Probabilistic electric load forecasting: A tutorial review

Tao Hong, Shu Fan

Research output: Contribution to journalArticleResearchpeer-review

378 Citations (Scopus)

Abstract

Load forecasting has been a fundamental business problem since the inception of the electric power industry. Over the past 100 plus years, both research efforts and industry practices in this area have focused primarily on point load forecasting. In the most recent decade, though, the increased market competition, aging infrastructure and renewable integration requirements mean that probabilistic load forecasting has become more and more important to energy systems planning and operations. This paper offers a tutorial review
of probabilistic electric load forecasting, including notable techniques, methodologies and evaluation methods, and common misunderstandings. We also underline the need to invest in additional research, such as reproducible case studies, probabilistic load forecast evaluation and valuation, and a consideration of emerging technologies and energy policies in the probabilistic load forecasting process. © 2015 International Institute of Forecasters. Published by Elsevier B.V. All rights reserved.
Original languageEnglish
Pages (from-to)914-938
Number of pages25
JournalInternational Journal of Forecasting
Volume32
Issue number3
DOIs
Publication statusPublished - 2016

Keywords

  • Short term load forecasting
  • Long term load forecasting
  • Probabilistic load forecasting
  • Regression analysis
  • Artificial neural networks
  • Forecast evaluation

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