Electricity Demand Forecasting at ISO-NE: Review of Peak Load Models

prepared forStakeholder Meeting

Installed Capacity (“IC”) Methodology ReviewJuly 14th, 2006

Westborough, MA

byBenchmark Forecasts Consulting

Douglas R. Hale, drhale@gmail.comFrederick L. Joutz, fred.joutz@gmail.com

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Project Goal and Milestones

• Goals

- Provide an independent evaluation of ISO-NE’s energy and peak demand forecasting models

- Compare ISO-NE’s methodology with industry

- Recommendations for improving models and forecasts

• Milestones

– Briefing and preliminary report June 8th.

– Draft report and briefing late June / early July

– Final report early / mid July

• Long Run and Medium Term Forecasting Models

• Short Run and Peak Forecasting Models

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Preliminary Findings on ISO-NE’s Energy Models and Methodology

• The ISO-NE forecasting methodology• The short term energy model forecasts demand two years out• The long term energy model forecasts annual demand ten years out• We have examined the peak demand forecasts

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Replication of Seasonal Peak Models

• Peak Models for Resource System Planning 2006 (RSP06)– Winter– May– June– July– August– September

• Sample Weekdays January 1992 through August 2005

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Replication of Seasonal Peak Models• Winter Peak Model Specification is Standard and includes

– a Base Load trend– Heating Degree Day Measure – Monthly Dummy Variables– Monday and Friday Effects

• Separate Summer Peak Model Specification is Standard and includes– A Base Load Trend– 3-Day Weighted Temperature Humidity Index (SWTHI)– Heat Wave Variable– Monday and Friday Effects

• Peak(t) = a0 + a1 Bnel(t) + a2 SWTHI(t) + a3 HW(t) + a4 Days

• We were able to replicate the Seasonal Peak Models

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Replication of Summer Seasonal Peak Models

• The Separate Monthly Summer Peak Models are driven by – the SWTHI and – the Base Load Trend

• The SWTHI coefficient is larger .the warmer the month• The Base Load coefficient suggests about a 1.6 to 1.8 percent

increase per year all else equal.• The Heat Wave Variable is better at capturing daily loads rather

than peaks. It does not provide much in the way of explanatory power.

• The Peak and SWTHI series are not normally distributed. It may suggest estimating the model in natural logarithms.

• There is slight autocorrelation in the estimated equations. This may be a specification issue.

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Scatter Plot of July SWTHI and Peak Load

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0 200 400 600 800 100012001400

3-Day Weighted Temperature-Humidity Index at Time of Daily Peak

Peak

Net E

nerg

y Load

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Scatter Plot of August SWTHI and Peak Load

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0 200 400 600 800 100012001400

3-Day Weighted Temperature-Humidity Index at Time of Daily Peak

Peak

Net E

nerg

y Load

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July Peak is Skewed as Expected

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4

8

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15000 17500 20000 22500 25000 27500

Series: PEAKSample 7/01/1992 7/31/1992 7/01/1993 7/30/1993 7/05/1994 7/29/1994 7/05/1995 7/31/1995 7/01/1996 7/31/1996 7/01/1997 7/31/1997 7/01/1998 7/31/1998 7/01/1999 7/30/1999 7/05/2000 7/31/2000 7/02/2001 7/31/2001 7/01/2002 7/31/2002 7/01/2003 7/31/2003 7/01/2004 7/30/2004 7/05/2005 7/29/2005Observations 283

Mean 18747.92Median 18360.00Maximum 27191.00

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July SWTHI is Skewed as Expected

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8

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24

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200 400 600 800 1000 1200

Series: SWTHISample 7/01/1992 7/31/1992 7/01/1993 7/30/1993 7/05/1994 7/29/1994 7/05/1995 7/31/1995 7/01/1996 7/31/1996 7/01/1997 7/31/1997 7/01/1998 7/31/1998 7/01/1999 7/30/1999 7/05/2000 7/31/2000 7/02/2001 7/31/2001 7/01/2002 7/31/2002 7/01/2003 7/31/2003 7/01/2004 7/30/2004 7/05/2005 7/29/2005Observations 283

Mean 477.9820Median 434.2000Maximum 1282.400

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Alternative Seasonal Peak Models Considered

•Convert Variables (Peak and SWTHI) to Natural Logarithms before Estimation

•Control for First Order Serial Correlation

•Modify Specification to ADL Autoregressive Distributed Lag

The specification is the same as current except that lagged values of dependent variable and SWTHI are included.

•Peak(t) = a0 + a1 Peak(t-1) + a2 Bnel(t) + a3 SWTHI(t) + a4 SWTHI(t-1) + …

The ADL model in levels or natural logarithms demonstrates a significantincrease in explanatory power.

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Cooling Load Indexes

• Cooling Load Index (CLI)

• Base-Cooling Load Index (BCLI)

• Estimated from Daily Peak Models

• Peak Load(t) = b0 + b1 CDD(t)

• CLI(t) = b1(t) / b1(1992)

• BCLI(t) = b0(t) / b0(1992)

• Able to Replicate these models and the Normalizations with Trends

• Tested whether adjusting for July August vs. May September made a difference. None found.

