Application of Neural Network and Support Vector Machines to Power System Short-term Load Forecasting

摘要：

Power system load was effected by many factors such as weather conditions, holidays, day types, so that the build of short-term load forecasting model is very important. The author analyzed the theory of support vector machine, studied the learning discipline of minimize the structural risk, solved the problem of insufficient training samples better. At the base of support vector machine, The author studied different kernel function and parameter, established the optimal kernel function and parameter, took network training with support vector machines algorithm, established network structure, built a support vector machine short-term load forecasting model; and applied this model to power systems short-term load forecasting. The forecasted results are compared with BP artificial neural network (ANN) methods. The result shows support vector machine short-term load forecasting model is more superiority.

关键词： Short-term load forecasting Support vector machine (SVM) Algorithms BP artificial neural network

作者: Du Xinhui Wang Liang Song Jiancheng Zhang Yan

作者单位: College of Electrical and Power Engineering Taiyuan University of Technology Taiyuan, China

会议类型: 国际会议

会议名称: International Conference on Computational Aspects of Social Networks(国际社会网络计算会议 CASoN 2010)

会议地点: 太原

会议语种:英文

页码: 729-732

在线出版日期: 2010-09-26（万方平台首次上网日期，不代表论文的发表时间）