Abram Gross

Yafeng Peng

Jedidiah Shirey

Contents Context

Problem Statement

Method of Analysis

Forecasting Model

Way Forward

Earned Value

NOVEC Background (1 of 2) Northern Virginia Electric Cooperative (NOVEC) is a

distributor of energy to 6 counties in Northern Virginia.

Nearly 150,000 customers.

Mandated to meet all energy requests in pre-specified zones of obligation.

Primary means to service communities is through bulk energy market purchases.

NOVEC Background (2 of 2) Energy Purchases:

1) Bulk purchases via contracts 1 month to 3 years prior to delivery.

2) Spot purchases as needed to meet peak demand up to one day prior to delivery.

Problem Statement Warming trends have caused NOVEC to question

whether the current weather-normalization methodology is still the best available model.

NOVEC needs a new weather-normalization method that accounts for changing weather trends or a recommendation that the existing model is sufficient.

To what extent is the climate and its impact on energy demands changing?

Purpose & Objectives Purpose:

NOVEC needs to remove weather-effects from energy consumption to more accurately inform bulk purchases.

Explore and recommend a methodology to normalize monthly energy purchases.

Objectives: Characterize the relationship between economic

variables, weather, customer-base, and energy consumption.

Develop a normalization procedure to remove weather effects from energy demand.

Recommendation for improvement to current weather-normalization methodology.

Scope Data:

NOVEC monthly energy purchases data since 1983. Dulles weather data since 1963. Historic economic factors data since 1980s. 30 years of forecasted economic factors.

Model: Parameters: historic energy purchases, weather data, economics, and

customer-base. Predictions for energy consumption over a 30-year horizon. Regression: characterize dynamics between parameters. Weather-normalization: remove seasonal weather impacts on

NOVEC’s load. Forecast: facilitate testing of varied normalization methodologies. Ensure synergy with NOVEC’s existing suite of models

(regression, weather-normalization, forecast).

Key Definitions CDD: Cooling Degree-Day = MAX(ActualDegree – UpperBound, 0) * Duration.

HDD: Heating Degree-Day = MAX(LowerBound – ActualDegree, 0) * Duration.

Neutral zone: Upper and lower bounds for temperatures that do not impact load.

* Analysis will explore varied upper/lower bounds for temperature

30

40

50

60

70

80

Actual_Temperature Upper_Bound Lower_Bound

CDD

HDD30

40

50

60

70

80

Actual_Temperature Upper_Bound Lower_Bound

CDD

HDD

t

Weather Data Visualization (1 of 2) Historic weather data was

analyzed by average temperature per month to view trends.

Average temperature trending upwards from 1960 to the present for each month (graph is for July).

Weather Data Visualization (2 of 2) Variance was also evaluated and no change is seen.

Essential Elements of Analysis & Measures of Effectiveness

EEA 1: What is the rate of climate change? MoE 1.1: Seasonal trends and variability.

MoE 1.2: Rate of climate change.

EEA2: What econ-model best predicts NOVEC’s customer-base? MOE 2.1: Best subset of econometrics to gauge service demand.

MOE 2.2: Goodness of fit test for selected model.

EEA3: What impact does weather have on energy demand? MOE 3.1: Relationship between weather and load beyond base demand.

MOE 3.2: Changes in climate compared to per capita consumption.

MOE3.3: Goodness of fit test for selected model.

III. Forecast ModelII. Develop normalization methodologyI. Relate model parameters

Methodology

Residential

Commercial

CompositeCustomer-Base

GDP

Housing Stocks

Employment

Historic Power Demand

Monthly Demand

HDD Impact

CDD Impact

Remaining Power Demand

Weather-Normalization

Forecast

Assess Model

Basic Load

Economic Variables

Customer Combination

Per CapitaDemand

* Model forecast will be verified against 2012-2013 power demand and compared to existing model’s accuracy.

*

EEA1: Rate of Climate Change Historic weather data was analyzed to determine trends:

Seasonal fluctuations.

