李翀,申洪涛,刘建华,吴一敌,孙晓腾,张英.基于Shapley组合模型及神经网络的电能表需求预测研究[J].电测与仪表,2021,58(9):187-193. Li Chong,Shen Hongtao,Liu Jianhua,Wu Yidi,Sun Xiaoteng,Zhang Ying.Demand prediction of electricity meter based on Shapley combined model and neural networks[J].Electrical Measurement & Instrumentation,2021,58(9):187-193.
基于Shapley组合模型及神经网络的电能表需求预测研究
Demand prediction of electricity meter based on Shapley combined model and neural networks
In terms of the demand prediction problem of electricity meter, based on Shapley combination model and neural networks, this paper establishes a reasonable and optimal distribution model of electricity meters to improve the accuracy of demand prediction. In this paper, by mining the historical data, Holt-Winters model, BP neural network and RBF neural network model are used to predict, compare, and analyze the demand of electricity meters. In addition, Shapley method is adopted to obtain a combined model for three prediction models, where weights of the corresponding models are calculated to obtain the optimal production scheduling scheme. Numerical simulation results indicate that the prediction accuracy of RBF neural network model is higher than those of BP neural network and Holt-Winters model. Moreover, in contrast to the single-model based method, Shapley combined model is more effective and more practical, which can be used for power grid companies to establish efficient and scientific production scheduling plan.
