Optimal control of HVAC and window systems for natural ventilation through reinforcement learning.
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| Title: | Optimal control of HVAC and window systems for natural ventilation through reinforcement learning. |
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| Authors: | Chen, Yujiao1,2,3 ychen@gsd.harvard.edu, Norford, Leslie K.4, Samuelson, Holly W.1, Malkawi, Ali1,2 |
| Source: | Energy & Buildings. Jun2018, Vol. 169, p195-205. 11p. |
| Subjects: | Heating equipment, Ventilation, Building design & construction, Windows, Cost functions, Energy consumption of buildings |
| Abstract: | Natural ventilation is a green building strategy that improves building energy efficiency, indoor thermal environment, and air quality. However, in practice, it is not always clear when and how to utilize the natural ventilation and coordinate its operation with the HVAC system. This paper introduces a reinforcement learning control strategy, specifically through model-free Q-learning, that makes optimal control decisions for HVAC and window systems to minimize both energy consumption and thermal discomfort. This control system evaluates the outdoor and indoor environments (temperature, humidity, solar radiation, and wind speed) at each time step, and responds with the best control decision that targets both immediate and long-term goals. The reinforcement learning control is evaluated through numerical simulation on a building thermal model and compared with a rule-based heuristic control strategy. Case studies in hot-and-humid Miami and warm-and-mild Los Angeles demonstrated the superior performance of reinforcement learning control, which led to 13% and 23% lower HVAC system energy consumption, 62% and 80% lower discomfort degree hours, and 63% and 77% fewer high humidity hours compared to heuristic control. Unlike heuristic control that requires specific knowledge of individual buildings and the creation of exhaustive decision-making scenarios to improve performance, reinforcement learning control guarantees optimality through self-advancement on given goals and cost functions and is able to adapt to stochastic occupancy and occupant behaviors, which is difficult to accommodate by heuristic control. [ABSTRACT FROM AUTHOR] |
| Copyright of Energy & Buildings is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 129333283 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Optimal control of HVAC and window systems for natural ventilation through reinforcement learning. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Chen%2C+Yujiao%22">Chen, Yujiao</searchLink><relatesTo>1,2,3</relatesTo><i> ychen@gsd.harvard.edu</i><br /><searchLink fieldCode="AR" term="%22Norford%2C+Leslie+K%2E%22">Norford, Leslie K.</searchLink><relatesTo>4</relatesTo><br /><searchLink fieldCode="AR" term="%22Samuelson%2C+Holly+W%2E%22">Samuelson, Holly W.</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Malkawi%2C+Ali%22">Malkawi, Ali</searchLink><relatesTo>1,2</relatesTo> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Energy+%26+Buildings%22">Energy & Buildings</searchLink>. Jun2018, Vol. 169, p195-205. 11p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Heating+equipment%22">Heating equipment</searchLink><br /><searchLink fieldCode="DE" term="%22Ventilation%22">Ventilation</searchLink><br /><searchLink fieldCode="DE" term="%22Building+design+%26+construction%22">Building design & construction</searchLink><br /><searchLink fieldCode="DE" term="%22Windows%22">Windows</searchLink><br /><searchLink fieldCode="DE" term="%22Cost+functions%22">Cost functions</searchLink><br /><searchLink fieldCode="DE" term="%22Energy+consumption+of+buildings%22">Energy consumption of buildings</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Natural ventilation is a green building strategy that improves building energy efficiency, indoor thermal environment, and air quality. However, in practice, it is not always clear when and how to utilize the natural ventilation and coordinate its operation with the HVAC system. This paper introduces a reinforcement learning control strategy, specifically through model-free Q-learning, that makes optimal control decisions for HVAC and window systems to minimize both energy consumption and thermal discomfort. This control system evaluates the outdoor and indoor environments (temperature, humidity, solar radiation, and wind speed) at each time step, and responds with the best control decision that targets both immediate and long-term goals. The reinforcement learning control is evaluated through numerical simulation on a building thermal model and compared with a rule-based heuristic control strategy. Case studies in hot-and-humid Miami and warm-and-mild Los Angeles demonstrated the superior performance of reinforcement learning control, which led to 13% and 23% lower HVAC system energy consumption, 62% and 80% lower discomfort degree hours, and 63% and 77% fewer high humidity hours compared to heuristic control. Unlike heuristic control that requires specific knowledge of individual buildings and the creation of exhaustive decision-making scenarios to improve performance, reinforcement learning control guarantees optimality through self-advancement on given goals and cost functions and is able to adapt to stochastic occupancy and occupant behaviors, which is difficult to accommodate by heuristic control. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Energy & Buildings is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1016/j.enbuild.2018.03.051 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 11 StartPage: 195 Subjects: – SubjectFull: Heating equipment Type: general – SubjectFull: Ventilation Type: general – SubjectFull: Building design & construction Type: general – SubjectFull: Windows Type: general – SubjectFull: Cost functions Type: general – SubjectFull: Energy consumption of buildings Type: general Titles: – TitleFull: Optimal control of HVAC and window systems for natural ventilation through reinforcement learning. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Chen, Yujiao – PersonEntity: Name: NameFull: Norford, Leslie K. – PersonEntity: Name: NameFull: Samuelson, Holly W. – PersonEntity: Name: NameFull: Malkawi, Ali IsPartOfRelationships: – BibEntity: Dates: – D: 15 M: 06 Text: Jun2018 Type: published Y: 2018 Identifiers: – Type: issn-print Value: 03787788 Numbering: – Type: volume Value: 169 Titles: – TitleFull: Energy & Buildings Type: main |
| ResultId | 1 |
