IDEAS home Printed from https://ideas.repec.org/a/eee/rensus/v167y2022ics1364032122005937.html
   My bibliography  Save this article

A review on occupancy prediction through machine learning for enhancing energy efficiency, air quality and thermal comfort in the built environment

Author

Listed:
  • Zhang, Wuxia
  • Wu, Yupeng
  • Calautit, John Kaiser

Abstract

The occupants' presence, activities, and behaviour can significantly impact the building's performance and energy efficiency. Currently, heating, ventilation, and air-conditioning (HVAC) systems are often run based on assumed occupancy levels and fixed schedules, or manually set by occupants based on their comfort needs. However, the unpredictability and variability of occupancy patterns can lead to over/under the conditioning of space when using such approaches, affecting indoor air quality and comfort. As a result, machine learning-based models and methodologies are progressively being used to forecast occupancy behaviour and routines in buildings, which may subsequently be used to aid in the design and operation of building systems. The present work reviews recent studies employing machine learning methods to predict occupancy behaviour and patterns, with a special focus on its related applications and benefits to building systems, improving energy efficiency, indoor air quality and thermal comfort. The review provides insight into the workflow of a machine learning-based occupancy prediction model, including data collection, prediction, and validation. An organised evaluation of the applicability or suitability of the different data collection methods, machine learning algorithms, and validation methods was carried out.

