Day-ahead Electricity Price Forecasting Based on Multi-factor Wavelet Analysis and Multivariate Time Series Models

The electricity market prices are highly volatile,seasonal and stochastic. The previous literature shows that it is difficult to improve the accuracy of price forecasting by applying one model alone. Hence a novel hybrid model for day-ahead electricity price forecasting was presented in this paper. The proposed model is based on multi-factor wavelet analysis and multivariate time series models. Historical prices and loads were decomposed and reconstructed into approximate price series,detailed price series,approximate load series and detailed load series. The future approximate prices were forecasted by multivariate time series models based on historical approximate prices and loads. The future detailed prices were forecasted by univariate time series models. The final forecasted prices are the sum of the predicted approximate prices and detailed prices. This proposed method was applied to forecast the day-ahead electricity prices in California electricity market. The comparisons of forecasting results between the presented method and other methods show that the proposed method can provide more accurate forecasted prices.