IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v15y2022i9p3112-d801394.html
   My bibliography  Save this article

Suitability Evaluation of Different Measured Variables to Assess the Occupancy Patterns of a Building: Analysis of a Classroom of a School in Madrid during the COVID-19 Pandemic

Author

Listed:
  • María Nuria Sánchez

    (Energy Efficiency in Buildings Unit, Department of Energy, CIEMAT, E-28040 Madrid, Spain)

  • Emanuela Giancola

    (Energy Efficiency in Buildings Unit, Department of Energy, CIEMAT, E-28040 Madrid, Spain)

  • Silvia Soutullo

    (Energy Efficiency in Buildings Unit, Department of Energy, CIEMAT, E-28040 Madrid, Spain)

  • Ana Rosa Gamarra

    (Energy Systems Analysis Unit, Department of Energy, CIEMAT, E-28040 Madrid, Spain)

  • Rafael Olmedo

    (Energy Efficiency in Buildings Unit, Department of Energy, CIEMAT, E-28040 Madrid, Spain)

  • José Antonio Ferrer

    (Energy Efficiency in Buildings Unit, Department of Energy, CIEMAT, E-28040 Madrid, Spain)

  • María José Jiménez

    (Energy Efficiency in Buildings Unit, Department of Energy, CIEMAT, E-28040 Madrid, Spain
    Plataforma Solar de Almería, CIEMAT, Carretera de Senés s/n, Tabernas, E-04200 Almería, Spain)

Abstract

Building occupancy is one of the relevant variables to understand the energy performance of buildings and to reduce the current gap between simulation-based and actual energy performance. In this study, the occupancy of a classroom in an educational center monitored over a full year was experimentally assessed. The classroom had different occupancy levels during the school year, with a theoretical minimum of eleven students, and no occupancy during vacations and weekends. Different variables such as indoor air temperature, relative humidity, CO 2 concentration, overall electrical energy consumption of the educational center, electrical energy consumption of the building in which the monitored classroom is located, and heating energy consumption were recorded. We analyzed which of these variables were possible indicators of classroom occupancy, using the school timetable as a theoretical reference value for the validation of the results. Based on previous studies, one-hour moving averages are used to better identify the occupancy patterns by smoothing the fluctuations that are not a consequence of a change in the classroom occupancy. Histograms of each variable are used to identify the variable ranges associated within the occupancy: occupied or empty. The concentration of CO 2 and electric measurements, identified in previous works as suitable to assess the occupancy patterns of rooms like offices with lower levels of occupancy, are recognized as potential occupancy indicators. It is therefore concluded that a higher level of space occupancy does not affect the result, and the same variables are identified as potential occupancy indicators.

