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

Date

2017-01-01

Author

OZGUNER, Erdem
TOR, Osman Bulent
Güven, Ali Nezih

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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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.

Subject Keywords

Artificial neural networks, Electricity market, Price forecasting, System marginal price

URI

https://hdl.handle.net/11511/36741

Collections

Department of Electrical and Electronics Engineering, Article