Machine Learning- and Artificial Intelligence-Derived Prediction for Home Smart Energy Systems with PV Installation and Battery Energy Storage
Overview: Photovoltaic (PV) systems are widely used in residential applications in Poland and Europe due to increasing environmental concerns and fossil fuel energy prices. Energy management strategies for residential systems (1.2 million prosumer PV installations in Poland) play an important role i...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-09-01
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| Series: | Energies |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1996-1073/16/18/6613 |
| _version_ | 1797580375503929344 |
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| author | Izabela Rojek Dariusz Mikołajewski Adam Mroziński Marek Macko |
| author_facet | Izabela Rojek Dariusz Mikołajewski Adam Mroziński Marek Macko |
| author_sort | Izabela Rojek |
| collection | DOAJ |
| description | Overview: Photovoltaic (PV) systems are widely used in residential applications in Poland and Europe due to increasing environmental concerns and fossil fuel energy prices. Energy management strategies for residential systems (1.2 million prosumer PV installations in Poland) play an important role in reducing energy bills and maximizing profits. Problem: This article aims to check how predictable the operation of a household PV system is in the short term—such predictions are usually made 24 h in advance. Methods: We made a comparative study of different energy management strategies based on a real household profile (selected energy storage installation) based on both traditional methods and various artificial intelligence (AI) tools, which is a new approach, so far rarely used and underutilized, and may inspire further research, including those based on the paradigm of Industry 4.0 and, increasingly, Industry 5.0. Results: This paper discusses the results for different operational scenarios, considering two prosumer billing systems in Poland (net metering and net billing). Conclusions: Insights into future research directions and their limitations due to legal status, etc., are presented. The novelty and contribution lies in the demonstration that, in the case of domestic PV grids, even simple AI solutions can prove effective in inference and forecasting to support energy flow management and make it more predictable and efficient. |
| first_indexed | 2024-03-10T22:49:03Z |
| format | Article |
| id | doaj.art-3b9573faa32f41d486725e6ae96d799b |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-10T22:49:03Z |
| publishDate | 2023-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-3b9573faa32f41d486725e6ae96d799b2023-11-19T10:27:39ZengMDPI AGEnergies1996-10732023-09-011618661310.3390/en16186613Machine Learning- and Artificial Intelligence-Derived Prediction for Home Smart Energy Systems with PV Installation and Battery Energy StorageIzabela Rojek0Dariusz Mikołajewski1Adam Mroziński2Marek Macko3Faculty of Computer Science, Kazimierz Wielki University, Chodkiewicza 30, 85-064 Bydgoszcz, PolandFaculty of Computer Science, Kazimierz Wielki University, Chodkiewicza 30, 85-064 Bydgoszcz, PolandFaculty of Mechanical Engineering, Bydgoszcz University of Science and Technology, Kaliskiego 7, 85-796 Bydgoszcz, PolandFaculty of Mechatronics, Kazimierz Wielki University, Chodkiewicza 30, 85-064 Bydgoszcz, PolandOverview: Photovoltaic (PV) systems are widely used in residential applications in Poland and Europe due to increasing environmental concerns and fossil fuel energy prices. Energy management strategies for residential systems (1.2 million prosumer PV installations in Poland) play an important role in reducing energy bills and maximizing profits. Problem: This article aims to check how predictable the operation of a household PV system is in the short term—such predictions are usually made 24 h in advance. Methods: We made a comparative study of different energy management strategies based on a real household profile (selected energy storage installation) based on both traditional methods and various artificial intelligence (AI) tools, which is a new approach, so far rarely used and underutilized, and may inspire further research, including those based on the paradigm of Industry 4.0 and, increasingly, Industry 5.0. Results: This paper discusses the results for different operational scenarios, considering two prosumer billing systems in Poland (net metering and net billing). Conclusions: Insights into future research directions and their limitations due to legal status, etc., are presented. The novelty and contribution lies in the demonstration that, in the case of domestic PV grids, even simple AI solutions can prove effective in inference and forecasting to support energy flow management and make it more predictable and efficient.https://www.mdpi.com/1996-1073/16/18/6613artificial intelligence (AI) PV energydistributed energy resources (DER)energy storage systemoptimizationenergy forecasting |
| spellingShingle | Izabela Rojek Dariusz Mikołajewski Adam Mroziński Marek Macko Machine Learning- and Artificial Intelligence-Derived Prediction for Home Smart Energy Systems with PV Installation and Battery Energy Storage Energies artificial intelligence (AI) PV energy distributed energy resources (DER) energy storage system optimization energy forecasting |
| title | Machine Learning- and Artificial Intelligence-Derived Prediction for Home Smart Energy Systems with PV Installation and Battery Energy Storage |
| title_full | Machine Learning- and Artificial Intelligence-Derived Prediction for Home Smart Energy Systems with PV Installation and Battery Energy Storage |
| title_fullStr | Machine Learning- and Artificial Intelligence-Derived Prediction for Home Smart Energy Systems with PV Installation and Battery Energy Storage |
| title_full_unstemmed | Machine Learning- and Artificial Intelligence-Derived Prediction for Home Smart Energy Systems with PV Installation and Battery Energy Storage |
| title_short | Machine Learning- and Artificial Intelligence-Derived Prediction for Home Smart Energy Systems with PV Installation and Battery Energy Storage |
| title_sort | machine learning and artificial intelligence derived prediction for home smart energy systems with pv installation and battery energy storage |
| topic | artificial intelligence (AI) PV energy distributed energy resources (DER) energy storage system optimization energy forecasting |
| url | https://www.mdpi.com/1996-1073/16/18/6613 |
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