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Performance Evaluation of an Occupancy-Based HVAC Control System in an Office Building

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  • Guanjing Lin

    (Building Technology and Urban Systems Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
    Institute of Future Human Habitats, Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, China)

  • Armando Casillas

    (Building Technology and Urban Systems Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA)

  • Maggie Sheng

    (Building Technology and Urban Systems Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
    Electric Power Research Institute, Sunnyvale, CA 94090, USA)

  • Jessica Granderson

    (Building Technology and Urban Systems Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA)

Abstract

As new algorithms incorporate occupancy count information into more sophisticated HVAC control, these technologies offer great potential for reductions in energy costs while enhancing flexibility. This study presents results from a two-year field evaluation of an occupancy-based HVAC control system installed in an office building. Two wings on each of the building’s 2–11 floors were equipped with occupancy counters to learn occupancy patterns. In combination with proprietary machine learning algorithms and thermal modeling, the occupancy data were leveraged to implement optimized start, early closure, and adjustments to fan operation at the air handling unit (AHU) level. This study conducted a holistic evaluation of technical performance, cost-effectiveness analysis, and user satisfaction. Results show the platform reduced weekday AHU run times by 2 h and 35 min per AHU per day during the pandemic time period. Simulation shows that 6.1% annual whole-building savings can be achieved when the building is fully occupied. The results are compared with prior studies, and potential drivers are discussed for future opportunities. The assessment results shed light on the expected in-the-field performance for researchers and industry stakeholders and enabled practical considerations as the technology strives to move beyond research-grade pilot trials into product-grade deployment.

Suggested Citation

  • Guanjing Lin & Armando Casillas & Maggie Sheng & Jessica Granderson, 2023. "Performance Evaluation of an Occupancy-Based HVAC Control System in an Office Building," Energies, MDPI, vol. 16(20), pages 1-21, October.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:20:p:7088-:d:1259584
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    References listed on IDEAS

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    1. Kim, Donghun & Wang, Zhe & Brugger, James & Blum, David & Wetter, Michael & Hong, Tianzhen & Piette, Mary Ann, 2022. "Site demonstration and performance evaluation of MPC for a large chiller plant with TES for renewable energy integration and grid decarbonization," Applied Energy, Elsevier, vol. 321(C).
    2. Kong, Meng & Dong, Bing & Zhang, Rongpeng & O'Neill, Zheng, 2022. "HVAC energy savings, thermal comfort and air quality for occupant-centric control through a side-by-side experimental study," Applied Energy, Elsevier, vol. 306(PA).
    3. Guanjing Lin & Hannah Kramer & Valerie Nibler & Eliot Crowe & Jessica Granderson, 2022. "Building Analytics Tool Deployment at Scale: Benefits, Costs, and Deployment Practices," Energies, MDPI, vol. 15(13), pages 1-17, July.
    4. Gianluca Serale & Massimo Fiorentini & Alfonso Capozzoli & Daniele Bernardini & Alberto Bemporad, 2018. "Model Predictive Control (MPC) for Enhancing Building and HVAC System Energy Efficiency: Problem Formulation, Applications and Opportunities," Energies, MDPI, vol. 11(3), pages 1-35, March.
    5. Pang, Zhihong & Chen, Yan & Zhang, Jian & O'Neill, Zheng & Cheng, Hwakong & Dong, Bing, 2020. "Nationwide HVAC energy-saving potential quantification for office buildings with occupant-centric controls in various climates," Applied Energy, Elsevier, vol. 279(C).
    6. Peng, Yuzhen & Rysanek, Adam & Nagy, Zoltán & Schlüter, Arno, 2018. "Using machine learning techniques for occupancy-prediction-based cooling control in office buildings," Applied Energy, Elsevier, vol. 211(C), pages 1343-1358.
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    1. García Vázquez, C.A. & Cotfas, D.T. & González Santos, A.I. & Cotfas, P.A. & León Ávila, B.Y., 2024. "Reduction of electricity consumption in an AHU using mathematical modelling for controller tuning," Energy, Elsevier, vol. 293(C).

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