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Planning for sustainable cities by estimating building occupancy with mobile phones

Author

Listed:
  • Edward Barbour

    (MIT
    Loughborough University
    Lawrence Berkeley National Laboratory)

  • Carlos Cerezo Davila

    (MIT)

  • Siddharth Gupta

    (MIT)

  • Christoph Reinhart

    (MIT)

  • Jasleen Kaur

    (Signify Research North America (formerly Philips Lighting))

  • Marta C. González

    (MIT
    Lawrence Berkeley National Laboratory
    Department of City and Regional Planning, UC)

Abstract

Accurate occupancy is crucial for planning for sustainable buildings. Using massive, passively-collected mobile phone data, we introduce a novel framework to estimate building occupancy at unprecedented scale. We show that, at urban-scale, occupancy differs widely from current estimates based on building types. For commercial buildings, we find typical occupancy rates are 5 times lower than current assumptions imply, while for residential buildings occupancy rates vary widely by neighborhood. Our mobile phone based occupancy estimates are integrated with a state-of-the-art urban building energy model to understand their impact on energy use predictions. Depending on the assumed relationship between occupancy and internal building loads, we find energy consumption which differs by +1% to −15% for residential buildings and by −4% to −21% for commercial buildings, compared to standard methods. This highlights a need for new occupancy-to-load models which can be applied at urban-scale to the diverse set of city building types.

Suggested Citation

  • Edward Barbour & Carlos Cerezo Davila & Siddharth Gupta & Christoph Reinhart & Jasleen Kaur & Marta C. González, 2019. "Planning for sustainable cities by estimating building occupancy with mobile phones," Nature Communications, Nature, vol. 10(1), pages 1-10, December.
    • Handle: RePEc:nat:natcom:v:10:y:2019:i:1:d:10.1038_s41467-019-11685-w
    DOI: 10.1038/s41467-019-11685-w
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    Citations

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    Cited by:

    1. Wu, Wenbo & Dong, Bing & Wang, Qi (Ryan) & Kong, Meng & Yan, Da & An, Jingjing & Liu, Yapan, 2020. "A novel mobility-based approach to derive urban-scale building occupant profiles and analyze impacts on building energy consumption," Applied Energy, Elsevier, vol. 278(C).
    2. Fu, Chun & Miller, Clayton, 2022. "Using Google Trends as a proxy for occupant behavior to predict building energy consumption," Applied Energy, Elsevier, vol. 310(C).
    3. Bianchi, Carlo & Zhang, Liang & Goldwasser, David & Parker, Andrew & Horsey, Henry, 2020. "Modeling occupancy-driven building loads for large and diversified building stocks through the use of parametric schedules," Applied Energy, Elsevier, vol. 276(C).
    4. Dong, Bing & Liu, Yapan & Fontenot, Hannah & Ouf, Mohamed & Osman, Mohamed & Chong, Adrian & Qin, Shuxu & Salim, Flora & Xue, Hao & Yan, Da & Jin, Yuan & Han, Mengjie & Zhang, Xingxing & Azar, Elie & , 2021. "Occupant behavior modeling methods for resilient building design, operation and policy at urban scale: A review," Applied Energy, Elsevier, vol. 293(C).
    5. Roth, Jonathan & Martin, Amory & Miller, Clayton & Jain, Rishee K., 2020. "SynCity: Using open data to create a synthetic city of hourly building energy estimates by integrating data-driven and physics-based methods," Applied Energy, Elsevier, vol. 280(C).
    6. Bass, Brett & New, Joshua & Clinton, Nicholas & Adams, Mark & Copeland, Bill & Amoo, Charles, 2022. "How close are urban scale building simulations to measured data? Examining bias derived from building metadata in urban building energy modeling," Applied Energy, Elsevier, vol. 327(C).
    7. Martín Mosteiro-Romero & Arno Schlueter, 2021. "Effects of Occupants and Local Air Temperatures as Sources of Stochastic Uncertainty in District Energy System Modeling," Energies, MDPI, vol. 14(8), pages 1-30, April.
    8. Lee, Zachary E. & Max Zhang, K., 2022. "Unintended consequences of smart thermostats in the transition to electrified heating," Applied Energy, Elsevier, vol. 322(C).
    9. Marie Urban & Robert Stewart & Scott Basford & Zachary Palmer & Jason Kaufman, 2023. "Estimating building occupancy: a machine learning system for day, night, and episodic events," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 116(2), pages 2417-2436, March.
    10. Zhang, Xingxing & Pellegrino, Filippo & Shen, Jingchun & Copertaro, Benedetta & Huang, Pei & Kumar Saini, Puneet & Lovati, Marco, 2020. "A preliminary simulation study about the impact of COVID-19 crisis on energy demand of a building mix at a district in Sweden," Applied Energy, Elsevier, vol. 280(C).
    11. Prataviera, Enrico & Vivian, Jacopo & Lombardo, Giulia & Zarrella, Angelo, 2022. "Evaluation of the impact of input uncertainty on urban building energy simulations using uncertainty and sensitivity analysis," Applied Energy, Elsevier, vol. 311(C).

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