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• Coefficient on WTHI a measure of peak load sensitivity to temperature/humidity– Indexed to 1992 value– Estimate a trend– Strong growth over historical period Doubling 1992-2005– Increasing penetration of Air Conditioning

• Constant a measure of peak load dependent on more general economic conditions– Indexed to 1992 value– Estimate a trend– Slower growth over historical period 16% increase 1992-2005

• Heating Index and Heating Base Load Index for heating season (Jan-Apr,Oct-Dec)

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1.0

1.2

1.4

1.6

1.8

2.0

92 93 94 95 96 97 98 99 00 01 02 03 04 05

CLICLIJJA

TCLITCLIJA

Comparison of Cooling Load Indexes Summer vs. June-August

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1.00

1.04

1.08

1.12

1.16

92 93 94 95 96 97 98 99 00 01 02 03 04 05

BCLITBCLI

BCLIJJATBCLIJJA

Comparison of Base Cooling Load Indexes Summer vs. June-August

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80 82 84 86 88 90 92 94 96 98 00 02 04

PKA PKWN

Annual Peak (Actual and Weather Normal)

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80 82 84 86 88 90 92 94 96 98 00 02 04

NELA/8.76 NELWN/8.76

Average Annual Hourly Load (Actual and Weather Normal)

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Load Factor Construction

Load Factor is Ratio of Annual “Adjusted” Hourly Load to Annual “Adjusted” Peak Loads

DSM adjusted load factor =

(Energy + DSM on Energy) / ( 8.76 * (Peak + DSM on Peak) )

The Change to DSM-adjusted Load Factor shows the impact over time of carrying forward the change in the short-run forecast and the load factor decrement

Long-run Peak = [ (Energy + DSM on Energy) / (DSM adjusted load factor + Change) / 8.76 ] - DSM on Peak

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.56

.58

.60

.62

.64

.66

.68

.70

.72

80 82 84 86 88 90 92 94 96 98 00 02 04

LFA LFWN

Load Factor (Actual and Weather Normal)

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Trend Load Factor vs. Constant

obs LFA_F LFWN_F LFA_LevLFWN_Trend

2000 0.645 0.622001 0.563 0.6052002 0.571 0.5892003 0.605 0.5842004 0.627 0.5842005 0.573 0.5772006 0.596 0.584 0.573   0.5772007 0.593 0.581   0.573   0.5772008 0.591 0.578   0.573   0.5772009 0.588 0.575   0.573   0.5772010 0.586 0.572   0.573   0.577

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Load Factor Construction and Forecast

Long-run reference summer peak forecast based on long-run energy forecast and a declining load factor.

Driven by increasing relative growth in Peak Demand

But part of this may be do to inclusion of DSM factors in calculation.

It is not clear that this offers a clear picture

The DSM factors or adjustments are not directly measured and taken as given by ISO-NE.

There can “political” or institutional factors driving these which the ISO-NE rightly chooses not to get involved in.

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Specification of the Estimated Models

• Diagnostic Testing of Estimated Models– Model Fit– Standard Error – Adj. R squared– Constant Variance– Correlated Errors– Model Stability– Elasticities (Price, Income, Weather)

• Time Series Properties of the Data– Short-run Dynamics– Long-run Relations– Integration / Cointegration

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Replication of the Forecasts

• Forecast Simulation

– Performed simple simulation conditioned on actual explanatory variables

– Long-run State Models are reasonably close

• Forecast Replication is close. Still need to compare notes with ISO-NE staff

• Forecasting Process and Theory

– Recent Developments in Econometric and Forecasting Techniques

– Archiving and Documentation Procedures

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Our Replication Experience

• Good news-we did it, all the data are extant, programs worked as advertised, etc.

• Not so good news-couldn’t have done it without lots of help• Learned a lot about models that was not obvious from reported

statistics

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Recommendations• Improve Documentation• Data and Model Archiving• Seasonal Peak Models (Specification Issues)

– Choice of Included Variables– Levels or Natural Logarithms– ADL Model Dynamics (Serial Correlation Correction)

• Cooling Load Indexes• Load Factor

– DSM Issue– Choice of Projection (Level, Continued Trend, and Smoothing)

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Recommendations• Switch from Current Annual Aggregate ISO-NE Model to a Quarterly

or Monthly Model.• Level of Detail

– Total Load – avoid data problems in the data– Sectoral - advantage might be a gain for peak use by residential

and commercial sectors

• Consider the MIT Center for Energy Policy Research Center for Evaluating Actual Decision Making process regarding Capacity Expansion– New Director specializes in the techniques– Real Options approach

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Are the Energy Forecasting Models Clearly Described in the Documentation?

• The equations for the short and long term models are exhibited• Data sources are identified and some data series are included• The estimation results, some diagnostics and some forecast errors

are reported• The general approach to merging the short and long term models is

described

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Assessment of ISO-NE Energy Model Documentation

• In our experience ISO-NE has done more than most forecasters to document their methods and make them accessible

• The content (models, data, estimation, error experience, etc.) is good

• Certain transformations to splice the short and long term forecasts are not fully explained

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Replication: Why is Replication Important?

• Important component of scientific process.• Provide confidence in methodology.• Serve as double-check on models and data.• Starting point for further analysis and diagnostic tests.• Verification of Documentation and Archives

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Replication: Why is Replication Important?

• Examination of the Historical Data• Specification of the Estimated Models• Economic Theory• Statistical Theory • Time Series Properties of the Data• Diagnostic Testing of Estimated Models• Forecast Simulation• Forecasting Process and Theory