Combining Forecast of Electricity Price with Adaptive Weights Selected by SOM Clustering

This paper proposes an efficient method of combining modelling to forecast day-ahead electricity price. The basic idea of this method is to develop a robust technique to select the proper combining weights of individual models according to their historical performance on similar circumstances. Firstly individuals were trained and validated separately, and then self-organising map neural network (SOM) was applied to cluster the circumstance into multiple categories. For day-ahead price forecasting, the circumstances of the coming day were compared with those of past days and then clustered into the same category by SOM. The combining weights of individual models for next-day price forecasting were determined by their performances in same category in the past. The final forecast was a weighted sum of the individual predictions with the time-varying weights obtained above. An experimental study indicated that the proposed method outperforms other price-forecasting techniques with high efficiency more robustness.