Online transfer learning (OTL) for accelerating deep reinforcement learning (DRL) for building energy management.
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| Title: | Online transfer learning (OTL) for accelerating deep reinforcement learning (DRL) for building energy management. |
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| Authors: | Quang, Tran Van1 (AUTHOR), Doan, Dat Tien2 (AUTHOR) dat.doan@aut.ac.nz |
| Source: | Journal of Building Performance Simulation. Jun2026, Vol. 19 Issue 4, p790-809. 20p. |
| Subjects: | Energy management, Reinforcement learning, Real-time control, Energy consumption, Clean energy, Machine learning |
| Abstract: | Buildings account for over one-third of global energy consumption and emissions, primarily from heating and cooling operations. Intelligent optimisation through predictive controls, such as deep reinforcement learning (DRL), offers significant potential for energy efficiency. However, DRL faces challenges in generalisation and impractical retraining when applied to different buildings, limiting its scalability. Prior online transfer learning (OTL) approaches relied on simulation or rule-based methods but lacked live learning and real-time optimisation. This study proposes an OTL strategy combining autonomous simulation-based DRL policy pretraining with real-time fine-tuning for rapid adaptation to new buildings. Using the Soft Actor-Critic (SAC) algorithm, it was tested on commercial building energy management simulations. Results showed 18%+ reductions in HVAC energy consumption and 8%+ improvement in thermal comfort compared to rule-based and non-transfer DRL baselines. Empirical validation highlights OTL's potential in overcoming DRL's cold start and training burdens, paving the way for broader deployment in sustainable energy management. [ABSTRACT FROM AUTHOR] |
| Copyright of Journal of Building Performance Simulation is the property of Taylor & Francis Ltd 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: 194221732 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Online transfer learning (OTL) for accelerating deep reinforcement learning (DRL) for building energy management. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Quang%2C+Tran+Van%22">Quang, Tran Van</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Doan%2C+Dat+Tien%22">Doan, Dat Tien</searchLink><relatesTo>2</relatesTo> (AUTHOR)<i> dat.doan@aut.ac.nz</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Journal+of+Building+Performance+Simulation%22">Journal of Building Performance Simulation</searchLink>. Jun2026, Vol. 19 Issue 4, p790-809. 20p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Energy+management%22">Energy management</searchLink><br /><searchLink fieldCode="DE" term="%22Reinforcement+learning%22">Reinforcement learning</searchLink><br /><searchLink fieldCode="DE" term="%22Real-time+control%22">Real-time control</searchLink><br /><searchLink fieldCode="DE" term="%22Energy+consumption%22">Energy consumption</searchLink><br /><searchLink fieldCode="DE" term="%22Clean+energy%22">Clean energy</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Buildings account for over one-third of global energy consumption and emissions, primarily from heating and cooling operations. Intelligent optimisation through predictive controls, such as deep reinforcement learning (DRL), offers significant potential for energy efficiency. However, DRL faces challenges in generalisation and impractical retraining when applied to different buildings, limiting its scalability. Prior online transfer learning (OTL) approaches relied on simulation or rule-based methods but lacked live learning and real-time optimisation. This study proposes an OTL strategy combining autonomous simulation-based DRL policy pretraining with real-time fine-tuning for rapid adaptation to new buildings. Using the Soft Actor-Critic (SAC) algorithm, it was tested on commercial building energy management simulations. Results showed 18%+ reductions in HVAC energy consumption and 8%+ improvement in thermal comfort compared to rule-based and non-transfer DRL baselines. Empirical validation highlights OTL's potential in overcoming DRL's cold start and training burdens, paving the way for broader deployment in sustainable energy management. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Journal of Building Performance Simulation is the property of Taylor & Francis Ltd 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: 10.1080/19401493.2025.2511826 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 20 StartPage: 790 Subjects: – SubjectFull: Energy management Type: general – SubjectFull: Reinforcement learning Type: general – SubjectFull: Real-time control Type: general – SubjectFull: Energy consumption Type: general – SubjectFull: Clean energy Type: general – SubjectFull: Machine learning Type: general Titles: – TitleFull: Online transfer learning (OTL) for accelerating deep reinforcement learning (DRL) for building energy management. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Quang, Tran Van – PersonEntity: Name: NameFull: Doan, Dat Tien IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 06 Text: Jun2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 19401493 Numbering: – Type: volume Value: 19 – Type: issue Value: 4 Titles: – TitleFull: Journal of Building Performance Simulation Type: main |
| ResultId | 1 |
