Study on the Energy Demand Forecasting Based on Master Slave Neural Network
The development of economy,the progress of the society and the modern civilization is inseparable from the energy.Along with rapid development of economy and society,the energy demand grows continuously.Therefore energy demand forecasting has important theoretic and realistic significance.The BP neural network is usually applied to energy demand forecasting.But traditional BP neural network easily gets into part minimum,which leads to non convergence of algorithm and fail training.The master slave neural network (MSNN) is consisted with two Hopfield networks as master network and a BP network as slave network because of its good dynamic evolution performance.It can solve the problem well.Compared with BP neural network,MSNN has smaller system error and quicker asymptotic convergence rate.In This paper,energy demand in the last five years is predicted by the MSNN model.The result shows that MSNN not only has a more rapid convergence rate but also has smaller network system errors.It predicts ultimately energy demand well.Therefore,the MSNN can improve effect of energy demand forecasting better.
master slave neural network energy demand forecasting BP neural network Hopfield neural network
Nan Zhao
College of Buiness An Hui University He Fei,China
国际会议
重庆
英文
137-140
2016-03-20（万方平台首次上网日期，不代表论文的发表时间）