Since 2012, Shu Fan and I have been trying to put together a review paper on load forecasting. We first started with a review on point load forecasting. Then both of us felt that our community may need a review paper on probabilistic load forecasting more than anything else, so we shifted the direction. The first submitted version was released at the beginning of GEFCom2014 for the contestants to have some basic idea about probabilistic load forecasting (see 10 Recommended Papers for GEFCom2014 Contestants). After going through two revisions, the paper went from 32 pages to 53 pages at its third submission. Many thanks to the valuable comments from Rob Hyndman, Pierre Pinson, Rafal Weron, and two anonymous reviewers, the quality of this review has gone up significantly. Today I'm very pleased to announce that this review paper on probabilistic load forecasting was just accepted by International Journal of Forecasting. We hope this paper is useful to the researchers and practitioners who are in the load forecasting community or interested in load forecasting.
Tao Hong and Shu Fan, "Probabilistic electric load forecasting: a tutorial review", International Journal of Forecasting, accepted. Working paper available online http://www.drhongtao.com/articles
Probabilistic Electric Load Forecasting: A Tutorial Review
Tao Hong and Shu Fan
Load forecasting is a fundamental business problem established since the inception of the electric power industry. Over the past 100 plus years, both research efforts and industry practices in this area are primarily focused on point load forecasting. In the recent decade, due to the increased market competition, aging infrastructure and renewable integration requirements, probabilistic load forecasting is becoming 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, 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 consideration of emerging technologies and energy policies in probabilistic load forecasting process.
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