SHORT-TERM LOAD FORECASTING USING A CBR-ANN MODEL

摘要：

This paper presents an approach based on rough set The approach improves case-based reasoning to reduce the initial information and to find similar historical daily information.The result of case-based reasoning will be put into an artificial neural network to process and then get the forecasting result.The paper provides new method to selecting a relevant feature subset and feature weights.The experiment results on Hangzhou area show that the proposed method is feasible and promising for short-term load forecasting.

关键词： Load forecasting Case-based reasoning Rough set Feature selection Artificial Neural Network

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

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

会议类型: 国际会议

会议名称: 2007 International Conference on Machine Learning and Cybernetics(IEEE第六届机器学习与控制论国际会议)

会议地点: 香港

会议语种:英文

页码: 2719-2723

在线出版日期: 2007-08-19（万方平台首次上网日期，不代表论文的发表时间）