric Corp).
The overall weighted-average energy saving for the energy consumed to cool and heat a
building in a climate conscious building, as calculated from the results of the model,
accounts to about 48.5% of electricity, compared to a "business as usual" situation. The
prediction model can be modified, relatively simply, to expand the range of the forecast
or to modify it according changes in input parameters (such as a change in the level of
savings in the single building).
The results of the forecast model clearly point out that the cumulative energy savings
potential holds high significance in the long run and that there is a great potential for
energy savings through climate-conscious building design. As shown in this case study,
the application of climate-conscious design to new buildings may enable the realization
of this potential for energy savings, along with a significant potential reduction of
greenhouse gas emissions (depending on the composition of fuels used for power
generation, and the technologies used to produce and transport those fuels).
82
The results of this study may be used to promote climate-conscious building design by
demonstrating the high potential for energy savings embodied in building design that
takes advantage of the surrounding environment. Through the quantitative assessment at
different scales, potential levers may be proposed for promoting applicable actions in
climate-conscious building design and planning. Providing decision makers or private
sector entrepreneurs with “solid numbers” based on quantitative assessments regarding
the benefits embodied in climate-conscious design is one of the major obstacles that
hinder a larger scale uptake of green building strategies.
This study takes a step forward toward providing such a quantitative assessment, upon
which decisions regarding cost-effective solutions for improved building design at the
national level can be made.
83
7.1. Points for possible improvement and future recommended
research.
It should be pointed out that there are some significant points for improvement and for
future studies:
1. Definition of climatic zones – The climatic zone definition in the study is based on
the climatic zone distribution according to SI 1045 from the early 90’s, and does not
take into consideration a larger variance of sub-climatic zones that exists in Israel.
2. Simulation-based analysis. Assumptions that were made in the study hold a
significant influence on its final results, including the fixed assumption of thermal
comfort thresholds set for the simulation at 24˚ in summer and 20˚ in winter. Such
factors could not feasibly and definitively be evaluated within the limitations of the
analytic tools used.
3. Urban microclimatic influences on energy consumption – Since the complex nature
of urban influences is not quantitatively definable, those influences are only partially
reflected in this study.
4. GIS analysis – The urban-scale shadow analysis presented in Chapter 4 is highly
dependent on the GIS database and GIS software tools. Future expected improvement in
both areas may provide a better and more accurate assessment in future studies.
5. Model validation and calibration through empirical research – The whole study is
based on a complex analysis of “modeling the reality” through the implementation of
software tools. Despite the highly recognized reliability of the GIS software, databases
and the thermal simulation programs, in the author's opinion additional empirical
research should be carried out in order to calibrate the findings of this study. Since not
enough green residential buildings exist in Israel in all the climatic zones, such
empirical validation remains a challenge.
84
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90
9. Appendix
9.1 Building plan upon which the model building for the simulations
was based. The plan is of a typical floor in buildings of “Ramot”
neighborhood in Beer-Sheva.
9.2. Thermal Simulation software EnergyPlus system of operation
(EERE, 2010).
91
9.3. Building materials used for the simulation models according to
climatic zones.
