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Understanding the deterministic and probabilistic business cases for occupant based plug load management strategies in commercial office buildings

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  • Wang, S.
  • Kim, A.A.
  • Johnson, E.M.

Abstract

Plug load monitoring and associated occupant behavior interventions can play a critical role in reducing commercial building energy consumption. This study investigates whether the reduction in building energy consumption justify the added cost of plug load monitoring and occupant energy saving interventions. The objective of this study is to conduct deterministic and probabilistic return-on-investment (ROI) analysis of instrumenting workspaces, monitoring plug load usage, and applying interventions to promote building energy reduction. The study uses the findings of actual occupant energy saving intervention investigations conducted with city and federal government offices in which the association between occupant energy savings interventions and energy use risk was evaluated. While the deterministic approach led to a positive net present value, the interventions failed to recapture the initial investment, and operational expenses given the uncertainties in the estimate of costs and energy use. The mean ten-year net present value was −$3914 at a 6% discount rate considering all U.S. states. From the project manager’s perspective, other non-energy benefits can justify the additional resources.

Suggested Citation

  • Wang, S. & Kim, A.A. & Johnson, E.M., 2017. "Understanding the deterministic and probabilistic business cases for occupant based plug load management strategies in commercial office buildings," Applied Energy, Elsevier, vol. 191(C), pages 398-413.
    • Handle: RePEc:eee:appene:v:191:y:2017:i:c:p:398-413
    DOI: 10.1016/j.apenergy.2017.01.082
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    Cited by:

    1. Francisco, Abigail & Taylor, John E., 2019. "Understanding citizen perspectives on open urban energy data through the development and testing of a community energy feedback system," Applied Energy, Elsevier, vol. 256(C).
    2. Botman, Lola & Lago, Jesus & Fu, Xiaohan & Chia, Keaton & Wolf, Jesse & Kleissl, Jan & De Moor, Bart, 2024. "Building plug load mode detection, forecasting and scheduling," Applied Energy, Elsevier, vol. 364(C).
    3. Anand, Prashant & Cheong, David & Sekhar, Chandra & Santamouris, Mattheos & Kondepudi, Sekhar, 2019. "Energy saving estimation for plug and lighting load using occupancy analysis," Renewable Energy, Elsevier, vol. 143(C), pages 1143-1161.
    4. Yan Ding & Xiao Pan & Wanyue Chen & Zhe Tian & Zhiyao Wang & Qing He, 2022. "Prediction Method for Office Building Energy Consumption Based on an Agent-Based Model Considering Occupant–Equipment Interaction Behavior," Energies, MDPI, vol. 15(22), pages 1-31, November.

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