Electricity price is of the first consideration for all the participants in electric power market and its characteristics are related to both market mechanism and variation in the behaviors of market participants. It ...Electricity price is of the first consideration for all the participants in electric power market and its characteristics are related to both market mechanism and variation in the behaviors of market participants. It is necessary to build a real-time price forecasting model with adaptive capability; and because there are outliers in the price data, they should be detected and filtrated in training the forecasting model by regression method. In view of these points, this paper presents an electricity price forecasting method based on accurate on-line support vector regression (AOSVR) and outlier detection. Numerical testing results show that the method is effective in forecasting the electricity prices in electric power market.展开更多
基金This paper is about a project financed by the National Outstanding Young Investigator Grant (6970025)863 High Tech Development Plan of China (2001AA413910) the Project of National Natural Science Foundation (60274054) the Key Project of National Natural Science Foundation (59937150)it is also supported by its cooperating project financed by 863 High Tech Development Plan of China (2004AA412050).
文摘Electricity price is of the first consideration for all the participants in electric power market and its characteristics are related to both market mechanism and variation in the behaviors of market participants. It is necessary to build a real-time price forecasting model with adaptive capability; and because there are outliers in the price data, they should be detected and filtrated in training the forecasting model by regression method. In view of these points, this paper presents an electricity price forecasting method based on accurate on-line support vector regression (AOSVR) and outlier detection. Numerical testing results show that the method is effective in forecasting the electricity prices in electric power market.
