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Building Performance Evaluation Using Coupled Simulation of EnergyPlus™ and an Occupant Behavior Model

Author

Listed:
  • Mengda Jia

    (UrbSys Lab (Urban Building Energy, Sensing, Controls, Big Data Analysis, and Visualization), M.E. Rinker, Sr. School of Construction Management, University of Florida, Gainesville, FL 32611, USA)

  • Ravi Srinivasan

    (UrbSys Lab (Urban Building Energy, Sensing, Controls, Big Data Analysis, and Visualization), M.E. Rinker, Sr. School of Construction Management, University of Florida, Gainesville, FL 32611, USA)

Abstract

Building energy simulation programs are used for optimal sizing of building systems to reduce excessive energy wastage. Such programs employ thermo-dynamic algorithms to estimate every aspect of the target building with a certain level of accuracy. Currently, almost all building simulation tools capture static features of a building including the envelope, geometry, and Heating, Ventilation, and Air Conditioning (HVAC) systems, etc. However, building performance also relies on dynamic features such as occupants’ interactions with the building. Such interactions have not been fully implemented in building energy simulation tools, which potentially influences the comprehensiveness and accuracy of estimations. This paper discusses an information exchange mechanism via coupling of EnergyPlus™, a building energy simulation engine and PMFServ, an occupant behavior modeling tool, to alleviate this issue. The simulation process is conducted in Building Controls Virtual Testbed (BCVTB), a virtual simulation coupling tool that connects the two separate simulation engines on a time-step basis. This approach adds a critical dimension to the traditional building energy simulation programs to seamlessly integrate occupants’ interactions with building components to improve the modeling capability, thereby improving building performance evaluation. The results analysis of this paper reveals a need to consider metrics that measure different types of comfort for building occupants.

Suggested Citation

  • Mengda Jia & Ravi Srinivasan, 2020. "Building Performance Evaluation Using Coupled Simulation of EnergyPlus™ and an Occupant Behavior Model," Sustainability, MDPI, vol. 12(10), pages 1-13, May.
    • Handle: RePEc:gam:jsusta:v:12:y:2020:i:10:p:4086-:d:359068
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    References listed on IDEAS

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    1. Dziedzic, Jakub Wladyslaw & Yan, Da & Sun, Hongsan & Novakovic, Vojislav, 2020. "Building occupant transient agent-based model – Movement module," Applied Energy, Elsevier, vol. 261(C).
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    Cited by:

    1. Hua Chen & Shuang Dai & Fanlin Meng, 2023. "Smart Building Thermal Management: A Data-Driven Approach Based on Dynamic and Consensus Clustering," Sustainability, MDPI, vol. 15(21), pages 1-25, October.
    2. Jonas Bielskus & Violeta Motuzienė & Tatjana Vilutienė & Audrius Indriulionis, 2020. "Occupancy Prediction Using Differential Evolution Online Sequential Extreme Learning Machine Model," Energies, MDPI, vol. 13(15), pages 1-20, August.
    3. Habtamu Tkubet Ebuy & Hind Bril El Haouzi & Riad Benelmir & Remi Pannequin, 2023. "Occupant Behavior Impact on Building Sustainability Performance: A Literature Review," Sustainability, MDPI, vol. 15(3), pages 1-23, January.
    4. Andrea Gabaldón Moreno & Fredy Vélez & Beril Alpagut & Patxi Hernández & Cecilia Sanz Montalvillo, 2021. "How to Achieve Positive Energy Districts for Sustainable Cities: A Proposed Calculation Methodology," Sustainability, MDPI, vol. 13(2), pages 1-19, January.
    5. Zhaoxi Zhan & Wenna Xu & Lin Xu & Xinyue Qi & Wenjie Song & Chen Wang & Ziye Huang, 2022. "BIM-Based Green Hospital Building Performance Pre-Evaluation: A Case Study," Sustainability, MDPI, vol. 14(4), pages 1-21, February.

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