基于Shapley组合模型及神经网络的电能表需求预测研究

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...

Production scheduling of power meter based on Shapley method and neural networks

In terms of the optimization and distribution problem of power metering, a neural networks based power metering scheduling model is developed to improve the prediction accuracy. The production scheduling process is influenced by multi-factors including demand, purchasing, and calibration plans, etc. In this paper, by mining the historical data, Holt-Winters model, BP neural network model 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 based on two models with smallest performance measures, where weights of the corresponding models are calculated to obtain the optimal production scheduling scheme. Numerical simulation 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 grid companies to establish efficient and scientific production scheduling plan.