Department of Architecture,Middle East Technical University,yilmaz.derya@metu.edu.tr

Derya YILMAZ, Assoc.Prof.Dr. Ali Murat TANYER

A study on multi objective optimization applications used to optimize building shape for energy efficiency

Introduction

Method

Analysis

Conclusion

Table of Contents

Optimization

Optimization is a process that searches for the optimal solution with respect to theobjective functions to be maximized or minimized, possibly subjected to someconstraints of the dependent variables (Attia et al., 2013)

Mathematical optimization is the process of finding the best solution to a problemfrom a set of available alternatives (Nguyen et al.,2014).

space layout design|structural design|construction|acoustics|HVAC systems|building components:insulation

Multi Objective Optimization

Multi-objective optimization which has to combine two aspects: optimization anddecision support, engages optimization in the presence of more than one objectivefunctions.

The major difference between single and multi-objective optimization is that in thecase of latter, there is usually no single optimal solution, but a set of equally goodalternatives with different trade-offs, also known as Pareto optimal solutions(Karmellos et al., 2015).

Motivations

all new buildings must be nearly zero energy buildings (NZEB) from 31 December 2020. (Since 31 December 2018, all new public buildings already need to be NZEB);

EU countries must set cost-optimal minimum energy performance requirements for new buildings, for the major renovation of existing buildings, and for the replacement or retrofit of building elements (heating and cooling systems, roofs, walls and so on);

health and well-being of building users will be promoted, for instance through an increased consideration of air quality and ventilation;

the conventional “trial-and-error” design methodology is largely dependent on designers' knowledge and experience and building energy efficient design optimization technique is a more efficient, more powerful design solution (Si et al.,2016)

how to optimize and match the relationship among technology-economy-ecology is stilla problem (Wu et al., 2018)

EPBD (2018/844/EU)

Multi Objective Optimization

Several authors indicate that there is a clear upward trend in the number of coreliterature on building energy efficient design optimization especially in late 2000s incomparison to 1980s and 1990s (Evins (2013),Nguyen et al.(2014), Lin (2014), Shi etal.(2016)).

Optimization Objectives

Annual Energy Consumption Life Cycle Energy Consumption Heating Load

Cooling Load CO2 Emissions Reducing Energy Cost Life Cycle Cost

Thermal Comfort Mean Air Temperature

Visual Comfort Discomfort Hours Lighting Quality

Optimization Design Variables

Building Envelope ( Opaque and/or Transparent Building Envelope)

Shape and Form

Mechanical Systems

U value of the exterior walls, thickness of insulation, insulation type, thermal properties of external walls, roofs and floors, glazing,window size, window shading, etc

Floor shapes: rectangular, trapezoid,L-shape, U-shape,T shape, cross shape,H shape , orientation, rectangular floor plans: aspect ratio, ratio between the length and the width of the floor plan

Photovoltaic system, solar thermal system, heating cooling distribution, pumps and fansVentilation strategy, lighting control

Shi et al. (2016)

Optimization Techniques

Existing building energy optimization techniques can be classified into three categories (Tian et al.,2015):

Stand-alone optimization tools such as GenOpt,MATLAB,Dakota,modeFRONTIERand ModelCenter,

Optimization engine oriented tools such as GENE_ARCH,MOBO,jEPlus+EA and MultiOpt,

Building Energy simulation tool-based optimization tools such as Design Builder optimization module, BEopt, and Opt-E-Plus.

Building Optimization Procedure:

Identify design variables and constraints

Select simulation tool and creation of a baseline model

Select objective functions

Select optimization algorithm

Run simulations

Touloupaki& Theodosiou (2017)

Number of Optimization Objectives

Two Objective Functions Three Objective Functions

Wu et al. (2018), Gou et al. (2018), Yu et al. (2015),Abdollahi&Meratizaman (2011)Asadi et al. (2012), Cassol et al. (2011)Diakaki et al. (2008),Evins et al. (2012)Evins et al. (2012),Fesanghary et al. (2012)Gayne & Andersen (2012),Hamdy et al. (2011)Hamdy et al. (2012),Huang et al. (2012)Kampf et al. (2010),Kayo&Ooka (2009)Marks (1997),Ooka&Komamura (2009)Palonen et al. (2009), Peippo et al. (1999)Pernodet et al.(2009),Pounney (2012)Salminen et al. (2012), Wang et al. (2005)Wang et al. (2006), Wright et al. (2002)

Four Objective Functions

Agrama (2014)Caldas (2008)Chantrelle et al. (2011)Evins et al. (2011)Jin&Overend (2012)Mahdavi&Mahattanatawe (2003)Turrin et al. (2011)Verbeeck&Hens (2007)Mangan&Oral (2016)

Many Objective Optimization

Chand&Wagner (2015)

Method

Three stages applied during the article search process in this study are listed as follow;

Stage 1: Science Direct Database is used for article selection.

Stage 2: “multi objective optimization”, “shape”, “form”, “buildings” and “architecture” were searched to decide the academic journals used for the article search.

Stage 3: A two-round article selection strategy was applied. In the first round, “Title and abstract” of the articles were checked. After that, unrelated articles were excluded. In the second round, “The whole article” was analyzed. 15 articles were selected and used in the study.

