Machine-Learning-Based Electric Power Forecasting

Gang Chen (), Qingchang Hu, Jin Wang, Xu Wang and Yuyu Zhu
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Gang Chen: Lingnan College, Sun Yat-sen University, Guangzhou 510275, China
Qingchang Hu: Guangdong Electric Power Development Co., Ltd., Guangzhou 510630, China
Jin Wang: Guangdong Electric Power Development Co., Ltd., Guangzhou 510630, China
Xu Wang: Guangdong Electric Power Development Co., Ltd., Guangzhou 510630, China
Yuyu Zhu: Lingnan College, Sun Yat-sen University, Guangzhou 510275, China

Sustainability, 2023, vol. 15, issue 14, 1-21

Abstract: The regional demand for electric power is influenced by a variety of factors, such as fluctuations in business cycles, dynamic linkages among regional development, and climate change. The valid quantification of the impacts of these factors on the demand for electric power poses significant challenges. Existing methods often fall short of capturing the inherent complexities. This paper addresses these limitations by proposing a framework, which integrates machine-learning techniques into regional electricity demand forecasting. Regional electricity generation firms could then leverage the power of machine learning and improve the accuracy and robustness of electric power forecasting. In this paper, we conduct extensive numerical experiments using an actual dataset from a large utility firm and other public data sources. The analysis indicates that the support vector regression model (the SVR model) has high accuracy in predicting the demand. The results show that socio-economic development is the major driver of growth in electricity demand, while weather variability is a key contributor to the seasonal fluctuations in electricity use. Furthermore, linkages among regional development and the status of development of the green economy become increasingly important influencing factors. The proposed forecasting approach helps the regional electricity generation firms reduce a large amount of carbon dioxide emissions.

Keywords: electricity demand forecasting; influencing factors; machine learning; support vector regression; carbon emissions (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
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