Ali Nezih GÜVEN, Osman Bülent TÖR, Erdem ÖZGÜNER

Probabilistic day-ahead system marginal price forecasting with ANN for the Turkish electricity market

This study presents a system day-ahead hourly market clearing price forecasting tool for the day-ahead (DA) market and a system DA hourly marginal price forecasting tool for the real-time market of the Turkish electric market (TEM). These forecasting tools are developed based on arti cial neural networks (ANNs). A series of historical price data of the TEM are utilized to model and optimize the ANN structure and to develop the ANN-based price forecasting tool. The methodology used to select the optimum ANN architecture provides the minimum daily mean absolute percentage error for both day-ahead market prices in the TEM. Performances of the proposed ANN model and the multiple linear regression model in forecasting the day-ahead hourly market clearing price are compared. The proposed ANN model is modi ed using volatility analysis and the Bienayme{Chebyshev inequality in order to forecast system marginal prices probabilistically within a lower and an upper boundary.

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