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Dergiler / Turkish Journal of Electrical Engineering and Computer Science / 2015 / Cilt: 23 - Sayı: 3

Forecasting the day-ahead price in electricity balancing and settlement market of Turkey by using artificial neural networks

Mehmet Ali KÖLMEK
İsa NAVRUZ
Sayfa
841–852
Yayın Tarihi
2015-08-13
DOI
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⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

Moreover, a time series model is made and it is compared with the artificial neural network's error performance.

📌 3 Maddede Bu Makale

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Amaç & Hipotez:In determination of electric energy price, most price information coming from bilateral contracts is effective, but the importance of the spot market (pool market) price cannot be ignored.
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Temel Bulgular & Veri:After selection of different network topologies and parameters, attempts are made to observe network performance by error rates and find the best suitable configuration.
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Sonuç & Katkı:Moreover, a time series model is made and it is compared with the artificial neural network's error performance.
🔬Tür:Laboratuvar & Saha Deneyi
📍Kapsam:Genişletilmiş Akademik Örneklem
⚙️Değişkenler:Artificial neural networks · Turkish electricity market · day-ahead price

Özet

In determination of electric energy price, most price information coming from bilateral contracts is effective, but the importance of the spot market (pool market) price cannot be ignored. Forecasting the spot market price is very crucial, especially for companies actively participating in the spot market and giving purchase and sale bids. An artificial neural network is a way frequently used for price forecasting research. In this study, simulation studies about price modeling via artificial neural networks and proper artificial neural network configurations are examined. After selection of different network topologies and parameters, attempts are made to observe network performance by error rates and find the best suitable configuration. Moreover, a time series model is made and it is compared with the artificial neural network's error performance.

Abstract

In determination of electric energy price, most price information coming from bilateral contracts is effective, but the importance of the spot market (pool market) price cannot be ignored. Forecasting the spot market price is very crucial, especially for companies actively participating in the spot market and giving purchase and sale bids. An artificial neural network is a way frequently used for price forecasting research. In this study, simulation studies about price modeling via artificial neural networks and proper artificial neural network configurations are examined. After selection of different network topologies and parameters, attempts are made to observe network performance by error rates and find the best suitable configuration. Moreover, a time series model is made and it is compared with the artificial neural network's error performance.

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Bu Makaleye Atıf Yap

KÖLMEK, M. A., NAVRUZ, İ. (2015). Forecasting the day-ahead price in electricity balancing and settlement market of Turkey by using artificial neural networks. Turkish Journal of Electrical Engineering and Computer Science, Cilt: 23 - Sayı: 3, 841–852. https://ajindex.com/articles/187841

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Kaynakça (12)

  1. preferred. Different studies like estimation of price classification [38] and price spikes [39] have also kept up-to dateness. The ANN model presented in this study could be developed further by utilizing price classification and spikes, and could be applied to SDAP forecasting. Conejo AJ, Contreras J, Espinola R, Plazas MA. Forecasting electricity prices for a day-ahead pool-based electric energy market. Int J Forecasting 2005; 21: 435–462. Hamza¸cebi C. Yapay Sinir A˘gları: Tahmin Ama¸clı Kullanımı Matlab ve Neurosolutions Uygulamalı. Bursa, Turkey: Ekin Basim Yayin Dagitim, 2011 (in Turkish). Niimura T. Forecasting techniques for deregulated electricity market prices - extended survey. In: Proceedings of the Power Engineering Society General Meeting, Atlanta, GA, USA, 2006.
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