Probabilistic electric load forecasting: A tutorial review

Tao Hong and Shu Fan

International Journal of Forecasting, 2016, vol. 32, issue 3, 914-938

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.

Keywords: Short term load forecasting; Long term load forecasting; Probabilistic load forecasting; Regression analysis; Artificial neural networks; Forecast evaluation (search for similar items in EconPapers)
Date: 2016
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Citations: View citations in EconPapers (198)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:intfor:v:32:y:2016:i:3:p:914-938

DOI: 10.1016/j.ijforecast.2015.11.011

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