Zone A Zone B Zone C Zone D
External wall section – Reference building (SI 1045)
Cement Mortar (2.5
cm)
Cement Mortar (2.5
cm) Limestone (5 cm)
Cement Mortar
(2.5 cm)
Block CB-13
(22 cm)
Block CB-13
(22 cm)
Cement Mortar
(2.5 cm)
Block CB1-13
(23 cm)
Lime-Cement Mortar
(2 cm)
Lime-Cement Mortar
(2 cm)
Block CB31-7
(20 cm)
Lime-Cement Mortar
(2 cm)
Lime-Cement Mortar
(2 cm)
Roof material sectioning – Reference building (SI 1045)
Bitumen (0.5 cm) Bitumen (0.5 cm) Bitumen (0.5 cm) Bitumen (0.5 cm)
Light concrete (5
cm)
Light concrete (5
cm)
Light concrete (5
cm)
Light concrete (5
cm)
Rigid granular
polystyrene (5.5 cm)
Rigid granular
polystyrene (5.5 cm)
Rigid granular
polystyrene (5.5 cm)
Rigid granular
polystyrene (5.5 cm)
Regular concrete (14
cm)
Regular concrete (14
cm)
Regular concrete (14
cm)
Regular concrete (14
cm)
Lime-Cement Mortar
(2 cm)
Lime-Cement Mortar
(2 cm)
Lime-Cement Mortar
(2 cm)
Lime-Cement Mortar
(2 cm)
Zone A Zone B Zone C Zone D
External wall section – Improved building (SI 5282)
Cement Mortar (2.5
cm)
Cement Mortar (2.5
cm)
Limestone (5 cm) Cement Mortar
(2.5 cm)
Rigid granular
polystyrene (3 cm)
Rigid granular
polystyrene (3 cm)
Cement Mortar
(2.5 cm)
Rigid granular
polystyrene (3 cm)
Block CB1-13
(23 cm)
Block CB1-13
(23 cm)
Rigid granular
polystyrene (3.5 cm)
Block CB1-13
(23 cm)
Lime-Cement Mortar
(2 cm)
Lime-Cement Mortar
(2 cm)
Block CB31-7
(20 cm)
Lime-Cement Mortar
(2 cm)
Lime-Cement Mortar
(2 cm)
Roof material sectioning – Improved building (SI 5282-2)
Bitumen (0.5 cm) Bitumen (0.5 cm) Bitumen (0.5 cm) Bitumen (0.5 cm)
Light concrete (5
cm)
Light concrete (5
cm)
Light concrete (5
cm)
Light concrete (5
cm)
Rigid granular
polystyrene (5.5)
Rigid granular
polystyrene (5.5)
Rigid granular
polystyrene (7 cm)
Rigid granular
polystyrene (6 cm)
Regular concrete (14
cm)
Regular concrete (14
cm)
Regular concrete (14
cm)
Regular concrete (14
cm)
Lime-Cement Mortar
(2 cm)
Lime-Cement Mortar
(2 cm)
Lime-Cement Mortar
(2 cm)
Lime-Cement Mortar
(2 cm)
92
9.4. Simulation results of electricity consumption and potential savings in residential units as a function
of floor location and climatic zones.
Tel-Aviv (Zone A)
Single house Acclimatization electricity
Loads (Kwh/Sqm) SI-5282 electricity loads
(Kwh/Sqm) Res. unit consumption
(Kwh/Sqm)*220 Res. unit saving
SI-1045 SI-5282 Cooling Heating SI-1045 Si-5282 % (Kwh/sqm)*220
26.8 13 5.6 7.4 5901 2860 52 3041
4-story building Acclimatization electricity
Loads (Kwh/Sqm) SI-5282 electricity loads
(Kwh/Sqm) Res. unit consumption
(Kwh/Sqm)*110 Res. unit saving
Floor SI-1045 SI-5282 Cooling Heating SI-1045 Si-5282 % (Kwh/sqm)*220
1 22.8 9.2 5.4 3.8 2509 1018 59 1491
2 24.2 10.4 7.3 3.1 2667 1147 57 1521
3 24.2 10.4 7.3 3.1 2667 1139 57 1529
4 28.2 13.9 7.6 6.3 3097 1524 51 1574
8-story building Acclimatization electricity
Loads (Kwh/Sqm) SI-5282 electricity loads
(Kwh/Sqm) Res. unit consumption
