Probabilistic electric load forecasting: A tutorial review

被引:737
作者
Hong, Tao [1 ,2 ,3 ,4 ]
Fan, Shu [5 ]
机构
[1] Univ N Carolina, BigDEAL Big Data Energy Analyt Lab, Charlotte, NC 28223 USA
[2] Univ N Carolina, Energy Analyt, Charlotte, NC 28223 USA
[3] Univ N Carolina, Syst Engn & Engn Management, Charlotte, NC 28223 USA
[4] Univ N Carolina, Energy Prod & Infrastruct Ctr, Charlotte, NC 28223 USA
[5] Monash Univ, Clayton, Vic 3800, Australia
关键词
Short term load forecasting; Long term load forecasting; Probabilistic load forecasting; Regression analysis; Artificial neural networks; Forecast evaluation; OF-THE-ART; WIND POWER; PREDICTION INTERVALS; UNIT COMMITMENT; UNCERTAINTY; REGRESSION; DEMAND; GENERATION; MODEL; INTELLIGENCE;
D O I
10.1016/j.ijforecast.2015.11.011
中图分类号
F [经济];
学科分类号
02 ;
摘要
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. (C) 2015 International Institute of Forecasters. Published by Elsevier B.V. All rights reserved.
引用
收藏
页码:914 / 938
页数:25
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