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-stationary and time-variant behavior. This studypresents a new approach to short-term electricity price forecasting. The proposed method is derived by integrating the kernelprincipal component analysis (KPCA) method with the local Gaussian Process (GP), which can be derived bycombining the GP with the local regression method. Local prediction makes use of similar historical data patterns in the reconstructed space to train the regressionalgorithm. In the proposed method, KPCA is used to extract features of the inputs and obtain kernel principal components forconstructing the phase space of the time series of the inputs. Then local GP is employed to solve the price forecastingproblem. The proposed method is evaluated using real-world dataset. The results show that the proposed method can improvethe price forecasting accuracy and provides a much better prediction performance in comparison with other recentlypublished approaches.

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