A Machine Learning-Based Electricity Consumption Forecast and Management System for Renewable Energy Communities
The energy sector is currently undergoing a significant shift, driven by the growing integration of renewable energy sources and the decentralization of electricity markets, which are now extending into local communities. This transformation highlights the pivotal role of prosumers within these mark...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-01-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/17/3/630 |
| _version_ | 1797318782383816704 |
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| author | Miguel Matos João Almeida Pedro Gonçalves Fabiano Baldo Fernando José Braz Paulo C. Bartolomeu |
| author_facet | Miguel Matos João Almeida Pedro Gonçalves Fabiano Baldo Fernando José Braz Paulo C. Bartolomeu |
| author_sort | Miguel Matos |
| collection | DOAJ |
| description | The energy sector is currently undergoing a significant shift, driven by the growing integration of renewable energy sources and the decentralization of electricity markets, which are now extending into local communities. This transformation highlights the pivotal role of prosumers within these markets, and as a result, the concept of Renewable Energy Communities is gaining traction, empowering their members to curtail reliance on non-renewable energy sources by facilitating local energy generation, storage, and exchange. Also in a community, management efficiency depends on being able to predict future consumption to make decisions regarding the purchase, sale and storage of electricity, which is why forecasting the consumption of community members is extremely important. This study presents an innovative approach to manage community energy balance, relying on Machine Learning (ML) techniques, namely eXtreme Gradient Boosting (XGBoost), to forecast electricity consumption. Subsequently, a decision algorithm is employed for energy trading with the public grid, based on solar production and energy consumption forecasts, storage levels and market electricity prices. The outcomes of the simulated model demonstrate the efficacy of incorporating these techniques, since the system showcases the potential to reduce both the community electricity expenses and its dependence on energy from the centralized distribution grid. ML-based techniques allowed better results specially for bi-hourly tariffs and high storage capacity scenarios with community bill reductions of 9.8%, 2.8% and 5.4% for high, low, and average photovoltaic (PV) generation levels, respectively. |
| first_indexed | 2024-03-08T03:57:24Z |
| format | Article |
| id | doaj.art-1f657130b48b4637827e54efb6c16c42 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-08T03:57:24Z |
| publishDate | 2024-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-1f657130b48b4637827e54efb6c16c422024-02-09T15:11:20ZengMDPI AGEnergies1996-10732024-01-0117363010.3390/en17030630A Machine Learning-Based Electricity Consumption Forecast and Management System for Renewable Energy CommunitiesMiguel Matos0João Almeida1Pedro Gonçalves2Fabiano Baldo3Fernando José Braz4Paulo C. Bartolomeu5Instituto de Telecomunicações, Departamento de Eletrónica, Telecomunicações e Informática, Universidade de Aveiro, Campus Universitário de Santiago, 3810-193 Aveiro, PortugalInstituto de Telecomunicações, Universidade de Aveiro, Campus Universitário de Santiago, 3810-193 Aveiro, PortugalInstituto de Telecomunicações, Escola Superior de Tecnologia e Gestão de Águeda, Universidade de Aveiro, Campus Universitário de Santiago, 3810-193 Aveiro, PortugalDepartamento de Ciência da Computação, Universidade do Estado de Santa Catarina, Joinville 89219-710, BrazilInstituto Federal Catarinense, Campus Araquari, Araquari 89245-000, BrazilInstituto de Telecomunicações, Departamento de Eletrónica, Telecomunicações e Informática, Universidade de Aveiro, Campus Universitário de Santiago, 3810-193 Aveiro, PortugalThe energy sector is currently undergoing a significant shift, driven by the growing integration of renewable energy sources and the decentralization of electricity markets, which are now extending into local communities. This transformation highlights the pivotal role of prosumers within these markets, and as a result, the concept of Renewable Energy Communities is gaining traction, empowering their members to curtail reliance on non-renewable energy sources by facilitating local energy generation, storage, and exchange. Also in a community, management efficiency depends on being able to predict future consumption to make decisions regarding the purchase, sale and storage of electricity, which is why forecasting the consumption of community members is extremely important. This study presents an innovative approach to manage community energy balance, relying on Machine Learning (ML) techniques, namely eXtreme Gradient Boosting (XGBoost), to forecast electricity consumption. Subsequently, a decision algorithm is employed for energy trading with the public grid, based on solar production and energy consumption forecasts, storage levels and market electricity prices. The outcomes of the simulated model demonstrate the efficacy of incorporating these techniques, since the system showcases the potential to reduce both the community electricity expenses and its dependence on energy from the centralized distribution grid. ML-based techniques allowed better results specially for bi-hourly tariffs and high storage capacity scenarios with community bill reductions of 9.8%, 2.8% and 5.4% for high, low, and average photovoltaic (PV) generation levels, respectively.https://www.mdpi.com/1996-1073/17/3/630distributed energy resourcesmachine learningrenewable energy communitieselectricity consumption forecastenergy management system |
| spellingShingle | Miguel Matos João Almeida Pedro Gonçalves Fabiano Baldo Fernando José Braz Paulo C. Bartolomeu A Machine Learning-Based Electricity Consumption Forecast and Management System for Renewable Energy Communities Energies distributed energy resources machine learning renewable energy communities electricity consumption forecast energy management system |
| title | A Machine Learning-Based Electricity Consumption Forecast and Management System for Renewable Energy Communities |
| title_full | A Machine Learning-Based Electricity Consumption Forecast and Management System for Renewable Energy Communities |
| title_fullStr | A Machine Learning-Based Electricity Consumption Forecast and Management System for Renewable Energy Communities |
| title_full_unstemmed | A Machine Learning-Based Electricity Consumption Forecast and Management System for Renewable Energy Communities |
| title_short | A Machine Learning-Based Electricity Consumption Forecast and Management System for Renewable Energy Communities |
| title_sort | machine learning based electricity consumption forecast and management system for renewable energy communities |
| topic | distributed energy resources machine learning renewable energy communities electricity consumption forecast energy management system |
| url | https://www.mdpi.com/1996-1073/17/3/630 |
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