Electrical peak demand forecasting- A review

• URL: http://arxiv.org/abs/2108.01393v1
• Date: Tue, 3 Aug 2021 10:04:33 GMT
• Title: Electrical peak demand forecasting- A review
• Authors: Shuang Dai, Fanlin Meng, Hongsheng Dai, Qian Wang and Xizhong Chen
• Abstract summary: This paper provides a timely and comprehensive overview of peak load demand forecast methods in the literature. In this paper we first give a precise and unified problem definition of peak load demand forecast. Second, 139 papers on peak load forecast methods were systematically reviewed where methods were classified into different stages based on the timeline. Third, a comparative analysis of peak load forecast methods are summarized and different optimizing methods to improve the forecast performance are discussed.
• Score: 7.5337868245858255
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The power system is undergoing rapid evolution with the roll-out of advanced metering infrastructure and local energy applications (e.g. electric vehicles) as well as the increasing penetration of intermittent renewable energy at both transmission and distribution level, which characterizes the peak load demand with stronger randomness and less predictability and therefore poses a threat to the power grid security. Since storing large quantities of electricity to satisfy load demand is neither economically nor environmentally friendly, effective peak demand management strategies and reliable peak load forecast methods become essential for optimizing the power system operations. To this end, this paper provides a timely and comprehensive overview of peak load demand forecast methods in the literature. To our best knowledge, this is the first comprehensive review on such topic. In this paper we first give a precise and unified problem definition of peak load demand forecast. Second, 139 papers on peak load forecast methods were systematically reviewed where methods were classified into different stages based on the timeline. Thirdly, a comparative analysis of peak load forecast methods are summarized and different optimizing methods to improve the forecast performance are discussed. The paper ends with a comprehensive summary of the reviewed papers and a discussion of potential future research directions.

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