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Meta-modeling of occupancy variables and analysis of their impact on energy outcomes of office buildings

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  • Wang, Qinpeng
  • Augenbroe, Godfried
  • Kim, Ji-Hyun
  • Gu, Li

Abstract

Occupants interact with buildings in various ways via their presence (passive effects) and control actions (active effects). Therefore, understanding the influence of occupants is essential if we are to evaluate the performance of a building. In this paper, we model the mean profiles and variability of occupancy variables (presence and actions) separately. We will use a multi-variate Gaussian distribution to generate mean profiles of occupancy variables, while the variability will be represented by a multi-dimensional time series model, within a framework for a meta-analysis that synthesizes occupancy data gathered from a pool of buildings. We then discuss variants of occupancy models with respect to various outcomes of interest such as HVAC energy consumption and peak demand behavior via a sensitivity analysis. Results show that our approach is able to generate stochastic occupancy profiles, requiring minimum additional input from the energy modeler other than standard diversity profiles. Along with the meta-analysis, we enable the generalization of previous research results and statistical inferences to choose occupancy variables for future buildings. The sensitivity analysis shows that for aggregated building energy consumption, occupant presence has a smaller impact compared to lighting and appliance usage. Specifically, being accumulatively 55% wrong with regard to presence, only translates to 2% error in aggregated cooling energy in July and 3.6% error in heating energy in January. Such a finding redirects focus to the accurate estimation of lighting and appliance usage for a better prediction of aggregated energy consumption. Furthermore, it proves that accurate knowledge of the mean profiles is sufficient, that is, stochastic occupancy models do not play a significant role in the prediction of aggregated consumption in a conventional office building where the interaction between the operation of building systems and the spatial and temporal variability of occupancy is weak. When it comes to peak demand behavior, occupancy variability should be taken into account, as static profiles are not able to produce adequate estimates of power duration probabilities close to the power peak.

Suggested Citation

  • Wang, Qinpeng & Augenbroe, Godfried & Kim, Ji-Hyun & Gu, Li, 2016. "Meta-modeling of occupancy variables and analysis of their impact on energy outcomes of office buildings," Applied Energy, Elsevier, vol. 174(C), pages 166-180.
    • Handle: RePEc:eee:appene:v:174:y:2016:i:c:p:166-180
    DOI: 10.1016/j.apenergy.2016.04.062
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    References listed on IDEAS

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    1. Menezes, Anna Carolina & Cripps, Andrew & Bouchlaghem, Dino & Buswell, Richard, 2012. "Predicted vs. actual energy performance of non-domestic buildings: Using post-occupancy evaluation data to reduce the performance gap," Applied Energy, Elsevier, vol. 97(C), pages 355-364.
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    2. Carstens, Herman & Xia, Xiaohua & Yadavalli, Sarma, 2018. "Measurement uncertainty in energy monitoring: Present state of the art," Renewable and Sustainable Energy Reviews, Elsevier, vol. 82(P3), pages 2791-2805.
    3. Wang, Wei & Chen, Jiayu & Huang, Gongsheng & Lu, Yujie, 2017. "Energy efficient HVAC control for an IPS-enabled large space in commercial buildings through dynamic spatial occupancy distribution," Applied Energy, Elsevier, vol. 207(C), pages 305-323.
    4. Kim, Yang-Seon & Heidarinejad, Mohammad & Dahlhausen, Matthew & Srebric, Jelena, 2017. "Building energy model calibration with schedules derived from electricity use data," Applied Energy, Elsevier, vol. 190(C), pages 997-1007.
    5. Lin Yang & Sha Liu & Jiaqi Liu, 2021. "The Interaction Effect of Occupant Behavior-Related Factors in Office Buildings Based on the DNAS Theory," Sustainability, MDPI, vol. 13(6), pages 1-25, March.
    6. Piselli, Cristina & Pisello, Anna Laura, 2019. "Occupant behavior long-term continuous monitoring integrated to prediction models: Impact on office building energy performance," Energy, Elsevier, vol. 176(C), pages 667-681.
    7. Yang, S. & Pilet, T.J. & Ordonez, J.C., 2018. "Volume element model for 3D dynamic building thermal modeling and simulation," Energy, Elsevier, vol. 148(C), pages 642-661.
    8. Zhang, Yuna & Augenbroe, Godfried, 2018. "Optimal demand charge reduction for commercial buildings through a combination of efficiency and flexibility measures," Applied Energy, Elsevier, vol. 221(C), pages 180-194.
    9. Alibabaei, Nima & Fung, Alan S. & Raahemifar, Kaamran & Moghimi, Arash, 2017. "Effects of intelligent strategy planning models on residential HVAC system energy demand and cost during the heating and cooling seasons," Applied Energy, Elsevier, vol. 185(P1), pages 29-43.
    10. Rashid, Syed Aftab & Haider, Zeeshan & Chapal Hossain, S.M. & Memon, Kashan & Panhwar, Fazil & Mbogba, Momoh Karmah & Hu, Peng & Zhao, Gang, 2019. "Retrofitting low-cost heating ventilation and air-conditioning systems for energy management in buildings," Applied Energy, Elsevier, vol. 236(C), pages 648-661.
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