Day-ahead electricity price prediction applying hybrid models of LSTM-based deep learning methods and feature selection algorithms under consideration of market coupling

Wei Li and Denis Mike Becker

Energy, 2021, vol. 237, issue C

Abstract: The availability of accurate day-ahead electricity price forecasts is pivotal for electricity market participants. In the context of trade liberalisation and market harmonisation in the European markets, accurate price forecasting becomes difficult for electricity market participants to obtain because electricity forecasting requires the consideration of features from ever-growing coupling markets. This study provides a method of exploring the influence of market coupling on electricity price prediction. We apply state-of-the-art long short-term memory (LSTM) deep neural networks combined with feature selection algorithms for electricity price prediction under the consideration of market coupling. LSTM models have a good performance in handling nonlinear and complex problems and processing time series data. In our empirical study of the Nordic market, the proposed models obtain considerably accurate results. The results show that feature selection is essential to achieving accurate prediction, and features from integrated markets have an impact on prediction. The feature importance analysis implies that the German market has a salient role in the price generation of Nord Pool.

Keywords: Deep learning; Electricity price forecasting (EPF); Electricity market coupling; Feature selection; Long short-term memory (LSTM); The Nord Pool system price (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (34)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:237:y:2021:i:c:s0360544221017916

DOI: 10.1016/j.energy.2021.121543

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