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《Modern Electronics Technique》 2016-20
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Power network load forecasting based on improved BP neural network

ZHU Haibing;CUI Yu;XIONG Hao;Jiangsu Electric Power Company of State Grid;  
Since the forecasting accuracy of the traditional linear load forecasting method cannot meet the requirements of the modern power grid management system,the nonlinear BP neural network algorithm suitable for the power grid load prediction task is used in this paper to build forecasting model. Because the conventional BP neural network is easy to fall into the local optimal solution and has low convergence efficiency,simulated annealing algorithm is used in this paper to optimize the BP neural network weight training algorithm to improve the convergence efficiency and self-learning ability of the prediction model.The prediction model studied in this paper is analyzed with an example. The results show that the training times and training time of the improved BP neural network are less than those of the conventional neural network,and it has higher convergence accuracy,in addition,the prediction error of the improved BP neural network prediction model is obviously reduced.
【Fund】: 国家电网公司科技项目(SG11028)
【CateGory Index】: TM715;TP183
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