A Hybrid Model for Day-Ahead Price Forecasting

Abstract

This paper presents a hybrid time-series and adaptive wavelet neural network (AWNN) model for the day-ahead electricity market clearing price forecast. Instead of using price series, one-period continuously compounded return series is used to achieve more attractive statistical properties. The autoregressive moving average with exogenous variables (ARMAX) model is used to catch the linear relationship between price return series and explanatory variable load series, the generalized autoregressive conditional heteroscedastic (GARCH) model is used to unveil the heteroscedastic character of residuals, and AWNN is used to present the nonlinear, nonstationary impact of load series on electricity prices. The Monte Carlo method is adopted to generate more evenly distributed random numbers used for time series and AWNN models to accelerate the convergence. Several criteria such as average mean absolute percentage error (AMAPE) and the variance of forecast errors are used to assess the model and measure the forecasting accuracy. Illustrative price forecasting examples of the PJM market are presented to show the efficiency of the proposed method.