(Kwh/Sqm)*110 Res. unit saving
Floor SI-1045 SI-5282 Cooling Heating SI-1045 Si-5282 % (Kwh/sqm)*110
1 22.8 9.6 6.0 3.6 2507 1048 58 1459
2 24.2 10.6 7.1 3.5 2667 1158 57 1510
3 24.2 10.3 7.1 3.2 2667 1136 57 1532
4 24.2 10.4 7.1 3.3 2667 1139 57 1529
5 24.2 10.4 7.1 3.3 2667 1133 58 1534
6 24.2 10.3 7.0 3.3 2667 1133 58 1534
7 24.2 10.3 7.1 3.2 2667 1128 58 1540
8 28.2 14.5 7.3 7.2 3097 1601 48 1497
93
Beer-Sheva (Zone B)
Single house Acclimatization electricity
Loads (Kwh/Sqm) SI-5282 electricity loads
(Kwh/Sqm) Res. unit consumption
(Kwh/Sqm)*220 Res. unit saving
SI-1045 SI-5282 Cooling Heating SI-1045 Si-5282 % (Kwh/sqm)*220
33.2 15 7.3 7.7 7304 3300 55 4004
4-story building Acclimatization electricity
Loads (Kwh/Sqm) SI-5282 electricity loads
(Kwh/Sqm) Res. unit consumption
(Kwh/Sqm)*110 Res. unit saving
Floor SI-1045 SI-5282 Cooling Heating SI-1045 Si-5282 % (Kwh/sqm)*110
1 28.4 12.8 9.2 3.6 3128 1400 55 1728
2 30.4 13.4 10.6 2.8 3349 1477 56 1873
3 30.4 13.4 10.6 2.7 3349 1463 56 1886
4 35.0 18.1 11.9 6.2 3855 1988 48 1867
8-story building Acclimatization electricity
Loads (Kwh/Sqm) SI-5282 electricity loads
(Kwh/Sqm) Res. unit consumption
(Kwh/Sqm)*110 Res. unit saving
Floor SI-1045 SI-5282 Cooling Heating SI-1045 Si-5282 % (Kwh/sqm)*110
1 28.4 12.1 8.7 3.4 3128 1326 58 1802
2 30.4 13.4 10.5 2.9 3347 1466 56 1881
3 30.4 13.3 10.4 2.9 3347 1460 56 1887
4 30.4 13.3 10.3 3.0 3347 1463 56 1884
5 30.4 13.3 10.3 3.0 3347 1460 56 1887
6 30.4 13.3 10.3 3.0 3347 1460 56 1887
7 30.4 13.2 10.3 2.9 3347 1449 57 1898
8 35.0 17.7 11.8 5.9 3855 1939 50 1916
94
Jerusalem (Zone C)
Single house Acclimatization electricity
Loads (Kwh/Sqm) SI-5282 electricity loads
(Kwh/Sqm) Res. unit consumption
(Kwh/Sqm)*220 Res. unit saving
SI-1045 SI-5282 Cooling Heating SI-1045 Si-5282 % (Kwh/sqm)*220
26.2 13.6 2.9 10.7 5772 2992 48 2780
4-story building Acclimatization electricity
Loads (Kwh/Sqm) SI-5282 electricity loads
(Kwh/Sqm) Res. unit consumption
(Kwh/Sqm)*220 Res. unit saving
Floor SI-1045 SI-5282 Cooling Heating SI-1045 Si-5282 % (Kwh/sqm)*110
1 21.4 10.3 3.5 6.8 2353 1128 52 1226
2 23.6 10.5 2.9 7.6 2591 1152 56 1439
3 23.6 10.3 3.8 6.5 2591 1133 56 1458
4 26.9 16.8 2.6 14.3 2961 1854 37 1108
8-story building Acclimatization electricity
Loads (Kwh/Sqm) SI-5282 electricity loads
(Kwh/Sqm) Res. unit consumption
(Kwh/Sqm)*220 Res. unit saving
Floor SI-1045 SI-5282 Cooling Heating SI-1045 Si-5282 % (Kwh/sqm)*110
2 21.4 10.3 2.1 8.2 2353 1128 52 1226
3 23.5 10.5 2.9 7.6 2589 1150 56 1440
4 23.5 10.5 2.9 7.6 2589 1152 56 1437
5 23.5 10.5 2.8 7.7 2589 1155 55 1434
6 23.5 10.6 2.8 7.8 2589 1155 55 1434
7 23.5 10.6 2.8 7.8 2589 1161 55 1429
8 23.5 10.4 2.8 7.6 2589 1141 56 1448
9 27.0 16.8 2.4 14.4 2965 1854 37 1111
95
Eilat (Zone D)
Single house Acclimatization electricity
Loads (Kwh/Sqm) SI-5282 electricity loads
(Kwh/Sqm) Res. unit consumption
(Kwh/Sqm)*220 Res. unit saving
