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A Holistic Review of Building Energy Efficiency and Reduction Based on Big Data

Author

Listed:
  • Jeeyoung Lim

    (Department of Architectural Engineering, Pusan National University, Busan 46241, Korea)

  • Joseph J. Kim

    (Department of Civil Engineering and Construction Engineering Management, California State University Long Beach, Long Beach, CA 90840, USA)

  • Sunkuk Kim

    (Department of Architectural Engineering, Kyung Hee University, Yongin-si 17104, Gyeonggi-do, Korea)

Abstract

The construction industry is recognized as a major cause of environmental pollution, and it is important to quantify and evaluate building energy. As interest in big data has increased over the past 20 years, research using big data is active. However, the links and contents of much literature have not been summarized, and systematic literature studies are insufficient. The objective of this study was a holistic review of building energy efficiency/reduction based on big data. This review study used a holistic analysis approach method framework. As a result of the analysis, China, the Republic of Korea, and the USA had the most published papers, and the simulation and optimization area occupied the highest percentage with 33.33%. Most of the researched literature was papers after 2015, and it was analyzed because many countries introduced environmental policies after the 2015 UN Conference on Climate Change. This study can be helpful in understanding the current research progress to understand the latest trends and to set the direction for further research related to big data.

Suggested Citation

  • Jeeyoung Lim & Joseph J. Kim & Sunkuk Kim, 2021. "A Holistic Review of Building Energy Efficiency and Reduction Based on Big Data," Sustainability, MDPI, vol. 13(4), pages 1-18, February.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:4:p:2273-:d:502299
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    References listed on IDEAS

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    1. Ali, Usman & Shamsi, Mohammad Haris & Bohacek, Mark & Hoare, Cathal & Purcell, Karl & Mangina, Eleni & O’Donnell, James, 2020. "A data-driven approach to optimize urban scale energy retrofit decisions for residential buildings," Applied Energy, Elsevier, vol. 267(C).
    2. Pan, Yue & Zhang, Limao, 2020. "Data-driven estimation of building energy consumption with multi-source heterogeneous data," Applied Energy, Elsevier, vol. 268(C).
    3. Shahriar Akter & Samuel Fosso Wamba, 2016. "Big data analytics in E-commerce: a systematic review and agenda for future research," Electronic Markets, Springer;IIM University of St. Gallen, vol. 26(2), pages 173-194, May.
    4. Li, Wenzhuo & Koo, Choongwan & Hong, Taehoon & Oh, Jeongyoon & Cha, Seung Hyun & Wang, Shengwei, 2020. "A novel operation approach for the energy efficiency improvement of the HVAC system in office spaces through real-time big data analytics," Renewable and Sustainable Energy Reviews, Elsevier, vol. 127(C).
    5. Jeeyoung Lim & Joseph J. Kim, 2020. "Dynamic Optimization Model for Estimating In-Situ Production Quantity of PC Members to Minimize Environmental Loads," Sustainability, MDPI, vol. 12(19), pages 1-20, October.
    6. Ding, Ying, 2011. "Scientific collaboration and endorsement: Network analysis of coauthorship and citation networks," Journal of Informetrics, Elsevier, vol. 5(1), pages 187-203.
    7. Fan, Cheng & Xiao, Fu & Yan, Chengchu & Liu, Chengliang & Li, Zhengdao & Wang, Jiayuan, 2019. "A novel methodology to explain and evaluate data-driven building energy performance models based on interpretable machine learning," Applied Energy, Elsevier, vol. 235(C), pages 1551-1560.
    8. Xiao Liu & Param Vir Singh & Kannan Srinivasan, 2016. "A Structured Analysis of Unstructured Big Data by Leveraging Cloud Computing," Marketing Science, INFORMS, vol. 35(3), pages 363-388, May.
    9. Nematchoua, Modeste Kameni & Orosa, Jose A. & Buratti, Cinzia & Obonyo, Esther & Rim, Donghyun & Ricciardi, Paola & Reiter, Sigrid, 2020. "Comparative analysis of bioclimatic zones, energy consumption, CO2 emission and life cycle cost of residential and commercial buildings located in a tropical region: A case study of the big island of Madagascar," Energy, Elsevier, vol. 202(C).
    10. Li, Clyde Zhengdao & Lai, Xulu & Xiao, Bing & Tam, Vivian W.Y. & Guo, Shan & Zhao, Yiyu, 2020. "A holistic review on life cycle energy of buildings: An analysis from 2009 to 2019," Renewable and Sustainable Energy Reviews, Elsevier, vol. 134(C).
    11. Enrico Biffis & Erik Chavez, 2017. "Satellite Data and Machine Learning for Weather Risk Management and Food Security," Risk Analysis, John Wiley & Sons, vol. 37(8), pages 1508-1521, August.
    12. Fan, Cheng & Wang, Jiayuan & Gang, Wenjie & Li, Shenghan, 2019. "Assessment of deep recurrent neural network-based strategies for short-term building energy predictions," Applied Energy, Elsevier, vol. 236(C), pages 700-710.
    13. Hsin-Ning Su & Pei-Chun Lee, 2010. "Mapping knowledge structure by keyword co-occurrence: a first look at journal papers in Technology Foresight," Scientometrics, Springer;Akadémiai Kiadó, vol. 85(1), pages 65-79, October.
    14. Ali, Usman & Shamsi, Mohammad Haris & Bohacek, Mark & Purcell, Karl & Hoare, Cathal & Mangina, Eleni & O’Donnell, James, 2020. "A data-driven approach for multi-scale GIS-based building energy modeling for analysis, planning and support decision making," Applied Energy, Elsevier, vol. 279(C).
    15. Roberto Chiosa & Marco Savino Piscitelli & Alfonso Capozzoli, 2021. "A Data Analytics-Based Energy Information System (EIS) Tool to Perform Meter-Level Anomaly Detection and Diagnosis in Buildings," Energies, MDPI, vol. 14(1), pages 1-28, January.
    16. Ma, Jun & Cheng, Jack C.P., 2016. "Estimation of the building energy use intensity in the urban scale by integrating GIS and big data technology," Applied Energy, Elsevier, vol. 183(C), pages 182-192.
    17. Kamel, Ehsan & Sheikh, Shaya & Huang, Xueqing, 2020. "Data-driven predictive models for residential building energy use based on the segregation of heating and cooling days," Energy, Elsevier, vol. 206(C).
    18. Fahimnia, Behnam & Sarkis, Joseph & Davarzani, Hoda, 2015. "Green supply chain management: A review and bibliometric analysis," International Journal of Production Economics, Elsevier, vol. 162(C), pages 101-114.
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