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Day-ahead hourly electricity load modeling by functional regression
Applied Energy
  • Yonghan Feng, Sears Holdings Corporation
  • Sarah M. Ryan, Iowa State University
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Publication Version
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Short-term load forecasting is important for power system generation planning and operation. For unit commitment and dispatch processes to incorporate uncertainty, a short-term load model must not only provide accurate load predictions but also enable the generation of reasonable probabilistic scenarios or uncertainty sets. This paper proposes a temporal and weather conditional epi-splines based load model (TWE) using functional approximation. TWE models the dependence of load on time and weather separately by functional approximation using epi-splines, conditional on season and area, in each segment of similar weather days. Load data are transformed from various day types to a specified reference day type among similar weather days in the same season and area, in order to enrich the data for capturing the non-weather dependent load pattern. In an instance derived from an Independent System Operator in the U.S., TWE not only provides accurate hourly load prediction and narrow bands of prediction errors, but also yields serial correlations among forecast hourly load values within a day that are similar to those of actual hourly load.

NOTICE: this is the author’s version of a work that was accepted for publication in Applied Energy. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Applied Energy, v.170, 15 May (2016): 455, doi: 10.1016/j.apenergy.2016.02.118.

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Elsevier Ltd.
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Citation Information
Yonghan Feng and Sarah M. Ryan. "Day-ahead hourly electricity load modeling by functional regression" Applied Energy Vol. 170 (2016) p. 455 - 465
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