SI-1045 SI-5282 Cooling Heating SI-1045 Si-5282 % (Kwh/sqm)*220
43.1 27.5 25.2 2.3 9485 6050 36 3435
4-story building
Acclimatization electricity Loads (Kwh/Sqm)
SI-5282 electricity loads (Kwh/Sqm)
Res. unit consumption (Kwh/Sqm)*220
Res. unit saving
Floor SI-1045 SI-5282 Cooling Heating SI-1045 Si-5282 % (Kwh/sqm)*110
1 39.1 22.4 21.8 0.6 4299 2467 43 1833
2 43.5 25.1 24.8 0.3 4780 2764 42 2016
3 43.5 25.1 24.8 0.3 4780 2761 42 2019
4 47.0 29.8 28.0 1.8 5172 3275 37 1896
8-story building Acclimatization electricity
Loads (Kwh/Sqm) SI-5282 electricity loads
(Kwh/Sqm) Res. unit consumption
(Kwh/Sqm)*220 Res. unit saving
Floor SI-1045 SI-5282 Cooling Heating SI-1045 Si-5282 % (Kwh/sqm)*110
1 39.1 22.4 21.8 0.6 4297 2464 43 1833
2 43.5 25.1 24.8 0.3 4780 2758 42 2022
3 43.5 25 24.7 0.3 4780 2753 42 2027
4 43.5 25 24.7 0.3 4780 2756 42 2024
5 43.5 25 24.6 0.4 4780 2745 43 2035
6 43.5 24.9 24.6 0.3 4780 2739 43 2041
7 43.5 24.8 24.5 0.3 4780 2736 43 2044
8 47.0 29.5 27.7 1.8 5169 3240 37 1930
96
9.5. Buildings design parametric analysis of energy savings potential simulation
Simulation results – Saving by parameters
Tel-Aviv (Zone A) Jerusalem (Zone C) Beer-Sheva (Zone B) Eilat (Zone D)
Building Parameters
F1 F2 F3 F1 F2 F3 F1 F2 F3 F1 F2 F3
Wall insulation – SI 5282
heating 1.4 1.9 1.9 2.6 6 3.1 1.3 1.5 1.6 0.2 0.1 0.3
cooling 0.9 -0.4 0 -0.1 -3.4 -0.5 0.6 -0.1 -0.1 2.8 2.5 2.3
total 2.3 1.5 1.9 2.5 2.6 2.6 1.9 1.4 1.5 3 2.6 2.6
Roof insulation –SI 5282
heating 0.1 0.2 -1 0 2.8 1 0 0 0 0 0 0.1
cooling 0.7 -0.3 2.5 0 -2.8 0 0 0 0 0 0 0.3
total 0.8 -0.1 1.5 0 0 1 0 0 0 0 0 0.4
Window area – SI 5282
heating -0.3 -0.5 -1.4 2.8 6 3.2 -0.7 -1.2 -1.6 -0.2 -0.1 -0.4
cooling 5.9 7.1 9 0.7 -2.8 0.1 10.5 13.3 12.3 9 11.4 10.6
total 5.6 6.6 7.6 3.5 3.2 3.3 9.8 12.1 10.7 8.8 11.3
Window fenestration (improved – DG)
heating 0.3 0.5 0.3 1.1 4.1 1.2 0.4 0.5 0.3 0 0 0
cooling 2.9 2.7 2.9 1.3 -1.6 1.1 3.9 4.4 4.1 5 6.8 6.1
total 3.2 3.2 3.2 2.4 2.5 2.3 4.3 4.9 4.4 5 6.8 6.1
Winter shading
heating -1.5 -2 -2.3 1.8 5.3 2.7 -2.1 -2.8 -3.3 -0.6 -0.4 -0.8
cooling 6.3 8.3 7.9 -0.1 -2.9 -0.1 9.4 12.7 11.6 6.9 9.4 8.7
total 4.8 6.3 5.6 1.7 2.4 2.6 7.3 9.9 8.3 6.3 9 7.9
Ventilation- Natural
heating 0 0.1 0 0 2.8 0 0 0 0 0 0 0
cooling 3.6 5 5 2.7 1.2 3.7 3.3 5 4.6 1.1 2.4 2
total 3.6 5.1 5 2.7 4 3.7 3.3 5 4.6 1.1 2.4 2
Wall color - Bright
heating -0.3 -0.2 -0.4 -0.6 2.1 -0.7 -0.3 -0.3 -0.5 -0.1 0 -0.1
cooling 1.4 1.2 1.4 0.8 -1.9 0.8 1.3 1.6 1.4 1.3 1.4 1.3
total 1.1 1 1 0.2 0.2 0.1 1 1.3 0.9 1.2 1.4 1.2
Roof color - bright
heating 0.1 0.2 -0.5 0 2.8 -1.1 0 0 -0.7 0 0 -0.2
cooling 0.1 -0.3 2.4 0 -2.8 1.8 0 0 2.7 0 0 2.7
total 0.2 -0.1 1.9 0 0 0.7 0 0 2 0 0 2.5
Floor\Internal wall (heavy mass)
heating 0.1 0.3 0.1 0 0 0 0 0.2 0.1 0 0.1 0
cooling 0.1 -0.2 0.2 0 0 0 -0.1 0.1 0.1 0 0 0
total 0.2 0.1 0.3 0 0 0 -0.1 0.3 0.2 0 0.1 0
Retrofit – (fenestration, Shading, Wall\Roof color)
