Medium and Long-Term Power Demand Forecasting Based on DE-GWO-SVR

doi:10.11930/j.issn.1004-9649.202105016

Abstract

Abstract: Power demand forecasting is an important prerequisite for the scientific planning and operation of power systems. According to the results of the correlation analysis, the key influencing factors of power demand are selected from eight aspects: economic development level, urbanization level, industrialization level, population size, industrial structure, household consumption level, electricity price and electricity base. Using differential evolution (DE) and grey wolf optimization (GWO) algorithms to optimize the parameters of the support vector regression (SVR), the power demand forecasting model is established. Based on the historical data of power demand in Beijing, this paper makes an empirical analysis, compares the prediction results of different models, verifies the effectiveness of the combined optimization model and the accuracy of prediction, forecasts the power demand of Beijing from 2021 to 2025.

Key words: power demand forecasting, differential evolution, grey wolf optimization algorithm, support vector machine

ZHANG Yunhou, LI Wanying, DONG Fugui. Medium and Long-Term Power Demand Forecasting Based on DE-GWO-SVR[J]. Electric Power, 2021, 54(9): 83-88.

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URL: https://www.electricpower.com.cn/EN/10.11930/j.issn.1004-9649.202105016

https://www.electricpower.com.cn/EN/Y2021/V54/I9/83

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