USING WAVELET TRANSFORM TO IMPROVE GENERALIZATION ABILITY OF NEURAL NETWORK IN NEXT DAY LOAD CURVE FORECASTING

摘要：

The net day load curve forecasting plays an important role for electric power system operation. Because of affecting by many factors, daily curve is composed by many regular wave trends and stochastic ones. This makes the poor efficiency and generalization capacity of neural network adopted in forecasting. By using discrete wavelet transform, the complicated load curve could be extracted to many simplex ones. After abnegating stochastic series, other extracting results are simulated by radial basis function (RBF) neural networks. Adding the forecasting results of neural network together, it will get the forecasting load. The tests show that the models brought forward in this paper is feasible.

关键词： Load Forecasting Discrete Wavelet Transform Trend Eztraction Neural Network

作者: CHUN-XIANG LI DONG-XIAO NIU MING MENG

作者单位: Information and Network Management Center, North China Electric Power University, Baoding, Hebei 071 Department of Economics & Management, North China Electric Power University, Baoding, Hebei 071003,

会议类型: 国际会议

会议名称: 2008 International Conference on Machine Learning and Cybernetics(2008机器学习与控制论国际会议)

会议地点: 昆明

会议语种:英文

页码: 1526-1531

在线出版日期: 2008-07-12（万方平台首次上网日期，不代表论文的发表时间）