Short-term electricity price forecasting has been a prominent factor in many aspects of electrical power companies. Revolutionary changes in modern electrical grids so-called Smart Grids have introduced smart meters as essential parts of the Smart Grids, which increase the importance of Electricity price and load in the energy management system. Short-term electricity price forecasting has been a prominent factor in many aspects of electrical power companies over the last 15 years. Several modeling approaches, such as Fundamental models, Reduced-form models, Statistical Models, and Computational intelligence models, have been used by the researchers in the field. Among the Statistical models, the time series models have shown great performance. In this paper, we propose a new price forecasting method based on SARIMA-GARCH models with the Skew-Normal Distribution as electricity spot prices exhibit large deviations. The model is constructed to simulate and compose the estimated components of the time series model to predict the future electricity price for the Electric Reliability Council of Texas. Finally, the obtained results from the proposed model are compared with the Normal Distribution Assumption. The effectiveness of the presented method is verified by using real electricity price data from the Electric Reliability Council of Texas. This finding confirms that the forecast accuracy can be significantly improved by the proposed method.
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Keywords: Electricity price, forecasting, econometrics time series, SARIMA
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