Probabilistic Load Prediction with Risk-Severity Score

Happy Aprillia, Hong Tzer Yang, Chao Ming Huang

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Emerging interest in probabilistic load forecasting (PLF) has increased because it can address load uncertainty better than point forecasting. This paper proposed a new strategy to treat the input of PLF as a probability map, which enables an actual pictorial to depict the prediction outcome risk of a load demand profile according to the preferred length of the prediction horizon. The proposed probabilistic method consists of optimal signal decomposition, prediction model construction and an evaluation strategy to obtain the prediction interval risk outcome. Whale optimization algorithm-discrete wavelet transformation (WOA-DWT) is employed to detect optimal input decomposition. The quantile regression random forest (QRRF) is utilized to build a prediction interval from various quantiles. A novel scheme to calculate the prediction outcome risk due to current input, which provides a degree of confidence, is also presented. The efficacy of prediction interval is verified using the Independent System Operator - New England (ISO-NE) set with the Winkler score and proposed risk-severity score.

Original languageEnglish
Title of host publicationProceedings - IECON 2020
Subtitle of host publication46th Annual Conference of the IEEE Industrial Electronics Society
PublisherIEEE Computer Society
Number of pages6
ISBN (Electronic)9781728154145
Publication statusPublished - 2020 Oct 18
Event46th Annual Conference of the IEEE Industrial Electronics Society, IECON 2020 - Virtual, Singapore, Singapore
Duration: 2020 Oct 192020 Oct 21

Publication series

NameIECON Proceedings (Industrial Electronics Conference)


Conference46th Annual Conference of the IEEE Industrial Electronics Society, IECON 2020
CityVirtual, Singapore

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Electrical and Electronic Engineering


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