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23 October 2020
Advanced data analytics for enhancing building performances: From data-driven to big data-driven approaches
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building performance, advanced data analytics, big-data-driven, building energy modeling, building operational data
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Fan C, Yan D, Xiao F, et al.
Advanced data analytics for enhancing building performances: From data-driven to big data-driven approaches.
Building Simulation,
2021, 14(1): 3-24.
https://doi.org/10.1007/s12273-020-0723-1
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Buildings have a significant impact on global sustainability. During the past decades, a wide variety of studies have been conducted throughout the building lifecycle for improving the building performance. Data-driven approach has been widely adopted owing to less detailed building information required and high computational efficiency for online applications. Recent advances in information technologies and data science have enabled convenient access, storage, and analysis of massive on-site measurements, bringing about a new big-data-driven research paradigm. This paper presents a critical review of data-driven methods, particularly those methods based on larger datasets, for building energy modeling and their practical applications for improving building performances. This paper is organized based on the four essential phases of big-data-driven modeling, i.e., data preprocessing, model development, knowledge post-processing, and practical applications throughout the building lifecycle. Typical data analysis and application methods have been summarized and compared at each stage, based upon which in-depth discussions and future research directions have been presented. This review demonstrates that the insights obtained from big building data can be extremely helpful for enriching the existing knowledge repository regarding building energy modeling. Furthermore, considering the ever-increasing development of smart buildings and IoT-driven smart cities, the big data-driven research paradigm will become an essential supplement to existing scientific research methods in the building sector.
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Advanced data analytics for enhancing building performances: From data-driven to big data-driven approaches
Abstract
Buildings have a significant impact on global sustainability. During the past decades, a wide variety of studies have been conducted throughout the building lifecycle for improving the building performance. Data-driven approach has been widely adopted owing to less detailed building information required and high computational efficiency for online applications. Recent advances in information technologies and data science have enabled convenient access, storage, and analysis of massive on-site measurements, bringing about a new big-data-driven research paradigm. This paper presents a critical review of data-driven methods, particularly those methods based on larger datasets, for building energy modeling and their practical applications for improving building performances. This paper is organized based on the four essential phases of big-data-driven modeling, i.e., data preprocessing, model development, knowledge post-processing, and practical applications throughout the building lifecycle. Typical data analysis and application methods have been summarized and compared at each stage, based upon which in-depth discussions and future research directions have been presented. This review demonstrates that the insights obtained from big building data can be extremely helpful for enriching the existing knowledge repository regarding building energy modeling. Furthermore, considering the ever-increasing development of smart buildings and IoT-driven smart cities, the big data-driven research paradigm will become an essential supplement to existing scientific research methods in the building sector.
Keywords:
building performance, advanced data analytics, big-data-driven, building energy modeling, building operational data
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Publication history
Copyright
Acknowledgements
Publication history
Received: 24 May 2020
Accepted: 03 September 2020
Published:
23 October 2020
Issue date: February 2021
Copyright
© Tsinghua University Press and Springer-Verlag GmbH Germany, part of Springer Nature 2020
Acknowledgements
The authors gratefully acknowledge the support of this research by the Research Grant Council of Hong Kong SAR (152075/19E), the National Natural Science Foundation of China (No. 51908365), and the National Natural Science Foundation of China (No. 51778321).
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