Optimized Data-Driven Models for Short-Term Electricity Price Forecasting Based on Signal Decomposition and Clustering Techniques
In recent decades, the traditional monopolistic energy exchange market has been replaced by deregulated, competitive marketplaces in which electricity may be purchased and sold at market prices like any other commodity.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...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-10-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/15/21/7929 |
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| author | Athanasios Ioannis Arvanitidis Dimitrios Bargiotas Dimitrios Kontogiannis Athanasios Fevgas Miltiadis Alamaniotis |
| author_facet | Athanasios Ioannis Arvanitidis Dimitrios Bargiotas Dimitrios Kontogiannis Athanasios Fevgas Miltiadis Alamaniotis |
| author_sort | Athanasios Ioannis Arvanitidis |
| collection | DOAJ |
| description | In recent decades, the traditional monopolistic energy exchange market has been replaced by deregulated, competitive marketplaces in which electricity may be purchased and sold at market prices like any other commodity.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%. |
| first_indexed | 2024-03-09T19:07:33Z |
| format | Article |
| id | doaj.art-88137e261ef448dbb9bdafb4f0d15a42 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-09T19:07:33Z |
| publishDate | 2022-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-88137e261ef448dbb9bdafb4f0d15a422023-11-24T04:29:03ZengMDPI AGEnergies1996-10732022-10-011521792910.3390/en15217929Optimized Data-Driven Models for Short-Term Electricity Price Forecasting Based on Signal Decomposition and Clustering TechniquesAthanasios Ioannis Arvanitidis0Dimitrios Bargiotas1Dimitrios Kontogiannis2Athanasios Fevgas3Miltiadis Alamaniotis4Department of Electrical and Computer Engineering, University of Thessaly, 38334 Volos, GreeceDepartment of Electrical and Computer Engineering, University of Thessaly, 38334 Volos, GreeceDepartment of Electrical and Computer Engineering, University of Thessaly, 38334 Volos, GreeceDepartment of Electrical and Computer Engineering, University of Thessaly, 38334 Volos, GreeceDepartment of Electrical and Computer Engineering, University of Texas at San Antonio, San Antonio, TX 78249, USAIn 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%.https://www.mdpi.com/1996-1073/15/21/7929short-term electricity price forecastingdata-driven forecasting modelsmetaheuristic optimization algorithmssignal decompositionclustering algorithmspreprocessing approaches |
| spellingShingle | Athanasios Ioannis Arvanitidis Dimitrios Bargiotas Dimitrios Kontogiannis Athanasios Fevgas Miltiadis Alamaniotis Optimized Data-Driven Models for Short-Term Electricity Price Forecasting Based on Signal Decomposition and Clustering Techniques Energies short-term electricity price forecasting data-driven forecasting models metaheuristic optimization algorithms signal decomposition clustering algorithms preprocessing approaches |
| title | Optimized Data-Driven Models for Short-Term Electricity Price Forecasting Based on Signal Decomposition and Clustering Techniques |
| title_full | Optimized Data-Driven Models for Short-Term Electricity Price Forecasting Based on Signal Decomposition and Clustering Techniques |
| title_fullStr | Optimized Data-Driven Models for Short-Term Electricity Price Forecasting Based on Signal Decomposition and Clustering Techniques |
| title_full_unstemmed | Optimized Data-Driven Models for Short-Term Electricity Price Forecasting Based on Signal Decomposition and Clustering Techniques |
| title_short | Optimized Data-Driven Models for Short-Term Electricity Price Forecasting Based on Signal Decomposition and Clustering Techniques |
| title_sort | optimized data driven models for short term electricity price forecasting based on signal decomposition and clustering techniques |
| topic | short-term electricity price forecasting data-driven forecasting models metaheuristic optimization algorithms signal decomposition clustering algorithms preprocessing approaches |
| url | https://www.mdpi.com/1996-1073/15/21/7929 |
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