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

2017-01-01
OZGUNER, Erdem
TOR, Osman Bulent
Güven, Ali Nezih
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 artificial 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 modified using volatility analysis and the Bienayme Chebyshev inequality in order to forecast system marginal prices probabilistically within a lower and an upper boundary.
TURKISH JOURNAL OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCES

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Citation Formats
E. OZGUNER, O. B. TOR, and A. N. Güven, “Probabilistic day-ahead system marginal price forecasting with ANN for the Turkish electricity market,” TURKISH JOURNAL OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCES, pp. 4923–4935, 2017, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/36741.