heating -1.5 -2.1 -10.3 2.3 6 2.2 -2.1 -2.8 -4.2 -0.7 -0.5 -1.3
cooling 8.9 11.2 19.7 2.6 -0.2 4.2 13 16.7 17.8 10.7 13.7 15.3
total 7.4 9.1 9.4 4.9 5.8 6.4 10.9 13.9 13.6 10 13.2 14
97
9.6. Shadow footprint model (SFM) as created using the ArcGIS model-builder.
The oval shape represents a GIS layer (blue oval represents an input and green oval represents an output), while the yellow rectangle represents a
calculation operator. The grey shadow represents that processing was completed. Part A of the model represents the input and facet orientation stage of
the model, Part B represents the geometry calculations of the shadow, Part C represents the preparation of the output layers of shadow polygons.
Part A Part B
98
Part B
99
Part C
100
9.7. Annual analysis of shadow area (Sqm) cast on walls in case study zone. (Based on SFM results).
The following tables presents minimal and maximal estimations based on interpolation done on the SFM results (Highlighted by pink
color)
Annual Minimal area of shadow influence on walls (in the case study area in Tel-Aviv)
Time/Date 21-Dec 21-Jan 21-Feb 21-Mar 21-Apr 21-May 21-Jun 21-Jul 21-Aug 21-Sep 21-Oct 21-Nov Avg.
9:00 307,135 260,649 214,163 167,677 121,191 74,705 28,219 74,705 121,191 167,677 214,163 260,649 167,677
10:00 243,789 206,307 168,826 131,344 93,863 56,382 18,900 56,382 93,863 131,344 168,826 206,307 131,344
11:00 180,442 151,966 123,489 95,012 66,535 38,058 9,581 38,058 66,535 95,012 123,489 151,966 95,012
12:00 117,096 97,624 78,152 58,679 39,207 19,735 262 19,735 39,207 58,679 78,152 97,624 58,679
13:00 179,820 151,439 123,058 94,677 66,296 37,915 9,534 37,915 66,296 94,677 123,058 151,439 94,677
14:00 242,545 205,255 167,965 130,675 93,385 56,095 18,805 56,095 93,385 130,675 167,965 205,255 130,675
15:00 305,269 259,070 212,871 166,673 120,474 74,275 28,077 74,275 120,474 166,673 212,871 259,070 166,673
Avg. 225,157 190,330 155,503 120,677 85,850 51,024 16,197 51,024 85,850 120,677 155,503 190,330 120,677
Annual Maximal area of shadow influence on walls (in the case study area in Tel-Aviv)
Time/Date 21-Dec 21-Jan 21-Feb 21-Mar 21-Apr 21-May 21-Jun 21-Jul 21-Aug 21-Sep 21-Oct 21-Nov Avg.
9:00 577,616 517,876 458,135 398,395 338,654 278,914 219,173 278,914 338,654 398,395 458,135 517,876 398,395
10:00 424,109 377,792 331,474 285,156 238,838 192,521 146,203 192,521 238,838 285,156 331,474 377,792 285,156
11:00 270,603 237,708 204,813 171,918 139,023 106,128 73,233 106,128 139,023 171,918 204,813 237,708 171,918
12:00 325,763 274,020 222,277 170,534 118,791 67,048 15,305 67,048 118,791 170,534 222,277 274,020 170,534
13:00 405,813 351,700 297,588 243,476 189,363 135,251 81,139 135,251 189,363 243,476 297,588 351,700 243,476
14:00 485,862 429,381 372,899 316,417 259,936 203,454 146,972 203,454 259,936 316,417 372,899 429,381 316,417
15:00 565,912 507,061 448,210 389,359 330,508 271,657 212,806 271,657 330,508 389,359 448,210 507,061 389,359
Avg. 436,525 385,077 333,628 282,179 230,730 179,282 127,833 179,282 230,730 282,179 333,628 385,077 282,179
