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Development of an ANN-based building energy model for information-poor buildings using transfer learning

Abstract:: Accurate building energy prediction is vital to develop optimal control strategies to enhance building energy efficiency and energy flexibility. In recent years, the data-driven approach based on machine learning algorithms has been widely adopted for building energy prediction due to the availability of massive data in building automation systems (BASs), which automatically collect and store real-time building operational data. For new buildings and most existing buildings without installing...
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Publication status:: Published

Peer review status:: Peer reviewed

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APA Style

Li, A., Xiao, F., Fan, C., & Hu, M. (2020). Development of an ANN-based building energy model for information-poor buildings using transfer learning. Building Simulation, 14, 89–101.

MLA Style

Li, A., et al. “Development of an ANN-Based Building Energy Model for Information-Poor Buildings Using Transfer Learning.” Building Simulation, vol. 14, Springer, 2020, pp. 89–101.

Chicago Style

Li, A, F Xiao, C Fan, and M Hu. 2020. “Development of an ANN-Based Building Energy Model for Information-Poor Buildings Using Transfer Learning.” Building Simulation 14: 89–101.
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Publisher copy:: 10.1007/s12273-020-0711-5

Authors

+ Li, A More by this author

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Author

+ Xiao, F More by this author

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Author

+ Fan, C More by this author

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+ Hu, M More by this author

Institution:

University of Oxford

Division:

MPLS

Department:

Engineering Science

Role:

Author

Bibliographic Details

Publisher:: Springer Publisher's website
Journal:: Building Simulation Journal website
Volume:: 14
Pages:: 89-101
Publication date:: 2020-09-11
Acceptance date:: 2020-08-17
DOI:: 10.1007/s12273-020-0711-5
EISSN:: 1996-8744
ISSN:: 1996-3599

Item Description

Language:: English
Keywords:: data-driven approach

FFR

neural network

information poor buildings

building energy prediction

transfer learning
Pubs id:: 1133784
Local pid:: pubs:1133784
Deposit date:: 2021-09-20

Terms of use

Copyright holder:: Tsinghua University Press and Springer-Verlag GmbH Germany, part of Springer Nature
Copyright date:: 2020
Rights statement:: Copyright © 2020, Tsinghua University Press and Springer-Verlag GmbH Germany, part of Springer Nature.

Licence:: Terms and Conditions of Use for Oxford University Research Archive

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