电力需求的非线性回归组合神经网络预测研究

电力需求同时具有典型的增长性和季节波动性二重趋势，从而显示出复杂的非线性组合特征。为了提高电力需求的预测精度，提出一种新的预测模型——非线性回归组合神经网络模型。该模型有效兼顾了非线性回归分析和人工神经网络的优点，与其他预测模型进行了比较，该模型明显提高了电力需求预测的精度。仿真实验表明了该模型用于电力需求预测的可行性和有效性。同时，该模型也可以作为其他类似季节型时间序列预测建模的有效工具。

Study on power demand forecasting based on non-linear regression combined neural network

Power demand possesses dual property of increasement and seasonal fluctuation simultaneously,so it makes power demand variation possess complex non-linear combined character.To improve the forecasting accuracy of power demand,a new forecasting model which named non-linear combined neural network is put forward.The model can effectively use the advantages of non-linear regression analysis and artifical neural network and improve the forecasting accuracy of power demand obviously.The simulation result indicates that the model is effective and feasible for forecasting power demand.At the same time,the model is an effective tool to solve the problems of forecasting for other similar seasonal time series.