Intraday Electricity Price Forecasting via LSTM and Trading Strategy for the Power Market: A Case Study of the West Denmark DK1 Grid Region

Deniz Kenan Kılıç (), Peter Nielsen and Amila Thibbotuwawa
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Deniz Kenan Kılıç: Department of Materials and Production, Aalborg University, Fibigerstræde 16, 9220 Aalborg, Denmark
Peter Nielsen: Department of Materials and Production, Aalborg University, Fibigerstræde 16, 9220 Aalborg, Denmark
Amila Thibbotuwawa: Center for Supply Chain, Operations and Logistics Optimization, University of Moratuwa, Katubedda, Moratuwa 10400, Sri Lanka

Energies, 2024, vol. 17, issue 12, 1-15

Abstract: For several stakeholders, including market players, customers, grid operators, policy-makers, investors, and energy efficiency initiatives, having a precise estimate of power pricing is crucial. It is easier for traders to plan, purchase, and sell power transactions with access to accurate electricity price forecasting (EPF). Although energy production and consumption topics are widely discussed in the literature, EPF and renewable energy trading studies receive less attention, especially for intraday market modeling and forecasting. Considering the rapid development of renewable energy sources, the article highlights the significance of integrating the deep learning model, long short-term memory (LSTM), with the proper trading strategy for short-term hourly renewable energy trading by utilizing two different spot markets. Day-ahead and intraday markets are taken into account for the West Denmark grid region (DK1). The time series analysis indicates that LSTM yields superior results compared to other benchmark machine learning algorithms. Using the predictions obtained by LSTM and the recommended trading strategy, promising profit values are achieved for the DK1 wind and solar energy use case, which ensures future motivation to develop a general and flexible model for global data.

Keywords: long short-term memory (LSTM); electricity price forecasting (EPF); intraday electricity market; time series; energy trading; power market; data-driven prediction; machine learning; renewable energy (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: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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