Electricity price forecasting by a hybrid model, combining wavelet transform, ARMA and kernel-based extreme learning machine methods. (15th March 2017) - Yang, Zhang; Ce, Li; Lian, Li
Highlights: Propose a new hybrid model for day-ahead electricity price forecasting. Analyze complex features of prices series by wavelet transform and stationarity test. The proposed model has linear and nonlinear prediction abilities. Three real electricity markets data are used to assess the forecasting performance. Improve the prediction accuracy of electricity price forecasting. Abstract: Electricity prices have rather complex features such as high volatility, high frequency, nonlinearity, mean reversion and non-stationarity that make forecasting very difficult. However, accurate electricity price forecasting is essential to market traders, retailers, and generation companies. To improve prediction accuracy using each model's unique features, this paper proposes a hybrid approach that combines the wavelet transform, the kernel extreme learning machine (KELM) based on self-adapting particle swarm optimization and an auto regressive moving average (ARMA). Self-adaptive particle swarm optimization (SAPSO) is adopted to search for the optimal kernel parameters of the KELM. After testing the wavelet decomposition components, stationary series as new input sets are predicted by the ARMA model and non-stationary series are predicted by the SAPSO-KELM model. The performance of the proposed method is evaluated by using electricity price data from the Pennsylvania-New Jersey-Maryland (PJM), Australian and Spanish markets. The experimental results show that the developed method has more accurate prediction, better generality and practicability than individual methods and other hybrid methods.