Reinforcement Learning for Sustainable Energy: A Survey
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arXiv
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| Auteurs principaux: | , , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866929438148526080 |
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| author | Ponse, Koen Kleuker, Felix Fejér, Márton Serra-Gómez, Álvaro Plaat, Aske Moerland, Thomas |
| author_facet | Ponse, Koen Kleuker, Felix Fejér, Márton Serra-Gómez, Álvaro Plaat, Aske Moerland, Thomas |
| contents | The transition to sustainable energy is a key challenge of our time, requiring modifications in the entire pipeline of energy production, storage, transmission, and consumption. At every stage, new sequential decision-making challenges emerge, ranging from the operation of wind farms to the management of electrical grids or the scheduling of electric vehicle charging stations. All such problems are well suited for reinforcement learning, the branch of machine learning that learns behavior from data. Therefore, numerous studies have explored the use of reinforcement learning for sustainable energy. This paper surveys this literature with the intention of bridging both the underlying research communities: energy and machine learning. After a brief introduction of both fields, we systematically list relevant sustainability challenges, how they can be modeled as a reinforcement learning problem, and what solution approaches currently exist in the literature. Afterwards, we zoom out and identify overarching reinforcement learning themes that appear throughout sustainability, such as multi-agent, offline, and safe reinforcement learning. Lastly, we also cover standardization of environments, which will be crucial for connecting both research fields, and highlight potential directions for future work. In summary, this survey provides an extensive overview of reinforcement learning methods for sustainable energy, which may play a vital role in the energy transition. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_18597 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Reinforcement Learning for Sustainable Energy: A Survey Ponse, Koen Kleuker, Felix Fejér, Márton Serra-Gómez, Álvaro Plaat, Aske Moerland, Thomas Machine Learning Artificial Intelligence Computers and Society Systems and Control The transition to sustainable energy is a key challenge of our time, requiring modifications in the entire pipeline of energy production, storage, transmission, and consumption. At every stage, new sequential decision-making challenges emerge, ranging from the operation of wind farms to the management of electrical grids or the scheduling of electric vehicle charging stations. All such problems are well suited for reinforcement learning, the branch of machine learning that learns behavior from data. Therefore, numerous studies have explored the use of reinforcement learning for sustainable energy. This paper surveys this literature with the intention of bridging both the underlying research communities: energy and machine learning. After a brief introduction of both fields, we systematically list relevant sustainability challenges, how they can be modeled as a reinforcement learning problem, and what solution approaches currently exist in the literature. Afterwards, we zoom out and identify overarching reinforcement learning themes that appear throughout sustainability, such as multi-agent, offline, and safe reinforcement learning. Lastly, we also cover standardization of environments, which will be crucial for connecting both research fields, and highlight potential directions for future work. In summary, this survey provides an extensive overview of reinforcement learning methods for sustainable energy, which may play a vital role in the energy transition. |
| title | Reinforcement Learning for Sustainable Energy: A Survey |
| topic | Machine Learning Artificial Intelligence Computers and Society Systems and Control |
| url | https://arxiv.org/abs/2407.18597 |