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A framework for estimating the energy-saving potential of occupant behaviour improvement

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  • He, Zhiyuan
  • Hong, Tianzhen
  • Chou, S.K.

Abstract

Energy-related occupant behaviour in buildings has demonstrated considerable energy-saving potential. However, the current modelling method of occupant behaviour does not give sufficient considerations on the implementation difficulty of behaviour and provide a holistic map from survey data to various behaviour models. This article proposes a holistic survey-and-simulation-based framework for estimating the energy-saving potential of occupant behaviour improvement. In the framework, seven typical categories of occupant behaviour models are identified based on the survey results. According to the implementation difficulty, the models are integrated into four behaviour styles (baseline, wasteful, moderate and austere) to represent different levels of energy-saving consciousness of occupants. Based on a case study with a nationwide survey in Singapore, there are remarkable energy savings potential if occupant behaviour is improved; the building energy consumption can be reduced by up to 9.5% with the moderate behaviour improvement, and up to 21.0% with the aggressive behaviour improvement. The simulation results accord well with the measured results within a reasonable range of deviation. The framework can be applied to estimate the energy-saving potential of occupant behaviour improvement in a building with affordable cost, and the findings can inform a behaviour improvement programme with effective and efficient measures.

Suggested Citation

  • He, Zhiyuan & Hong, Tianzhen & Chou, S.K., 2021. "A framework for estimating the energy-saving potential of occupant behaviour improvement," Applied Energy, Elsevier, vol. 287(C).
    • Handle: RePEc:eee:appene:v:287:y:2021:i:c:s0306261921001343
    DOI: 10.1016/j.apenergy.2021.116591
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    References listed on IDEAS

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    Cited by:

    1. Abolfazl Mohammadabadi & Samira Rahnama & Alireza Afshari, 2022. "Indoor Occupancy Detection Based on Environmental Data Using CNN-XGboost Model: Experimental Validation in a Residential Building," Sustainability, MDPI, vol. 14(21), pages 1-17, November.
    2. Yan, Biao & Yang, Wansheng & He, Fuquan & Zeng, Wenhao, 2023. "Occupant behavior impact in buildings and the artificial intelligence-based techniques and data-driven approach solutions," Renewable and Sustainable Energy Reviews, Elsevier, vol. 184(C).
    3. Varlamis, Iraklis & Sardianos, Christos & Chronis, Christos & Dimitrakopoulos, George & Himeur, Yassine & Alsalemi, Abdullah & Bensaali, Faycal & Amira, Abbes, 2022. "Smart fusion of sensor data and human feedback for personalized energy-saving recommendations," Applied Energy, Elsevier, vol. 305(C).
    4. Xiang, Xiwang & Ma, Minda & Ma, Xin & Chen, Liming & Cai, Weiguang & Feng, Wei & Ma, Zhili, 2022. "Historical decarbonization of global commercial building operations in the 21st century," Applied Energy, Elsevier, vol. 322(C).
    5. Jaqueline Litardo & Ruben Hidalgo-Leon & Guillermo Soriano, 2021. "Energy Performance and Benchmarking for University Classrooms in Hot and Humid Climates," Energies, MDPI, vol. 14(21), pages 1-17, October.
    6. Xingjun Ru & Min Chen & Shanyong Wang & Zhenling Chen, 2022. "Does environmental concern fail to predict energy-saving behavior? A study on the office energy-saving behavior of employees of Chinese Internet companies," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 24(11), pages 12691-12711, November.
    7. Dong, Kangyin & Li, Jiaman & Zhang, Haoran, 2023. "LNG point supply of villages and towns in China: Challenges and countermeasures," Applied Energy, Elsevier, vol. 334(C).
    8. Yang, Xining & Hu, Mingming & Tukker, Arnold & Zhang, Chunbo & Huo, Tengfei & Steubing, Bernhard, 2022. "A bottom-up dynamic building stock model for residential energy transition: A case study for the Netherlands," Applied Energy, Elsevier, vol. 306(PA).

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