Suggested Citation

  • Zhang, Wuxia & Wu, Yupeng & Calautit, John Kaiser, 2022. "A review on occupancy prediction through machine learning for enhancing energy efficiency, air quality and thermal comfort in the built environment," Renewable and Sustainable Energy Reviews, Elsevier, vol. 167(C).
    • Handle: RePEc:eee:rensus:v:167:y:2022:i:c:s1364032122005937
    DOI: 10.1016/j.rser.2022.112704
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S1364032122005937
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.rser.2022.112704?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Fotios Zantalis & Grigorios Koulouras & Sotiris Karabetsos & Dionisis Kandris, 2019. "A Review of Machine Learning and IoT in Smart Transportation," Future Internet, MDPI, vol. 11(4), pages 1-23, April.
    2. Goyal, Siddharth & Barooah, Prabir & Middelkoop, Timothy, 2015. "Experimental study of occupancy-based control of HVAC zones," Applied Energy, Elsevier, vol. 140(C), pages 75-84.
    3. Zhao, Hai-xiang & Magoulès, Frédéric, 2012. "A review on the prediction of building energy consumption," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(6), pages 3586-3592.
    4. Tien, Paige Wenbin & Wei, Shuangyu & Calautit, John Kaiser & Darkwa, Jo & Wood, Christopher, 2022. "Real-time monitoring of occupancy activities and window opening within buildings using an integrated deep learning-based approach for reducing energy demand," Applied Energy, Elsevier, vol. 308(C).
    5. Kong, Meng & Dong, Bing & Zhang, Rongpeng & O'Neill, Zheng, 2022. "HVAC energy savings, thermal comfort and air quality for occupant-centric control through a side-by-side experimental study," Applied Energy, Elsevier, vol. 306(PA).
    6. Chong, Adrian & Augenbroe, Godfried & Yan, Da, 2021. "Occupancy data at different spatial resolutions: Building energy performance and model calibration," Applied Energy, Elsevier, vol. 286(C).
    7. Fan, Cheng & Xiao, Fu & Zhao, Yang, 2017. "A short-term building cooling load prediction method using deep learning algorithms," Applied Energy, Elsevier, vol. 195(C), pages 222-233.
    8. Fu, Hongxiang & Baltazar, Juan-Carlos & Claridge, David E., 2021. "Review of developments in whole-building statistical energy consumption models for commercial buildings," Renewable and Sustainable Energy Reviews, Elsevier, vol. 147(C).
    9. Killian, M. & Zauner, M. & Kozek, M., 2018. "Comprehensive smart home energy management system using mixed-integer quadratic-programming," Applied Energy, Elsevier, vol. 222(C), pages 662-672.
    10. Eric Hitimana & Gaurav Bajpai & Richard Musabe & Louis Sibomana & Jayavel Kayalvizhi, 2021. "Implementation of IoT Framework with Data Analysis Using Deep Learning Methods for Occupancy Prediction in a Building," Future Internet, MDPI, vol. 13(3), pages 1-19, March.
    11. Wei, Yixuan & Xia, Liang & Pan, Song & Wu, Jinshun & Zhang, Xingxing & Han, Mengjie & Zhang, Weiya & Xie, Jingchao & Li, Qingping, 2019. "Prediction of occupancy level and energy consumption in office building using blind system identification and neural networks," Applied Energy, Elsevier, vol. 240(C), pages 276-294.
    12. Zihao Li & Daniel Friedrich & Gareth P. Harrison, 2020. "Demand Forecasting for a Mixed-Use Building Using Agent-Schedule Information with a Data-Driven Model," Energies, MDPI, vol. 13(4), pages 1-20, February.
    13. Jan Vanus & Ojan M. Gorjani & Petr Bilik, 2019. "Novel Proposal for Prediction of CO 2 Course and Occupancy Recognition in Intelligent Buildings within IoT," Energies, MDPI, vol. 12(23), pages 1-25, November.
    14. Yue Yuan & Jisoo Shim & Seungkeon Lee & Doosam Song & Joowook Kim, 2020. "Prediction for Overheating Risk Based on Deep Learning in a Zero Energy Building," Sustainability, MDPI, vol. 12(21), pages 1-20, October.
    15. 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).
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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. John Kaiser Calautit & Hassam Nasarullah Chaudhry, 2022. "Sustainable Buildings: Heating, Ventilation, and Air-Conditioning," Energies, MDPI, vol. 15(21), pages 1-5, November.
    3. Yue, Naihua & Caini, Mauro & Li, Lingling & Zhao, Yang & Li, Yu, 2023. "A comparison of six metamodeling techniques applied to multi building performance vectors prediction on gymnasiums under multiple climate conditions," Applied Energy, Elsevier, vol. 332(C).
    4. Hidayatus Sibyan & Jozef Svajlenka & Hermawan Hermawan & Nasyiin Faqih & Annisa Nabila Arrizqi, 2022. "Thermal Comfort Prediction Accuracy with Machine Learning between Regression Analysis and Naïve Bayes Classifier," Sustainability, MDPI, vol. 14(23), pages 1-18, November.
    5. Muhammad Emad-Ud-Din & Ya Wang, 2023. "Indoor Occupancy Sensing via Networked Nodes (2012–2022): A Review," Future Internet, MDPI, vol. 15(3), pages 1-20, March.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Li, Tao & Liu, Xiangyu & Li, Guannan & Wang, Xing & Ma, Jiangqiaoyu & Xu, Chengliang & Mao, Qianjun, 2024. "A systematic review and comprehensive analysis of building occupancy prediction," Renewable and Sustainable Energy Reviews, Elsevier, vol. 193(C).