Overall rate of change.

Characterize uncertainty/variability.

0

200

400

600

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1000

1200

1400

1600

0

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6000

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63

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01

20

03

20

05

20

07

20

09

20

11

De

gre

e-D

ays

Be

low

65

°F

Heating Degree-Days - 1963 to 2011

Total HDD

Average HDD

Max HDD

0

200

400

600

800

1000

1200

1400

1600

0

1000

2000

3000

4000

5000

6000

19

63

19

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67

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71

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79

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81

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01

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03

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20

07

20

09

20

11

De

gre

e-D

ays

Ab

ove

6

5 °F

Cooling Degree-Days - 1963 to 2011

Total CDD

Average CDD

Max CDD

EEA2: Economics Model Model to associate economic factors to customer-base

in under development.

0

20

40

60

80

100

120

140

0

10

20

30

40

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60

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80

Aug-89 Aug-90 Aug-91 Aug-92 Aug-93 Aug-94

De

man

d (

Mill

ion

s -

kWH

)

Cu

sto

me

rs &

Ho

usr

ing

Sto

cks

(Th

ou

san

ds)

Residential Demand & Metro-D.C. Econometrics

Housing Stocks

Customers

Demand

EEA3: Impact of Weather Changes to per capita energy demand by customer

type: residential and non-residential.

0

2000

4000

6000

8000

10000

12000

14000

16000

Jan-05 Jan-06 Jan-07 Jan-08 Jan-09 Jan-10

Ener

gy C

onsu

mpt

ion

(kW

hr)

Monthly Customer per Capita Demand (2005 - 2010)

Non-Residential

Residential

HDD

CDD

Model Development Pre-processing: Excel Workbook with VBA Macros.

Automated computations of HDD and CDD.

Allows user-defined “neutral” boundaries for calculating.

Ingests data as currently maintained by sponsor.

Automated linear transformations for regression models.

GUI design facilitates some simple regression models germane to VBA; limited capability.

Launch and export conditioned data to R; expanded capability.

Statistical Modeling in R: Executable file for regression analysis and improved visualization.

Preliminary Results Variability in yearly weather patters doesn’t appear to change.

