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

针对电能表需求预测问题,建立基于Shapley组合模型及神经网络的电能表合理优化分配模型,以提升需求预测精度。文章通过挖掘历史数据,采用Holt-Winters、BP神经网络和RBF神经网络模型对电能表需求分别进行预测、对比和分析,并且引入Shapley法对三类预测模型进行组合建模,求取相应模型的权重,获取最优的生产调度方案。仿真实验结果表明,RBF神经网络模型预测精度要高于BP神经网络和Holt-Winters模型。相较于单一模型,Shapley法组合模型具有更好的效果和实用性,有助于国家电网公司建立高效、科学的生产调度计划。

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.