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

Author

Listed:
  • 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
    Department of Transport Management and Logistics Engineering, University of Moratuwa, Katubedda, Moratuwa 10400, Sri Lanka)

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.

Suggested Citation

  • Deniz Kenan Kılıç & Peter Nielsen & Amila Thibbotuwawa, 2024. "Intraday Electricity Price Forecasting via LSTM and Trading Strategy for the Power Market: A Case Study of the West Denmark DK1 Grid Region," Energies, MDPI, vol. 17(12), pages 1-15, June.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:12:p:2909-:d:1414040
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    References listed on IDEAS

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    1. Vasileios Laitsos & Georgios Vontzos & Paschalis Paraschoudis & Eleftherios Tsampasis & Dimitrios Bargiotas & Lefteri H. Tsoukalas, 2024. "The State of the Art Electricity Load and Price Forecasting for the Modern Wholesale Electricity Market," Energies, MDPI, vol. 17(22), pages 1-37, November.

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