Climate change is more apparent in winter and summer than fall and spring.0

500

1000

1500

2000

2500

3000

3500

1963

1966

1969

1972

1975

1978

1981

1984

1987

1990

1993

1996

1999

2002

2005

2008

2011

Degr

ee-D

ays B

elow

65

°F

Winter Heating Degree Days

DEC

JAN

FEB

0

200

400

600

800

1000

1200

1400

1600

1963

1966

1969

1972

1975

1978

1981

1984

1987

1990

1993

1996

1999

2002

2005

2008

2011

Degr

ee-D

ays B

elow

65

°F

Spring Heating Degree Days

MAR

APR

MAY

0

20

40

60

80

100

120

140

160

1963

1966

1969

1972

1975

1978

1981

1984

1987

1990

1993

1996

1999

2002

2005

2008

2011

Degr

ee-D

ays B

elow

65

°F

Summer Heating Degree Days

JUN

JUL

AUG

0

200

400

600

800

1000

1200

1400

1600

1963

1966

1969

1972

1975

1978

1981

1984

1987

1990

1993

1996

1999

2002

2005

2008

2011

Degr

ee-D

ays B

elow

65

°F

Fall Heating Degree Days

SEP

OCT

NOV

0

2

4

6

8

10

12

14

1963

1966

1969

1972

1975

1978

1981

1984

1987

1990

1993

1996

1999

2002

2005

2008

2011

Degr

ee-D

ays E

xcee

ding

65

°F

Winter Cooling Degree Days

DEC

JAN

FEB

0

50

100

150

200

250

300

350

1963

1966

1969

1972

1975

1978

1981

1984

1987

1990

1993

1996

1999

2002

2005

2008

2011

Degr

ee-D

ays E

xcee

ding

65

°F

Spring Cooling Degree Days

MAR

APR

MAY

0

200

400

600

800

1000

1200

1400

1963

1966

1969

1972

1975

1978

1981

1984

1987

1990

1993

1996

1999

2002

2005

2008

2011

Degr

ee-D

ays E

xcee

ding

65

°FSummer Cooling Degree Days

JUN

JUL

AUG

0

50

100

150

200

250

300

350

400

450

1963

1966

1969

1972

1975

1978

1981

1984

1987

1990

1993

1996

1999

2002

2005

2008

2011

Degr

ee-D

ays E

xcee

ding

65

°F

Fall Cooling Degree Days

SEP

OCT

NOV

0

500

1000

1500

2000

2500

3000

3500

1963

1966

1969

1972

1975

1978

1981

1984

1987

1990

1993

1996

1999

2002

2005

2008

2011

Degr

ee-D

ays B

elow

65

°F

Winter Heating Degree Days

DEC

JAN

FEB

0

200

400

600

800

1000

1200

1400

1600

1963

1966

1969

1972

1975

1978

1981

1984

1987

1990

1993

1996

1999

2002

2005

2008

2011

Degr

ee-D

ays B

elow

65

°F

Spring Heating Degree Days

MAR

APR

MAY

0

20

40

60

80

100

120

140

160

1963

1966

1969

1972

1975

1978

1981

1984

1987

1990

1993

1996

1999

2002

2005

2008

2011

Degr

ee-D

ays B

elow

65

°F

Summer Heating Degree Days

JUN

JUL

AUG

0

200

400

600

800

1000

1200

1400

1600

1963

1966

1969

1972

1975

1978

1981

1984

1987

1990

1993

1996

1999

2002

2005

2008

2011

Degr

ee-D

ays B

elow

65

°F

Fall Heating Degree Days

SEP

OCT

NOV

0

2

4

6

8

10

12

14

1963

1966

1969

1972

1975

1978

1981

1984

1987

1990

1993

1996

1999

2002

2005

2008

2011

Degr

ee-D

ays E

xcee

ding

65

°F

Winter Cooling Degree Days

DEC

JAN

FEB

0

50

100

150

200

250

300

350

1963

1966

1969

1972

1975

1978

1981

1984

1987

1990

1993

1996

1999

2002

2005

2008

2011

Degr

ee-D

ays E

xcee

ding

65

°F

Spring Cooling Degree Days

MAR

APR

MAY

0

200

400

600

800

1000

1200

1400

1963

1966

1969

1972

1975

1978

1981

1984

1987

1990

1993

1996

1999

2002

2005

2008

2011

Degr

ee-D

ays E

xcee

ding

65

°FSummer Cooling Degree Days

JUN

JUL

AUG

0

50

100

150

200

250

300

350

400

450

1963

1966

1969

1972

1975

1978

1981

1984

1987

1990

1993

1996

1999

2002

2005

2008

2011

Degr

ee-D

ays E

xcee

ding

65

°F

Fall Cooling Degree Days

SEP

OCT

NOV

Way Forward & Risks Data visualization and preliminary analysis completed.

Website design completed. Still need to finalize content.

Model design/creation under construction. Major focus over next couple of weeks.

Excel calling R needs to be completed.

Customer transformation

Final model fitting

Forecast

Earned Value

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rs

Earned Value

Earned Value

Planned Value

Actual Cost

CPI & SPI

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Week 1 Week 2 Week 3 Week 4 Week 5 Week 6 Week 7 Week 8

Ra

tio

CPI and SPI

CPI

SPI

References www.novec.com

NOVEC 25th Anniversary History Film http://www.youtube.com/watch?v=2qfeOKnPPGg

“Improving Load Management Control for NOVEC”; Kozera, Lohr, McInerney, and Pane. http://seor.gmu.edu/projects/SEOR-Spring12/NOVECLoadManagement/team.html