APPLICATION OF SVM BASED ON IMMUNE GENETIC FUZZY CLUSTERING ALGORITHM TO SHORT-TERM LOAD FORECASTING

摘要：

Support vector machine (SVM) has been applied to load forecasting field widely. However, if the training data has much noise and redundancy, the generalized performance of SVM will be weakened, so this can cause some disadvantages of slow convergence speed and low forecasting accuracy. A SVM forecasting model based on immune genetic fuzzy clustering algorithm (1GA-SVM) is presented, using immune genetic fuzzy clustering algorithm to preprocess historical load data, and then extract training samples from clustered data, and the result is that both processing speed and forecasting accuracy are improved. At last, apply this model to short-term load forecasting, and it shows more generalized performance and better forecasting accuracy compared with the methods of single SVM and BP neural networks.

关键词： Load forecasting Immune genetic algorithm Fuzzy clustering Support vector machines

作者: YUAN-SHENG HUANG JIA-JIA DENG YUN-YUN ZHANG

作者单位: Department of Economics & Management, North China Electric Power University, Baoding, 071003, China

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: 2646-2650

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）