Day-ahead price forecasting of electricity markets based on local informative vector machine

Abstract

In a competitive electricity market, short-term electricity price forecasting are very important for market participants. Electricity price is a very complex signal as a result of its non-linearity, non-stationarity and time-variant behaviour. This study presents a new approach to short-term electricity price forecasting. The proposed method is derived by integrating the kernel principal component analysis (KPCA) method with the local informative vector machine (IVM), which can be derived by combining the IVM with the local regression method. IVM is a practical probabilistic alternative to the popular support vector machine. Local prediction makes use of similar historical data patterns in the reconstructed space to train the regression algorithm. In the proposed method, KPCA is used to extract features of the inputs and obtain kernel principal components for constructing the phase space of the time series of the inputs. Then local IVM is employed to solve the price forecasting problem. The proposed method is evaluated using real-world dataset. The results show that the proposed method can improve the price forecasting accuracy and provides a much better prediction performance in comparison with other 12 recently published approaches. © The Institution of Engineering and Technology 2013.