Suggested Citation

  • María Nuria Sánchez & Emanuela Giancola & Silvia Soutullo & Ana Rosa Gamarra & Rafael Olmedo & José Antonio Ferrer & María José Jiménez, 2022. "Suitability Evaluation of Different Measured Variables to Assess the Occupancy Patterns of a Building: Analysis of a Classroom of a School in Madrid during the COVID-19 Pandemic," Energies, MDPI, vol. 15(9), pages 1-15, April.
  • Handle: RePEc:gam:jeners:v:15:y:2022:i:9:p:3112-:d:801394
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/15/9/3112/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/15/9/3112/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Sánchez, M.N. & Soutullo, S. & Olmedo, R. & Bravo, D. & Castaño, S. & Jiménez, M.J., 2020. "An experimental methodology to assess the climate impact on the energy performance of buildings: A ten-year evaluation in temperate and cold desert areas," Applied Energy, Elsevier, vol. 264(C).
    2. 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.
    3. Boštjan Aver & Ajda Fošner & Nikša Alfirević, 2021. "Higher Education Challenges: Developing Skills to Address Contemporary Economic and Sustainability Issues," Sustainability, MDPI, vol. 13(22), pages 1-20, November.
    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. Luis Mayor & Line F. Lindner & Christoph F. Knöbl & Ana Ramalho & Remigio Berruto & Francesca Sanna & Daniele Rossi & Camilla Tomao & Billy Goodburn & Concha Avila & Marg Leijdens & Katharina Stollewe, 2022. "Skill Needs for Sustainable Agri-Food and Forestry Sectors (I): Assessment through European and National Focus Groups," Sustainability, MDPI, vol. 14(15), pages 1-24, August.
    2. Himeur, Yassine & Alsalemi, Abdullah & Bensaali, Faycal & Amira, Abbes, 2020. "Effective non-intrusive load monitoring of buildings based on a novel multi-descriptor fusion with dimensionality reduction," Applied Energy, Elsevier, vol. 279(C).
    3. Muhammad Setiawan Kusmulyono & Wawan Dhewanto & Melia Famiola, 2023. "Energizing Higher Education Sustainability through Rural-Community Development Activation," Sustainability, MDPI, vol. 15(3), pages 1-13, January.
    4. Silvia Soutullo & Emanuela Giancola & María Nuria Sánchez & José Antonio Ferrer & David García & María José Súarez & Jesús Ignacio Prieto & Elena Antuña-Yudego & Juan Luís Carús & Miguel Ángel Fernánd, 2020. "Methodology for Quantifying the Energy Saving Potentials Combining Building Retrofitting, Solar Thermal Energy and Geothermal Resources," Energies, MDPI, vol. 13(22), pages 1-25, November.
    5. 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).
    6. 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.
    7. Jana Stofkova & Adela Poliakova & Katarina Repkova Stofkova & Peter Malega & Matej Krejnus & Vladimira Binasova & Naqibullah Daneshjo, 2022. "Digital Skills as a Significant Factor of Human Resources Development," Sustainability, MDPI, vol. 14(20), pages 1-18, October.
    8. Wei, Shuangyu & Tien, Paige Wenbin & Calautit, John Kaiser & Wu, Yupeng & Boukhanouf, Rabah, 2020. "Vision-based detection and prediction of equipment heat gains in commercial office buildings using a deep learning method," Applied Energy, Elsevier, vol. 277(C).
    9. 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).
    10. Himeur, Yassine & Alsalemi, Abdullah & Bensaali, Faycal & Amira, Abbes, 2020. "Robust event-based non-intrusive appliance recognition using multi-scale wavelet packet tree and ensemble bagging tree," Applied Energy, Elsevier, vol. 267(C).
    11. 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).
    12. Salins, Sampath Suranjan & Kota Reddy, S.V. & Shiva Kumar,, 2021. "Experimental Investigation and Neural network based parametric prediction in a multistage reciprocating humidifier," Applied Energy, Elsevier, vol. 293(C).
    13. Silvia Soutullo & Laura Aelenei & Per Sieverts Nielsen & Jose Antonio Ferrer & Helder Gonçalves, 2020. "Testing Platforms as Drivers for Positive-Energy Living Laboratories," Energies, MDPI, vol. 13(21), pages 1-21, October.
    14. William Mounter & Chris Ogwumike & Huda Dawood & Nashwan Dawood, 2021. "Machine Learning and Data Segmentation for Building Energy Use Prediction—A Comparative Study," Energies, MDPI, vol. 14(18), pages 1-42, September.
    15. 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).
    16. Fernanda Spada Villar & Pedro Henrique Juliano Nardelli & Arun Narayanan & Renan Cipriano Moioli & Hader Azzini & Luiz Carlos Pereira da Silva, 2021. "Noninvasive Detection of Appliance Utilization Patterns in Residential Electricity Demand," Energies, MDPI, vol. 14(6), pages 1-23, March.
    17. 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).
    18. Mirfin, Anthony & Xiao, Xun & Jack, Michael W., 2024. "TOWST: A physics-informed statistical model for building energy consumption with solar gain," Applied Energy, Elsevier, vol. 369(C).
    19. Jallal, Mohammed Ali & González-Vidal, Aurora & Skarmeta, Antonio F. & Chabaa, Samira & Zeroual, Abdelouhab, 2020. "A hybrid neuro-fuzzy inference system-based algorithm for time series forecasting applied to energy consumption prediction," Applied Energy, Elsevier, vol. 268(C).
    20. Yuwen You & Zhonghua Wang & Zhihao Liu & Chunmei Guo & Bin Yang, 2024. "Load Prediction of Regional Heat Exchange Station Based on Fuzzy Clustering Based on Fourier Distance and Convolutional Neural Network–Bidirectional Long Short-Term Memory Network," Energies, MDPI, vol. 17(16), pages 1-19, August.

    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:9:p:3112-:d:801394. 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.