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Optimized Data-Driven Models for Short-Term Electricity Price Forecasting Based on Signal Decomposition and Clustering Techniques

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  • Athanasios Ioannis Arvanitidis

    (Department of Electrical and Computer Engineering, University of Thessaly, 38334 Volos, Greece)

  • Dimitrios Bargiotas

    (Department of Electrical and Computer Engineering, University of Thessaly, 38334 Volos, Greece)

  • Dimitrios Kontogiannis

    (Department of Electrical and Computer Engineering, University of Thessaly, 38334 Volos, Greece)

  • Athanasios Fevgas

    (Department of Electrical and Computer Engineering, University of Thessaly, 38334 Volos, Greece)

  • Miltiadis Alamaniotis

    (Department of Electrical and Computer Engineering, University of Texas at San Antonio, San Antonio, TX 78249, USA)

Abstract

In recent decades, the traditional monopolistic energy exchange market has been replaced by deregulated, competitive marketplaces in which electricity may be purchased and sold at market prices like any other commodity. As a result, the deregulation of the electricity industry has produced a demand for wholesale organized marketplaces. Price predictions, which are primarily meant to establish the market clearing price, have become a significant factor to an energy company’s decision making and strategic development. Recently, the fast development of deep learning algorithms, as well as the deployment of front-end metaheuristic optimization approaches, have resulted in the efficient development of enhanced prediction models that are used for electricity price forecasting. In this paper, the development of six highly accurate, robust and optimized data-driven forecasting models in conjunction with an optimized Variational Mode Decomposition method and the K-Means clustering algorithm for short-term electricity price forecasting is proposed. In this work, we also establish an Inverted and Discrete Particle Swarm Optimization approach that is implemented for the optimization of the Variational Mode Decomposition method. The prediction of the day-ahead electricity prices is based on historical weather and price data of the deregulated Greek electricity market. The resulting forecasting outcomes are thoroughly compared in order to address which of the two proposed divide-and-conquer preprocessing approaches results in more accuracy concerning the issue of short-term electricity price forecasting. Finally, the proposed technique that produces the smallest error in the electricity price forecasting is based on Variational Mode Decomposition, which is optimized through the proposed variation of Particle Swarm Optimization, with a mean absolute percentage error value of 6.15%.

Suggested Citation

  • Athanasios Ioannis Arvanitidis & Dimitrios Bargiotas & Dimitrios Kontogiannis & Athanasios Fevgas & Miltiadis Alamaniotis, 2022. "Optimized Data-Driven Models for Short-Term Electricity Price Forecasting Based on Signal Decomposition and Clustering Techniques," Energies, MDPI, vol. 15(21), pages 1-24, October.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:21:p:7929-:d:953093
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    References listed on IDEAS

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    1. Ghimire, Sujan & Deo, Ravinesh C. & Casillas-Pérez, David & Salcedo-Sanz, Sancho, 2024. "Two-step deep learning framework with error compensation technique for short-term, half-hourly electricity price forecasting," Applied Energy, Elsevier, vol. 353(PA).
    2. Vasileios Laitsos & Georgios Vontzos & Dimitrios Bargiotas & Aspassia Daskalopulu & Lefteri H. Tsoukalas, 2024. "Data-Driven Techniques for Short-Term Electricity Price Forecasting through Novel Deep Learning Approaches with Attention Mechanisms," Energies, MDPI, vol. 17(7), pages 1-27, March.

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