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Occupancy data at different spatial resolutions: Building energy performance and model calibration

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  • Chong, Adrian
  • Augenbroe, Godfried
  • Yan, Da

Abstract

Occupancy is a significant area of interest within the field of building performance simulation. Through Bayesian calibration, the present study investigates the impact of the availability of different spatial resolution of occupancy data on the gap between predicted and measured energy use in buildings. The study also examines the effect of occupancy data on the quality of the constructed prediction intervals (PIs) using the Coverage Width-based Criterion (CWC) metric. CWC evaluates the PIs based on both their coverage (correctness) and width (informativeness). This investigation takes the form of an actual building case study, with nine months of hourly measured building electricity use, WiFi connection counts as a proxy for occupancy, and actual weather data. In general, the building energy model’s accuracy improves with the occupancy and plug-loads schedule derived from WiFi data. Specifically, the Coefficient of Variation Root Mean Square Error (CV[RMSE]) reduced from 37% to 24% with an exponential improvement in the PIs quality compared to the results obtained with ASHRAE 90.1 reference schedules. However, the increase in prediction accuracy shrank to 5% CV(RMSE) and a comparable CWC upon calibrating the base loads of the reference schedules. Increasing the spatial resolution from building aggregated to floor aggregated occupancy data worsened the CV(RMSE) and CWC, suggesting trade-offs between parameter uncertainty and model bias/inadequacy. These results contribute to our understanding of the interactions between model complexity, simulation objectives, and data informativeness, facilitating future discussions on the right level of abstraction when modeling occupancy.

Suggested Citation

  • 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).
  • Handle: RePEc:eee:appene:v:286:y:2021:i:c:s0306261921000532
    DOI: 10.1016/j.apenergy.2021.116492
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    References listed on IDEAS

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    1. Jia, Mengda & Srinivasan, Ravi S. & Raheem, Adeeba A., 2017. "From occupancy to occupant behavior: An analytical survey of data acquisition technologies, modeling methodologies and simulation coupling mechanisms for building energy efficiency," Renewable and Sustainable Energy Reviews, Elsevier, vol. 68(P1), pages 525-540.
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    5. 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).
    6. S Robinson, 2008. "Conceptual modelling for simulation Part II: a framework for conceptual modelling," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 59(3), pages 291-304, March.
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    Cited by:

    1. 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).
    2. Zhan, Sicheng & Lei, Yue & Jin, Yuan & Yan, Da & Chong, Adrian, 2022. "Impact of occupant related data on identification and model predictive control for buildings," Applied Energy, Elsevier, vol. 323(C).
    3. 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).
    4. Bampoulas, Adamantios & Pallonetto, Fabiano & Mangina, Eleni & Finn, Donal P., 2023. "A Bayesian deep-learning framework for assessing the energy flexibility of residential buildings with multicomponent energy systems," Applied Energy, Elsevier, vol. 348(C).
    5. Zhan, Sicheng & Chong, Adrian, 2021. "Data requirements and performance evaluation of model predictive control in buildings: A modeling perspective," Renewable and Sustainable Energy Reviews, Elsevier, vol. 142(C).
    6. Nweye, Kingsley & Nagy, Zoltan, 2022. "MARTINI: Smart meter driven estimation of HVAC schedules and energy savings based on Wi-Fi sensing and clustering," Applied Energy, Elsevier, vol. 316(C).
    7. Kristina Vassiljeva & Margarita Matson & Andrea Ferrantelli & Eduard Petlenkov & Martin Thalfeldt & Juri Belikov, 2024. "Data-Driven Occupancy Profile Identification and Application to the Ventilation Schedule in a School Building," Energies, MDPI, vol. 17(13), pages 1-23, June.
    8. 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).

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