The accuracy of short-term electricity sales forecasting in the power market is of great signifi-cance for optimizing the electricity consumption structure and improving power supply reliability.Traditional short-term electricity sales forecasting methods do not consider the impact of deviation in electricity consump-tion assessment and differences in electricity consumption behavior,resulting in low electricity prediction ac-curacy.A convolutional neural network-based short-term electricity sales forecasting method in the power market is proposed,which first classifies users based on their electricity load rates,Obtain the electricity consumption characteristics and demand patterns of different industries,and then consider the impact of posi-tive and negative deviations in electricity consumption.Design a short-term electricity sales prediction method based on CNN ResNet.Experimental analysis shows that this method can effectively improve the accuracy of electricity sales prediction under multiple factors.
维普
万方数据