Improved RBF Neural Network for Short Term Load Forecasting

摘要：

According to the date of the type, temperature, weather conditions and other factors that affect the load forecasting, an improved radial basis function neural network is proposed for short-term load forecasting. Based on classical RBF neural network, combine the GA-optimized SVM radial basis function and RBF neural network. The Application of combined GA-optimized SVM radial basis function is used to extract useful data to improve the load forecasting accuracy of RBF neural network. Finally, spring load data of California were applied for simulation to illustrate the advantages of improved algorithm.

关键词： RBF neural network Short-term load forecasting Support Vector Machine Genetic Algorithm

作者: Pian Zhaoyu Li Shengzhu Zhang Nan

作者单位: School of Electrical Engineering and Information Technology Changchun Institute of Technology Changc Dispatching & Communication Institute Changchun Power Supply Company Changchun, China College of Electrical and Information Engineering Hunan University Changsha, China

会议类型: 国际会议

会议名称: 2010 International Conference on Circuit and Signal Processing(2010年电路与信号处理国际会议 ICCSP 2010)

会议地点: 上海

会议语种:英文

页码: 555-558

在线出版日期: 2010-12-25（万方平台首次上网日期，不代表论文的发表时间）