101
9.8. Shadow influence on energy saving – Simulation analysis results (refinement factor for national-scale analysis).
Influence of shadow cast on walls on annual potential electricity savings ( % ), as analyzed by simulation
Tel-Aviv (zone A) Beer-Sheva (Zone B) Jerusalem (Zone C) Eilat (zone D)
Floor number
Cooling Heating Total Cooling Heating Total Cooling Heating Total Cooling Heating Total
1
2.629% 0.000% 2.244% 2.760% -4.600% 2.091% 3.108% -1.797% 1.847% 2.353% 0.000% 2.347%
2.527% 0.000% 2.170% 3.194% -5.476% 2.605% 4.705% 0.000% 3.696% 2.861% 0.000% 2.854%
4.021% -2.054% 2.312% 3.239% -1.983% 2.122% 4.107% 0.000% 2.456% 2.255% -14.375% 1.890%
3.710% -2.170% 2.212% 3.915% 0.000% 3.274% 5.782% -1.162% 3.309% 2.760% 0.000% 2.724%
2
2.357% 0.000% 2.154% 2.477% 0.000% 2.340% 3.040% 0.000% 2.404% 2.421% 0.000% 2.416%
2.291% 0.000% 2.099% 3.117% 0.000% 2.995% 4.756% -2.091% 3.583% 3.076% 0.000% 3.070%
3.588% -2.447% 2.255% 3.129% -2.500% 2.368% 4.144% -1.198% 2.531% 2.091% 0.000% 2.077%
4.144% -2.674% 3.038% 3.275% 0.000% 2.974% 5.453% -1.386% 3.651% 2.505% 0.000% 2.500%
3
2.312% 0.000% 2.197% 2.410% 0.000% 2.130% 2.578% -1.027% 1.373% 1.769% -38.333% 1.539%
2.231% 0.000% 2.214% 2.797% -3.108% 2.260% 4.563% -1.085% 2.891% 2.410% 0.000% 2.396%
3.129% -1.764% 1.990% 2.454% -1.983% 1.788% 3.455% -0.891% 1.906% 1.620% 0.000% 1.594%
3.029% -1.852% 2.016% 3.044% -2.347% 2.365% 4.832% -0.935% 2.867% 2.264% 0.000% 2.237%
102
9.9. Construction completion distribution 1995-2012 by climatic zone and residential unit type – results of the GIS
analysis.
construction completion by
climate zone. (S.qm.)
1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012
Climate zone A 1398254 1895596 2521713 1838388 1660004 1758145 1341193 1603817 1381417 1420105 1476455 1303590 1099219 1350065 1381093 1353235 1497145 1752289
Climate zone B 3585248 4476344 5665317 4591252 3776761 3668400 3734564 3499467 3229306 3143852 2960890 2964776 3073076 3094261 3616095 3813999 4190332 4444156
Climate zone C 814217 936774 1087310 1061607 1124547 1004801 1007024 934092 848130 737752 791391 765129 824828 852450 885795 871249 777399 970397
Climate zone D 120281 108286 226660 233753 129688 121655 103220 114625 129146 89291 65264 102506 106877 70224 73018 133516 104125 125158
Grand Total 5918000 7417000 9501000 7725000 6691000 6553000 6186000 6152000 5588000 5391000 5294000 5136000 5104000 5367000 5956000 6172000 6569000 7292000
Construction
completion by floor height. (S.qm.)
1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012
Ground house 445153 411345 529597 906287 795265 550548 514440 303174 263924 270411 320589 221312 229257 240750 304955 265947 336381 324265
Ground floor apartment 1946060 2236190 2673618 2122438 1794521 1895090 1863467 1833400 1623875 1567298 1527059 1424171 1577138 1565757 1836360 1877212 1858563 1995165
Middle floor
apartment 1561518 2499380 3605151 2542979 2284575 2186863 1921147 2165776 2040305 1973909 1902523 2044649 1690707 1961822 1937078 2110251 2494776 2960660
Roof floor apartment 1946060 2236190 2673618 2122438 1794521 1895090 1863467 1833400 1623875 1567298 1527059 1424171 1577138 1565757 1836360 1877212 1858563 1995165
Grand total 5898791 7383105 9481984 7694142 6668882 6527590 6162522 6135751 5551979 5378917 5277230 5114302 5074240 5334086 5914753 6130622 6548283 7275256
103
Ben-Gurion University of the Negev The Jacob Blaustein Institutes for Desert Research
The Albert Katz International School for Desert Studies
Assessment of potential energy savings in Israel through
climate-aware residential building design.
Thesis submitted in partial fulfillment of the requirements for the degree of
"Master of Science"
By: Morel Weisthal
March, 2014