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Day-ahead hourly electricity load modeling by functional regression

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  • Feng, Yonghan
  • Ryan, Sarah M.

Abstract

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.

Suggested Citation

  • Feng, Yonghan & Ryan, Sarah M., 2016. "Day-ahead hourly electricity load modeling by functional regression," Applied Energy, Elsevier, vol. 170(C), pages 455-465.
    • Handle: RePEc:eee:appene:v:170:y:2016:i:c:p:455-465
    DOI: 10.1016/j.apenergy.2016.02.118
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    6. Vogel, E.E. & Saravia, G. & Kobe, S. & Schumann, R. & Schuster, R., 2018. "A novel method to optimize electricity generation from wind energy," Renewable Energy, Elsevier, vol. 126(C), pages 724-735.
    7. Lebotsa, Moshoko Emily & Sigauke, Caston & Bere, Alphonce & Fildes, Robert & Boylan, John E., 2018. "Short term electricity demand forecasting using partially linear additive quantile regression with an application to the unit commitment problem," Applied Energy, Elsevier, vol. 222(C), pages 104-118.
    8. Xu, Xiuqin & Chen, Ying & Goude, Yannig & Yao, Qiwei, 2021. "Day-ahead probabilistic forecasting for French half-hourly electricity loads and quantiles for curve-to-curve regression," Applied Energy, Elsevier, vol. 301(C).
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    11. Lin Lin & Lin Xue & Zhiqiang Hu & Nantian Huang, 2018. "Modular Predictor for Day-Ahead Load Forecasting and Feature Selection for Different Hours," Energies, MDPI, vol. 11(7), pages 1-30, July.

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