Analysis of Power Material Demand Prediction Method Based on Improved BPNN Algorithm
Power Material Demand Prediction is an important technology in the application and management of power enterprises,but the current prediction level is relatively low.Not only MAPE is relatively large,but also the confidence level of the prediction results is relatively low,which cannot achieve the expected prediction effect.Therefore,a Power Material Demand Prediction method based on the improved BPNN algorithm is proposed.To ensure the accuracy of power material demand prediction,the power materials are firstly divided into technical ma-terials,maintenance materials,scientific and technological materials,infrastructure materials and information materials according to the performance of the project.Then,according to the division of materials category,historical elec-tricity material information is collected from the electricity material information system or platform,and the data is generalized and normalized.The improved BPNN algorithm is used to train data on power material demand,extract power material demand characteristics,and quantify the power material demand,so as to realize the power material demand prediction based on the improved BPNN algorithm.The experiment has proved that the design method MAPE is not more than 1%,and the confidence level of the prediction results is not less than 95%,which has a good application prospect in power materials demand prediction.
Improved BPNN algorithmPower material demandPower material information systemNormaliza-tionDemand characteristicsQuantification
NETL
NSTL
万方数据
