Building Energy Management With Reinforcement Learning and Model Predictive Control: A Survey
Building energy management has been recognized as of significant importance on improving the overall system efficiency and reducing the greenhouse gas emission. However, the building energy management system is now facing more challenges and uncertainties with the increasing penetration of renewable...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2022-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9727169/ |
| _version_ | 1818338731598282752 |
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| author | Huiliang Zhang Sayani Seal Di Wu Francois Bouffard Benoit Boulet |
| author_facet | Huiliang Zhang Sayani Seal Di Wu Francois Bouffard Benoit Boulet |
| author_sort | Huiliang Zhang |
| collection | DOAJ |
| description | Building energy management has been recognized as of significant importance on improving the overall system efficiency and reducing the greenhouse gas emission. However, the building energy management system is now facing more challenges and uncertainties with the increasing penetration of renewable energy and increasing adoption of different types of electrical appliances and equipment. Classical model predictive control (MPC) has shown effective in building energy management, although it suffers from labour-intensive modelling and complex online control optimization. Recently, with the growing accessibility to building control and automation data, data-driven solutions such as data-driven MPC and reinforcement learning (RL)-based methods have attracted more research interest. However, the potential of integrating these two types of methods and how to choose suitable control algorithms have not been well discussed. In this work, we first present a compact review of the recent advances in data-driven MPC and RL-based control methods for building energy management. Furthermore, the main challenges in these approaches and general discussions on the selection of control methods are discussed. |
| first_indexed | 2024-12-13T15:15:46Z |
| format | Article |
| id | doaj.art-d6aa2e2efce24c2e8fc23ba1c9df4f1a |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-13T15:15:46Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-d6aa2e2efce24c2e8fc23ba1c9df4f1a2022-12-21T23:40:43ZengIEEEIEEE Access2169-35362022-01-0110278532786210.1109/ACCESS.2022.31565819727169Building Energy Management With Reinforcement Learning and Model Predictive Control: A SurveyHuiliang Zhang0https://orcid.org/0000-0002-4189-7577Sayani Seal1https://orcid.org/0000-0002-0982-8272Di Wu2https://orcid.org/0000-0001-7419-9903Francois Bouffard3https://orcid.org/0000-0003-0410-4483Benoit Boulet4https://orcid.org/0000-0002-3191-3967Department of Electrical and Computer Engineering, McGill University, Montreal, QC, CanadaDepartment of Electrical and Computer Engineering, McGill University, Montreal, QC, CanadaDepartment of Electrical and Computer Engineering, McGill University, Montreal, QC, CanadaDepartment of Electrical and Computer Engineering, McGill University, Montreal, QC, CanadaDepartment of Electrical and Computer Engineering, McGill University, Montreal, QC, CanadaBuilding energy management has been recognized as of significant importance on improving the overall system efficiency and reducing the greenhouse gas emission. However, the building energy management system is now facing more challenges and uncertainties with the increasing penetration of renewable energy and increasing adoption of different types of electrical appliances and equipment. Classical model predictive control (MPC) has shown effective in building energy management, although it suffers from labour-intensive modelling and complex online control optimization. Recently, with the growing accessibility to building control and automation data, data-driven solutions such as data-driven MPC and reinforcement learning (RL)-based methods have attracted more research interest. However, the potential of integrating these two types of methods and how to choose suitable control algorithms have not been well discussed. In this work, we first present a compact review of the recent advances in data-driven MPC and RL-based control methods for building energy management. Furthermore, the main challenges in these approaches and general discussions on the selection of control methods are discussed.https://ieeexplore.ieee.org/document/9727169/Building energy managementmodel predictive controlreinforcement learningdata-driven control |
| spellingShingle | Huiliang Zhang Sayani Seal Di Wu Francois Bouffard Benoit Boulet Building Energy Management With Reinforcement Learning and Model Predictive Control: A Survey IEEE Access Building energy management model predictive control reinforcement learning data-driven control |
| title | Building Energy Management With Reinforcement Learning and Model Predictive Control: A Survey |
| title_full | Building Energy Management With Reinforcement Learning and Model Predictive Control: A Survey |
| title_fullStr | Building Energy Management With Reinforcement Learning and Model Predictive Control: A Survey |
| title_full_unstemmed | Building Energy Management With Reinforcement Learning and Model Predictive Control: A Survey |
| title_short | Building Energy Management With Reinforcement Learning and Model Predictive Control: A Survey |
| title_sort | building energy management with reinforcement learning and model predictive control a survey |
| topic | Building energy management model predictive control reinforcement learning data-driven control |
| url | https://ieeexplore.ieee.org/document/9727169/ |
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