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Estimation and Simulation of the Transaction Arrival Process in Intraday Electricity Markets
Open AccessArticle

Neural Network Based Model Comparison for Intraday Electricity Price Forecasting

by Ilkay Oksuz 1,*,† and Umut Ugurlu 2,†
1
Biomedical Engineering Department, King’s College London, London SE1 7EU, UK
2
Management Department, Bahcesehir University, Besiktas, Istanbul 34349, Turkey
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Energies 2019, 12(23), 4557; https://doi.org/10.3390/en12234557
Received: 18 October 2019 / Revised: 26 November 2019 / Accepted: 27 November 2019 / Published: 29 November 2019
(This article belongs to the Special Issue Modeling and Forecasting Intraday Electricity Markets)
The intraday electricity markets are continuous trade platforms for each hour of the day and have specific characteristics. These markets have shown an increasing number of transactions due to the requirement of close to delivery electricity trade. Recently, intraday electricity price market research has seen a rapid increase in a number of works for price prediction. However, most of these works focus on the features and descriptive statistics of the intraday electricity markets and overlook the comparison of different available models. In this paper, we compare a variety of methods including neural networks to predict intraday electricity market prices in Turkish intraday market. The recurrent neural networks methods outperform the classical methods. Furthermore, gated recurrent unit network architecture achieves the best results with a mean absolute error of 0.978 and a root mean square error of 1.302. Moreover, our results indicate that day-ahead market price of the corresponding hour is a key feature for intraday price forecasting and estimating spread values with day-ahead prices proves to be a more efficient method for prediction. View Full-Text
Keywords: electricity price forecasting; neural networks; gated recurrent unit; long short term memory; artificial intelligence; Turkish intraday market electricity price forecasting; neural networks; gated recurrent unit; long short term memory; artificial intelligence; Turkish intraday market
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MDPI and ACS Style

Oksuz, I.; Ugurlu, U. Neural Network Based Model Comparison for Intraday Electricity Price Forecasting. Energies 2019, 12, 4557.

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