Long term load forecasting accuracy in electric utility integrated resource planning

Carvallo, Juan Pablo; Larsen, Peter H.; Sanstad, Alan H.; Goldman, Charles A.

doi:10.1016/j.enpol.2018.04.060

Title: Long term load forecasting accuracy in electric utility integrated resource planning

Abstract

Forecasts of electricity consumption and peak demand over time horizons of one or two decades are a key element in electric utilities’ meeting their core objective and obligation to ensure reliable and affordable electricity supplies for their customers while complying with a range of energy and environmental regulations and policies. These forecasts are an important input to integrated resource planning (IRP) processes involving utilities, regulators, and other stake-holders. Despite their importance, however, there has been little analysis of long term utility load forecasting accuracy. We conduct a retrospective analysis of long term load forecasts on twelve Western U. S. electric utilities in the mid-2000s to find that most overestimated both energy consumption and peak demand growth. A key reason for this was the use of assumptions that led to an overestimation of economic growth. We find that the complexity of forecast methods and the accuracy of these forecasts are mildly correlated. In addition, sensitivity and risk analysis of load growth and its implications for capacity expansion were not well integrated with subsequent implementation. As a result, we review changes in the utilities load forecasting methods over the subsequent decade, and discuss the policy implications of long term load forecast inaccuracymore » and its underlying causes.« less

Authors:

Carvallo, Juan Pablo ^[1]; Larsen, Peter H. ^[1]; Sanstad, Alan H. ^[1]; Goldman, Charles A. ^[1]

Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)

Publication Date:: Wed May 23 00:00:00 EDT 2018

Research Org.:: Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)

Sponsoring Org.:: USDOE Office of Electricity (OE)

OSTI Identifier:: 1457015

Alternate Identifier(s):: OSTI ID: 1582992

Grant/Contract Number:: AC02-05CH11231

Resource Type:: Accepted Manuscript

Journal Name:: Energy Policy

Additional Journal Information:: Journal Volume: 119; Journal Issue: C; Related Information: © 2018 Elsevier Ltd; Journal ID: ISSN 0301-4215

Publisher:: Elsevier

Country of Publication:: United States

Language:: English

Subject:: 32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATION; 29 ENERGY PLANNING, POLICY, AND ECONOMY; Resource planning; Forecast accuracy; Load; Retrospective analysis; Resource expansion; Electric utility

Citation Formats


                    Carvallo, Juan Pablo, Larsen, Peter H., Sanstad, Alan H., and Goldman, Charles A. Long term load forecasting accuracy in electric utility integrated resource planning.  United States: N. p., 2018. 
Web.  doi:10.1016/j.enpol.2018.04.060.

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                    Carvallo, Juan Pablo, Larsen, Peter H., Sanstad, Alan H., & Goldman, Charles A. Long term load forecasting accuracy in electric utility integrated resource planning.  United States.  https://doi.org/10.1016/j.enpol.2018.04.060

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                    Carvallo, Juan Pablo, Larsen, Peter H., Sanstad, Alan H., and Goldman, Charles A. Wed .  
"Long term load forecasting accuracy in electric utility integrated resource planning".  United States.  https://doi.org/10.1016/j.enpol.2018.04.060.  https://www.osti.gov/servlets/purl/1457015.

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@article{osti_1457015,

  title        = {Long term load forecasting accuracy in electric utility integrated resource planning},

  author       = {Carvallo, Juan Pablo and Larsen, Peter H. and Sanstad, Alan H. and Goldman, Charles A.},

  abstractNote = {Forecasts of electricity consumption and peak demand over time horizons of one or two decades are a key element in electric utilities’ meeting their core objective and obligation to ensure reliable and affordable electricity supplies for their customers while complying with a range of energy and environmental regulations and policies. These forecasts are an important input to integrated resource planning (IRP) processes involving utilities, regulators, and other stake-holders. Despite their importance, however, there has been little analysis of long term utility load forecasting accuracy. We conduct a retrospective analysis of long term load forecasts on twelve Western U. S. electric utilities in the mid-2000s to find that most overestimated both energy consumption and peak demand growth. A key reason for this was the use of assumptions that led to an overestimation of economic growth. We find that the complexity of forecast methods and the accuracy of these forecasts are mildly correlated. In addition, sensitivity and risk analysis of load growth and its implications for capacity expansion were not well integrated with subsequent implementation. As a result, we review changes in the utilities load forecasting methods over the subsequent decade, and discuss the policy implications of long term load forecast inaccuracy and its underlying causes.},

  doi          = {10.1016/j.enpol.2018.04.060},

  journal      = {Energy Policy},

  number       = C,

  volume       = 119,

  place        = {United States},

  year         = {Wed May 23 00:00:00 EDT 2018},

  month        = {Wed May 23 00:00:00 EDT 2018}

}

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Journal Article:

Free Publicly Available Full Text

Accepted Manuscript (Publisher)

Accepted Manuscript (DOE)

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https://doi.org/10.1016/j.enpol.2018.04.060

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Cited by: 36 works

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Figures / Tables:

Fig. 1: Variables used for residential and commercial/industrial load forecasts, and model complexity. There is no information available for PGE in their 2007 plan. Blank spaces in the table indicate that the variable was not documented or formally employed in the forecast.

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Works referenced in this record:

A Partial Adjustment Model of U.S. Electricity Demand by Region, Season, and Sector
journal, January 2009

Paul, Anthony C.; Myers, Erica C.; Palmer, Karen L.
SSRN Electronic Journal
DOI: 10.2139/ssrn.1372228

Modeling an aggressive energy-efficiency scenario in long-range load forecasting for electric power transmission planning
journal, September 2014

Sanstad, Alan H.; McMenamin, Stuart; Sukenik, Andrew
Applied Energy, Vol. 128
DOI: 10.1016/j.apenergy.2014.04.096

Survey of Western U.S. electric utility resource plans
journal, March 2014

Wilkerson, Jordan; Larsen, Peter; Barbose, Galen
Energy Policy, Vol. 66
DOI: 10.1016/j.enpol.2013.11.029

Works referencing / citing this record:

Short-Term Electricity Load Forecasting Model Based on EMD-GRU with Feature Selection
journal, March 2019

Gao, Xin; Li, Xiaobing; Zhao, Bing
Energies, Vol. 12, Issue 6
DOI: 10.3390/en12061140

Figures / Tables found in this record:

Figures/Tables have been extracted from DOE-funded journal article accepted manuscripts.

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Similar Records

Title: Long term load forecasting accuracy in electric utility integrated resource planning

Abstract

Citation Formats

Figures / Tables:

A Partial Adjustment Model of U.S. Electricity Demand by Region, Season, and Sector journal, January 2009

Modeling an aggressive energy-efficiency scenario in long-range load forecasting for electric power transmission planning journal, September 2014

Survey of Western U.S. electric utility resource plans journal, March 2014

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journal, September 2014

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journal, March 2019