基于灰色理论和BP神经网络串联组合的能源需求预测

本文对某省能源现状进行了分析，进而提出了能源需求预测的必要性，在利用统计数据分析某省能源消耗趋势后，利用灰色GH（1，1）模型进行预测，然后把预测结果和影响因素同时作为BP神经网络的输入进行预测，结果表明，基于GH—BP串联组合模型的预测精度要高于单纯地GH（1，1）预测。最后分析了某省新能源的发展优势。

Energy Demand Forecasting Based on the Series Combination of Gray Theory and BP Neural Network

The energy situation in a province was analyzed in this article,and proposing the need for energy de-mand forecasting.After analyzing energy consump-tion trends in a province according to the statistical data, and to predict the energy demand base on gray GM （1,1）,then we use the predicting results and impact factors at the same time as the input of BP neural network to predict ,the results show that the prediction accuracy of GM-BP series model combi- nation is higher than GM （1,1） model.Finally,we analyze the development superiority of new energy in a province.