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


OZGUNER E., TOR O. B. , GÜVEN A. N.

TURKISH JOURNAL OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCES, vol.25, no.6, pp.4923-4935, 2017 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 25 Issue: 6
  • Publication Date: 2017
  • Doi Number: 10.3906/elk-1612-206
  • Journal Name: TURKISH JOURNAL OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCES
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, TR DİZİN (ULAKBİM)
  • Page Numbers: pp.4923-4935
  • Keywords: Artificial neural networks, electricity market, price forecasting, system marginal price, ARTIFICIAL NEURAL-NETWORKS
  • Middle East Technical University Affiliated: Yes

Abstract

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.