考虑气象因素的PCA-BP神经网络短期负荷预测

Short-Term Load Forecasting Based on Principal Component Analysis-Back Propagation Neural Network Considering Meteorological Factor

为有效提高电力系统短期负荷预测精度及效率,提出一种基于主成分分析的BP神经网络短期负荷预测优化算法。利用主成分分析法将多个原始变量降维成少数彼此独立的变量作为输入,并根据各主成分的贡献率来确定网络的结构,有效解决BP网络预测精度与效率不高问题。在考虑气象因素的影响下通过对某地区历史负荷数据进行训练仿真,平均预测精度接近98%,预测程序运行效率提高两倍以上,仿真结果表明,该模型在效率和预测精度方面优于BP神经网络模型。

In order to effectively improve the accuracy and efficiency of short-term load forecasting,this paper proposed a back propagation(BP)neural network short-term load forecasting optimization algorithm based on the principal component analysis.The principal component analysis method was used to reduce a number of original variables into a few independent variables as input,and to determine the network structure according to the contribution rate of the main components,and effectively solve the problem of low prediction accuracy and efficiency of BP network.Taking the influence of meteorological factors into consideration,the results of training and simulation of historical load data in a certain area show that the average prediction accuracy is close to 98%,which is more than two times of the running efficiency of the forecast program.The simulation results show that the model is superior to the BP neural network model in efficiency and prediction accuracy.