Short-term Load Forecasting: Learning in the Feature Space Based on Local Temperature Sensitive Information

摘要：

A novel hybrid method based on feature extraction and neural network for short-term load forecasting was presented. It is well known that temperature information is very important for load forecasting, but the local structure of temperature sensitive information is not adopted in the literature. The proposed model adopts an integrated architecture to handle the local temperature sensitive information. Firstly, the input load data set is clustered into several temperature similar days subsets by the k-means algorithm in an unsupervised manner, Then compute max temperature factor in each subsets and split the time point (5 minutes, 288/day) into several time range, in each time range, we extract the features (coefficients) from load data using flourier basis system, and then learn the function in the feature space using artificial neural network. Finally, we smooth the whole forecasted load curve using linear programming. The empirical results indicate that our hybrid method results in better forecasting performance than the original generic support vector regression.

作者: Huanda Lu Kangsheng Liu

作者单位: Laboratory of Information and Optimization Technologies Ningbo Institute of Technology, Zhejiang Uni Department of Mathematics Zhejiang University Hangzhou, China

会议类型: 国际会议

会议名称: The 2010 International Conference on Intelligent Systems and Knowledge Engineering(第五届智能系统与知识工程国际会议)

会议地点: 杭州

会议语种:英文

页码: 177-181

在线出版日期: 2010-11-15（万方平台首次上网日期，不代表论文的发表时间）