Day-ahead electricity price forecasting by a new hybrid method

Electricity price forecasting has become necessary for power producers and consumers in the current deregulated electricity markets. Seeking for more accurate price forecasting techniques, this paper proposes a new hybrid method based on wavelet transform (WT), autoregressive integrated moving average (ARIMA) and least squares support vector machine (LSSVM) optimized by particle swarm optimization (PSO) to predict electricity prices. The proposed method is examined by using the data from New South Wales (NSW) of Australian national electricity market. Empirical testing indicates that the proposed method can provide more accurate and effective results than the other price forecasting methods.

► Advanced computational methods aims at developing efficient solution techniques.
► Hybrid model can capture different patterns in electricity price series.
► Validation of results through experiments connects hybrid model to the real word.