Advanced data analytics for enhancing building performances: From data-driven to big data-driven approaches
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Advanced data analytics for enhancing building performances: From data-driven to big data-driven approaches
1. Sino-Australia Joint Research Center in BIM and Smart Construction, College of Civil and Transportation Engineering, Shenzhen University, Shenzhen, China 2. Building Energy Research Center, School of Architecture, Tsinghua University, Beijing, China 3. Department of Building Services Engineering, The Hong Kong Polytechnic University, Hong Kong, China 4. School of Environment and Energy Engineering, Beijing University of Civil Engineering and Architecture, Beijing, China
Abstract
Keywords
Buildings have a significant impact on global sustainability. During the past decades, a wide
advanced data analytics,
variety of studies have been conducted throughout the building lifecycle for improving the building
big-data-driven,
performance. Data-driven approach has been widely adopted owing to less detailed building
building energy modeling,
information required and high computational efficiency for online applications. Recent advances
building operational data,
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
Review Article
Cheng Fan1, Da Yan2 (), Fu Xiao3 (), Ao Li3, Jingjing An4, Xuyuan Kang2
building performance
Article History Received: 24 May 2020 Revised: 26 July 2020 Accepted: 03 September 2020
modeling, i.e., data preprocessing, model development, knowledge post-processing, and practical applications throughout the building lifecycle. Typical data analysis and application methods have
© Tsinghua University Press and
been summarized and compared at each stage, based upon which in-depth discussions and
Springer-Verlag GmbH Germany,
future research directions have been presented. This review demonstrates that the insights obtained
part of Springer Nature 2020
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.
Introduction
Buildings represent a significant amount of total energy consumption in the world. According to the World Energy Balances (IEA 2019a), the building sector accounts for more than 30% of the final energy consumption globally and contributes to nearly 40% of global carbon-dioxide emissions. Energy consumption and carbon emissions are expected to continue increasing in upcoming years (IEA 2019b). As such, building energy perform
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