Neural Network Based Model Comparison for Intraday Electricity Price Forecasting

Ilkay Oksuz and Umut Ugurlu
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Ilkay Oksuz: Biomedical Engineering Department, King’s College London, London SE1 7EU, UK
Umut Ugurlu: Management Department, Bahcesehir University, Besiktas, Istanbul 34349, Turkey

Energies, 2019, vol. 12, issue 23, 1-14

Abstract: 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.

Keywords: electricity price forecasting; neural networks; gated recurrent unit; long short term memory; artificial intelligence; Turkish intraday market (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (19)

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