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.
Artificial neural networks, electricity market, price forecasting, system marginal price
ÖZGÜNER, ERDEM; TÖR, OSMAN BÜLENT; and GÜVEN, ALİ NEZİH
"Probabilistic day-ahead system marginal price forecasting with ANN for the Turkish electricity market,"
Turkish Journal of Electrical Engineering and Computer Sciences: Vol. 25:
6, Article 40.
Available at: https://journals.tubitak.gov.tr/elektrik/vol25/iss6/40