ANALYSIS ON SELECTED ARTICLES: Optimization algorithms

Year Author Algorithms Frameworks

NSGA-II Genetic Algorithm IRRGA HASA SIA Galapagos Octopus

2006 Wang et al.

2014 Quaglia et al.

2015 Yu et al.

2015 Echenagucia et al.

2016 Zhang et al.

2016 Song et al.

2016 Brown&Mueller

2016 Konis et al.

2017 Chen&Yang

2017 Camporeale et al.

2017 Zhang et al.

2018 Gou et al.

2018 Chen et al.

2018 Wu et al.

2019 Agirbas

NSGA-II: Non-dominated genetic algorithm| IRRGA: Implicit redundant genetic algorithm | HASA: Heuristic algorithm of simulated annealing | SIA: Swarm intelligence algorithm | Galapagos: Genetic algorithm

| Octopus: Based on SPEA-2 (Strength pareto evolutionary algorithm)

ANALYSIS ON SELECTED ARTICLES: Optimization objectives

14

7

3

21

Energy Performance Occupant Comfort Structural System Cost Environment

Heating, cooling, and lighting (Méndez Echenagucia et al. (2015),Chen&Yang (2017)) minimize heating and cooling demand,maximise Net Present Value (Camporeale et al. (2017) ) improve indoor thermal comfort,reduce building energy demand(Gou et al. (2018)) reduce energy consumption,increase thermal comfort (Yu et al. (2015)) life cycle energy, life cycle cost(LCC) (Wu et al. (2018)), life cycle environmental impact,LCC (Wang et al. (2006)) structural efficiency with embodied andoperational energy (Brown and Mueller, 2016) structural efficiency and energy efficiency (Quaglia, 2014) improve thermalcomfort (Yu et al. (2015), Zhang et al. (2017), Gou et al. (2018), Chen et al. (2018)) maximise direct sunlight (Zhang et al.,2016) optimize daylight (Agirbas, 2019) ventilation and daylighting( Konis et al. (2016) and Chen et al. (2018)

ANALYSIS ON SELECTED ARTICLES: Energy Simulation Programs

Energy Plus (Méndez Echenagucia et al.(2015), Chen&Yang (2017), Yu et al. (2015), Quaglia et al.(2014), Camporeale et al.(2017), Konis et al. (2016), Gou et al. (2018), Chen et al. (2018)) Ladybug (Zhang et al. (2016)) Design Builder and Matlab(Wu et al.(2018)) Diva plugin in Rhinoceros (Agirbas (2019)) Radiance (Zhang et al. (2017))

ANALYSIS ON SELECTED ARTICLES: Building Type

Majority of the studies used residential buildings in their study such asChen&Yang (2017), Yu et al. (2015), Song et al. (2016), Camporeale et al. (2017),Gou et al. (2018), Chen et al. (2018). In addition, office (Méndez Echenagucia etal.,2015, Wang et al.,2006), community centre (Zhang et al.,2016), military anddisaster relief housing ( Quaglia et al., 2014), and school building (Zhang etal.,2017) typologies are studied.

Moreover, Wu et al. (2018) made their study based on a solar decathlon house.Brown & Mueller (2016) studied three case study building with long span roofsand Agirbas (2019) studied a façade of row houses.

RESULTS

The findings of the analyses can be summarized as follow:

NSGA-II which is an instance of an evolutionary algorithm was the major optimization algorithm used in the studies.

While energy performance was the most dominant optimization objective, occupant comfort was the second most widely studied optimization objective. However, the number of studies which focus on occupant comfort was much less compare to energy performance.

Energy Plus was the most widely used energy simulation program in the analyzed journals.

Majority of the journals focused on residential building typologies in their study.

REFERENCES

Attia, S., Hamdy, M., O’Brien, W., & Carlucci, S. (2013). Assessing gaps and needs for integrating building performance optimization tools in net zero energy buildings design. Energy and Buildings, 60, 110–124. https://doi.org/10.1016/j.enbuild.2013.01.016

Evins, R. (2013). A review of computational optimisation methods applied to sustainable building design. Renewable and Sustainable Energy Reviews, 22, 230–245. https://doi.org/10.1016/j.rser.2013.02.004

Karmellos, M., Kiprakis, A., Mavrotas, G. (2015).A multi objective approach for optimal prioritization of energy efficiency measures in buildings: Model, software and case studies. Applied Energy, 139, 131-150

Lin, S. E. (2014). Designing-in performance : energy simulation feedback for early stage design decision making a dissertation presented to the faculty of the usc graduate school. (Doctoral Disseration). Retrieved from Proquest Database. (Order No. 3628225).

Shi X., Tian Z., Chen W., Si B., Jin X. (2016).A review on building energy efficient design optimization from the perspective of architects. Renewable and Sustainable Energy Reviews, 65, 872-884.

Si, B., Tian, Z., Jin, X., Zhou, X., Tang, P., Shi, X. (2016).Performance indices and evaluation of algorithms in building energy efficient design optimization. Energy, 114,100-112

Tian, Z., Chen, W., Tang, P., Wang, J., Shi, X. (2015). Building energy optimization tools and their applicability in architectural conceptual design stage. Energy Procedia, 78, 25722577.

Touloupaki, E., & Theodosiou, T. (2017). Optimization of Building form to Minimize Energy Consumption through Parametric Modelling. ProcediaEnvironmental Sciences, 38, 509–514. https://doi.org/10.1016/j.proenv.2017.03.114

yilmaz.derya@metu.edu.tr

PhD Candidate Derya YILMAZ