Affected by the pandemic coronavirus-19(COVID-19),significant changes have taken place in all aspects of social production and residents’lives,as well as in the energy supply and consumption characteristics of the po...Affected by the pandemic coronavirus-19(COVID-19),significant changes have taken place in all aspects of social production and residents’lives,as well as in the energy supply and consumption characteristics of the power system.COVID-19 has brought unpredictable uncertainties to the power grid.These changes and uncertainties pose a challenge to conventional electric load forecasting.Therefore,aiming to load forecasting under the background of the pandemic,this paper proposes a power load segmented forecasting method based on the pandemic stage division method,attention mechanism,and bi-directional long and short-term memory artificial neural network quantile regression model(ESD-ABiLSTMQR).According to the development degree of the pandemic,considering characteristics of different development stages of the pandemic,the pandemic is divided into four stages by using the analytic hierarchy process method(AHP):initial stage,outbreak stage,control stage,and recovery stage.A segmented load forecasting model based on LSTM and attention mechanism is established to forecast load in different time series.Cases used data from the pandemic in Wuhan,China,for verification.Results show the segmented forecasting method can analyze load characteristics of each stage and can effectively improve the accuracy of load forecasting.展开更多
基金supported by National Natural Science Foundation of China(No.52407074)Anhui Provincial Natural Science Foundation Youth Project,China(2308085QE177)Key Research Projects in Natural Sciences of Universities funded by the Department of Education in Anhui Province,China(2023AH050092).
文摘Affected by the pandemic coronavirus-19(COVID-19),significant changes have taken place in all aspects of social production and residents’lives,as well as in the energy supply and consumption characteristics of the power system.COVID-19 has brought unpredictable uncertainties to the power grid.These changes and uncertainties pose a challenge to conventional electric load forecasting.Therefore,aiming to load forecasting under the background of the pandemic,this paper proposes a power load segmented forecasting method based on the pandemic stage division method,attention mechanism,and bi-directional long and short-term memory artificial neural network quantile regression model(ESD-ABiLSTMQR).According to the development degree of the pandemic,considering characteristics of different development stages of the pandemic,the pandemic is divided into four stages by using the analytic hierarchy process method(AHP):initial stage,outbreak stage,control stage,and recovery stage.A segmented load forecasting model based on LSTM and attention mechanism is established to forecast load in different time series.Cases used data from the pandemic in Wuhan,China,for verification.Results show the segmented forecasting method can analyze load characteristics of each stage and can effectively improve the accuracy of load forecasting.
