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A novel mobility-based approach to derive urban-scale building occupant profiles and analyze impacts on building energy consumption

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  • Wu, Wenbo
  • Dong, Bing
  • Wang, Qi (Ryan)
  • Kong, Meng
  • Yan, Da
  • An, Jingjing
  • Liu, Yapan

Abstract

In the US, people spend more than 90% of their time in buildings, which contributes to more than 70% of overall electricity usage in the country. Occupant behavior is becoming a leading factor impacting energy consumption in buildings. Existing occupant-behavior studies are often limited to a single building and individual behavior, such as presence or interactions in confined spaces. Moreover, studies modeling occupant behavior at the building or community level are limited. With the development of the Internet of Things, mobile positioning data are available through social media and location-based service applications. The goal of this study is to analyze the impacts of more representative occupancy profiles, derived from high resolution urban scale mobile position data, on building energy consumption. . A pilot study was conducted on more than 900 buildings in downtown San Antonio, Texas, with billions of mobile positioning data. We then compared these profiles with the existing Department of Energy prototype models and quantified the differences using a statistical method. On average, the differences in occupancy rates between the ones derived from the empirical profile and the ones from the Department of Energy reference ranged from −30% to 70%. The realistic derived profiles are then simulated in the CityBES. The results show that the predicted cooling energy demand is reduced by up to 40% while the heating energy demand is reduced by up to 60%. This study, therefore, advances knowledge of urban planning as well as urban-scale energy modeling and optimization.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:appene:v:278:y:2020:i:c:s0306261920311545
    DOI: 10.1016/j.apenergy.2020.115656
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    2. Shen, Rendong & Zhong, Shengyuan & Wen, Xin & An, Qingsong & Zheng, Ruifan & Li, Yang & Zhao, Jun, 2022. "Multi-agent deep reinforcement learning optimization framework for building energy system with renewable energy," Applied Energy, Elsevier, vol. 312(C).
    3. Hu, Yuqing & Cheng, Xiaoyuan & Wang, Suhang & Chen, Jianli & Zhao, Tianxiang & Dai, Enyan, 2022. "Times series forecasting for urban building energy consumption based on graph convolutional network," Applied Energy, Elsevier, vol. 307(C).
    4. Gao, Yuan & Matsunami, Yuki & Miyata, Shohei & Akashi, Yasunori, 2022. "Multi-agent reinforcement learning dealing with hybrid action spaces: A case study for off-grid oriented renewable building energy system," Applied Energy, Elsevier, vol. 326(C).
    5. Zhou, Yuekuan & Zheng, Siqian, 2024. "A co-simulated material-component-system-district framework for climate-adaption and sustainability transition," Renewable and Sustainable Energy Reviews, Elsevier, vol. 192(C).
    6. Piselli, Cristina & Salvadori, Giacomo & Diciotti, Lorenzo & Fantozzi, Fabio & Pisello, Anna Laura, 2021. "Assessing users’ willingness-to-engagement towards Net Zero Energy communities in Italy," Renewable and Sustainable Energy Reviews, Elsevier, vol. 152(C).
    7. Razak Olu-Ajayi & Hafiz Alaka & Christian Egwim & Ketty Grishikashvili, 2024. "Comprehensive Analysis of Influencing Factors on Building Energy Performance and Strategic Insights for Sustainable Development: A Systematic Literature Review," Sustainability, MDPI, vol. 16(12), pages 1-27, June.
    8. 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).
    9. Yamaguchi, Yohei & Shoda, Yuto & Yoshizawa, Shinya & Imai, Tatsuya & Perwez, Usama & Shimoda, Yoshiyuki & Hayashi, Yasuhiro, 2023. "Feasibility assessment of net zero-energy transformation of building stock using integrated synthetic population, building stock, and power distribution network framework," Applied Energy, Elsevier, vol. 333(C).
    10. Alessia Banfi & Martina Ferrando & Peixian Li & Xing Shi & Francesco Causone, 2024. "Integrating Occupant Behaviour into Urban-Building Energy Modelling: A Review of Current Practices and Challenges," Energies, MDPI, vol. 17(17), pages 1-28, September.

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