IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v15y2022i16p5993-d891893.html
   My bibliography  Save this article

Prediction of Occupant Behavior toward Natural Ventilation in Japanese Dwellings: Machine Learning Models and Feature Selection

Author

Listed:
  • Kaito Furuhashi

    (Faculty of Engineering, Department of Architecture, Shinshu University, Nagano 380-0928, Japan)

  • Takashi Nakaya

    (Faculty of Engineering, Department of Architecture, Shinshu University, Nagano 380-0928, Japan)

  • Yoshihiro Maeda

    (Faculty of Engineering, Department of Electrical Engineering, Tokyo University of Science (TUS), Tokyo 125-8585, Japan)

Abstract

Occupant behavior based on natural ventilation has a significant impact on building energy consumption. It is important for the quantification of occupant-behavior models to select observed variables, i.e., features that affect the state of window opening and closing, and to consider machine learning models that are effective in predicting this state. In this study, thermal comfort was investigated, and machine learning data were analyzed for 30 houses in Gifu, Japan. Among the selected machine learning models, the logistic regression and deep neural network models produced consistently excellent results. The accuracy of the prediction of open and closed windows differed among the models, and the factors influencing the window-opening behaviors of the occupants differed from those influencing their window-closing behavior. In the selection of features, the analysis using thermal indices representative of the room and cooling features showed excellent results, indicating that cooling features, which have conflicting relationships with natural ventilation, are useful for improving the accuracy of occupant-behavior prediction. The present study indicates that building designers should incorporate occupant behavior based on natural ventilation into their designs.

Suggested Citation

  • Kaito Furuhashi & Takashi Nakaya & Yoshihiro Maeda, 2022. "Prediction of Occupant Behavior toward Natural Ventilation in Japanese Dwellings: Machine Learning Models and Feature Selection," Energies, MDPI, vol. 15(16), pages 1-26, August.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:16:p:5993-:d:891893
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/15/16/5993/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/15/16/5993/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Fathi, Soheil & Srinivasan, Ravi & Fenner, Andriel & Fathi, Sahand, 2020. "Machine learning applications in urban building energy performance forecasting: A systematic review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 133(C).
    Full references (including those not matched with items on IDEAS)

    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. Coyne, Bryan & Denny, Eleanor, 2021. "Retrofit effectiveness: Evidence from a nationwide residential energy efficiency programme," Energy Policy, Elsevier, vol. 159(C).
    2. Wang, Qiang & Li, Shuyu & Zhang, Min & Li, Rongrong, 2022. "Impact of COVID-19 pandemic on oil consumption in the United States: A new estimation approach," Energy, Elsevier, vol. 239(PC).
    3. Hernandez-Matheus, Alejandro & Löschenbrand, Markus & Berg, Kjersti & Fuchs, Ida & Aragüés-Peñalba, Mònica & Bullich-Massagué, Eduard & Sumper, Andreas, 2022. "A systematic review of machine learning techniques related to local energy communities," Renewable and Sustainable Energy Reviews, Elsevier, vol. 170(C).
    4. Kapp, Sean & Choi, Jun-Ki & Hong, Taehoon, 2023. "Predicting industrial building energy consumption with statistical and machine-learning models informed by physical system parameters," Renewable and Sustainable Energy Reviews, Elsevier, vol. 172(C).
    5. Amini Toosi, Hashem & Del Pero, Claudio & Leonforte, Fabrizio & Lavagna, Monica & Aste, Niccolò, 2023. "Machine learning for performance prediction in smart buildings: Photovoltaic self-consumption and life cycle cost optimization," Applied Energy, Elsevier, vol. 334(C).
    6. 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).
    7. 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.
    8. 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).
    9. Lu, Chujie & Li, Sihui & Reddy Penaka, Santhan & Olofsson, Thomas, 2023. "Automated machine learning-based framework of heating and cooling load prediction for quick residential building design," Energy, Elsevier, vol. 274(C).
    10. Abokersh, Mohamed Hany & Gangwar, Sachin & Spiekman, Marleen & Vallès, Manel & Jiménez, Laureano & Boer, Dieter, 2021. "Sustainability insights on emerging solar district heating technologies to boost the nearly zero energy building concept," Renewable Energy, Elsevier, vol. 180(C), pages 893-913.
    11. Felix Biessmann & Bhaskar Kamble & Rita Streblow, 2023. "An Automated Machine Learning Approach towards Energy Saving Estimates in Public Buildings," Energies, MDPI, vol. 16(19), pages 1-12, September.
    12. Mohammad Mahdi Forootan & Iman Larki & Rahim Zahedi & Abolfazl Ahmadi, 2022. "Machine Learning and Deep Learning in Energy Systems: A Review," Sustainability, MDPI, vol. 14(8), pages 1-49, April.
    13. Jiaxin Zhang & Zhilin Yu & Yunqin Li & Xueqiang Wang, 2023. "Uncovering Bias in Objective Mapping and Subjective Perception of Urban Building Functionality: A Machine Learning Approach to Urban Spatial Perception," Land, MDPI, vol. 12(7), pages 1-20, June.
    14. Ahmad, Tanveer & Madonski, Rafal & Zhang, Dongdong & Huang, Chao & Mujeeb, Asad, 2022. "Data-driven probabilistic machine learning in sustainable smart energy/smart energy systems: Key developments, challenges, and future research opportunities in the context of smart grid paradigm," Renewable and Sustainable Energy Reviews, Elsevier, vol. 160(C).
    15. Ahmad, Tanveer & Zhang, Dongdong & Huang, Chao, 2021. "Methodological framework for short-and medium-term energy, solar and wind power forecasting with stochastic-based machine learning approach to monetary and energy policy applications," Energy, Elsevier, vol. 231(C).
    16. Jiang, Feifeng & Ma, Jun & Li, Zheng & Ding, Yuexiong, 2022. "Prediction of energy use intensity of urban buildings using the semi-supervised deep learning model," Energy, Elsevier, vol. 249(C).
    17. Jin, Xin & Zhang, Huihui & Huang, Gongsheng & Lai, Alvin CK., 2021. "Experimental investigation on the dynamic thermal performance of the parallel solar-assisted air-source heat pump latent heat thermal energy storage system," Renewable Energy, Elsevier, vol. 180(C), pages 637-657.
    18. Kangji Li & Borui Wei & Qianqian Tang & Yufei Liu, 2022. "A Data-Efficient Building Electricity Load Forecasting Method Based on Maximum Mean Discrepancy and Improved TrAdaBoost Algorithm," Energies, MDPI, vol. 15(23), pages 1-18, November.
    19. Karol Bot & Samira Santos & Inoussa Laouali & Antonio Ruano & Maria da Graça Ruano, 2021. "Design of Ensemble Forecasting Models for Home Energy Management Systems," Energies, MDPI, vol. 14(22), pages 1-37, November.
    20. Oraiopoulos, A. & Howard, B., 2022. "On the accuracy of Urban Building Energy Modelling," Renewable and Sustainable Energy Reviews, Elsevier, vol. 158(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:gam:jeners:v:15:y:2022:i:16:p:5993-:d:891893. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.