    2. Venkatraj, V. & Dixit, M.K., 2022. "Challenges in implementing data-driven approaches for building life cycle energy assessment: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 160(C).
    3. Wang, Zhe & Hong, Tianzhen & Piette, Mary Ann, 2020. "Building thermal load prediction through shallow machine learning and deep learning," Applied Energy, Elsevier, vol. 263(C).
    4. Jonas Bielskus & Violeta Motuzienė & Tatjana Vilutienė & Audrius Indriulionis, 2020. "Occupancy Prediction Using Differential Evolution Online Sequential Extreme Learning Machine Model," Energies, MDPI, vol. 13(15), pages 1-20, August.
    5. Wang, Zeyu & Liu, Jian & Zhang, Yuanxin & Yuan, Hongping & Zhang, Ruixue & Srinivasan, Ravi S., 2021. "Practical issues in implementing machine-learning models for building energy efficiency: Moving beyond obstacles," Renewable and Sustainable Energy Reviews, Elsevier, vol. 143(C).
    6. Gianluca Serale & Massimo Fiorentini & Alfonso Capozzoli & Daniele Bernardini & Alberto Bemporad, 2018. "Model Predictive Control (MPC) for Enhancing Building and HVAC System Energy Efficiency: Problem Formulation, Applications and Opportunities," Energies, MDPI, vol. 11(3), pages 1-35, March.
    7. Wang, Ran & Lu, Shilei & Feng, Wei, 2020. "A novel improved model for building energy consumption prediction based on model integration," Applied Energy, Elsevier, vol. 262(C).
    8. Li, Sihui & Gong, Guangcai & Peng, Jinqing, 2019. "Dynamic coupling method between air-source heat pumps and buildings in China’s hot-summer/cold-winter zone," Applied Energy, Elsevier, vol. 254(C).
    9. Chitalia, Gopal & Pipattanasomporn, Manisa & Garg, Vishal & Rahman, Saifur, 2020. "Robust short-term electrical load forecasting framework for commercial buildings using deep recurrent neural networks," Applied Energy, Elsevier, vol. 278(C).
    10. Li, Sihui & Peng, Jinqing & Zou, Bin & Li, Bojia & Lu, Chujie & Cao, Jingyu & Luo, Yimo & Ma, Tao, 2021. "Zero energy potential of photovoltaic direct-driven air conditioners with considering the load flexibility of air conditioners," Applied Energy, Elsevier, vol. 304(C).
    11. Wang, Lan & Lee, Eric W.M. & Yuen, Richard K.K., 2018. "Novel dynamic forecasting model for building cooling loads combining an artificial neural network and an ensemble approach," Applied Energy, Elsevier, vol. 228(C), pages 1740-1753.
    12. Haizhou Fang & Hongwei Tan & Ningfang Dai & Zhaohui Liu & Risto Kosonen, 2023. "Hourly Building Energy Consumption Prediction Using a Training Sample Selection Method Based on Key Feature Search," Sustainability, MDPI, vol. 15(9), pages 1-23, May.
    13. Razak Olu-Ajayi & Hafiz Alaka & Hakeem Owolabi & Lukman Akanbi & Sikiru Ganiyu, 2023. "Data-Driven Tools for Building Energy Consumption Prediction: A Review," Energies, MDPI, vol. 16(6), pages 1-20, March.
    14. Xiao, Tong & Xu, Peng & He, Ruikai & Sha, Huajing, 2022. "Status quo and opportunities for building energy prediction in limited data Context—Overview from a competition," Applied Energy, Elsevier, vol. 305(C).
    15. Bampoulas, Adamantios & Pallonetto, Fabiano & Mangina, Eleni & Finn, Donal P., 2022. "An ensemble learning-based framework for assessing the energy flexibility of residential buildings with multicomponent energy systems," Applied Energy, Elsevier, vol. 315(C).
    16. Jason Runge & Radu Zmeureanu, 2021. "A Review of Deep Learning Techniques for Forecasting Energy Use in Buildings," Energies, MDPI, vol. 14(3), pages 1-26, January.
    17. Zhang, Xu & Sun, Yongjun & Gao, Dian-ce & Zou, Wenke & Fu, Jianping & Ma, Xiaowen, 2022. "Similarity-based grouping method for evaluation and optimization of dataset structure in machine-learning based short-term building cooling load prediction without measurable occupancy information," Applied Energy, Elsevier, vol. 327(C).
    18. Li, Xinyi & Yao, Runming, 2020. "A machine-learning-based approach to predict residential annual space heating and cooling loads considering occupant behaviour," Energy, Elsevier, vol. 212(C).
    19. Zhang, Xiaofeng & Kong, Xiaoying & Yan, Renshi & Liu, Yuting & Xia, Peng & Sun, Xiaoqin & Zeng, Rong & Li, Hongqiang, 2023. "Data-driven cooling, heating and electrical load prediction for building integrated with electric vehicles considering occupant travel behavior," Energy, Elsevier, vol. 264(C).
    20. Zhang, Liang & Wen, Jin & Li, Yanfei & Chen, Jianli & Ye, Yunyang & Fu, Yangyang & Livingood, William, 2021. "A review of machine learning in building load prediction," Applied Energy, Elsevier, vol. 285(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:rensus:v:167:y:2022:i:c:s1364032122005937. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/600126/description#description .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.