Electricity Price Prediction by Enhanced Combination of Autoregression Moving Average and Kernal Extreme Learing Machine
In electricity market, electricity price has some complicated features like high volatility, non linearity and non stationarity that make very difficult to predict the accurate price. However, it is necessary for markets and companies to predict accurate electricity price. In this paper, we enhanced the forecasting accuracy by combined approaches of Kernel Extreme Learning Machine (KELM) and Autoregression Moving Average (ARMA) along with unique and enhanced features of both models. Wavelet transform is applied on prices series to decompose them, afterward test has performed on decomposed series for providing stationary series to AMRA model and non stationary series to KELM model. At the end series are tuned with our combine approach of enhanced price prediction. The performance of our enhanced combined method is evaluated by electricity price dataset of New South Wales (NSW) Australian market. The simulation results show that combined method has more accurate prediction than individual methods.
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