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The Impact of Occupancy-Driven Models on Cooling Systems in Commercial Buildings

Author

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  • Seyyed Danial Nazemi

    (Department of Industrial and Systems Engineering, Rutgers University, Piscataway, NJ 08854, USA)

  • Esmat Zaidan

    (Department of International Affairs, College of Arts and Science, Qatar University, Doha 999043, Qatar)

  • Mohsen A. Jafari

    (Department of Industrial and Systems Engineering, Rutgers University, Piscataway, NJ 08854, USA)

Abstract

Cooling systems play a key role in maintaining human comfort inside buildings. The key challenges that are facing conventional cooling systems are the rapid growth of total cooling energy and annual electricity consumption in commercial buildings. This is even more significant in countries with an arid climate, where the cooling systems are typically working 80% of the year. Thus, there has been growing interest in developing smart control models to assign optimal cooling setpoints in recent years. In the present work, we propose an occupancy-based control model that is based on a non-linear optimization algorithm to efficiently reduce energy consumption and costs. The model utilizes a Monte-Carlo method to determine the approximate occupancy schedule for building thermal zones. We compare our proposed model to three different strategies, namely: always-on thermostat, schedule-based model, and a rule-based occupancy-driven model. Unlike these three rule-based algorithms, the proposed optimization approach is a white-box model that considers the thermodynamic relationships in the cooling system to find the optimal cooling setpoints. For comparison, different case studies in five cities around the world were investigated. Our findings illustrate that the proposed optimization algorithm is able to noticeably reduce the cooling energy consumption of the buildings. Significantly, in cities that were located in severe hot regions, such as Doha and Phoenix, cooling energy consumption can be reduced by 14.71% and 15.19%, respectively.

Suggested Citation

  • Seyyed Danial Nazemi & Esmat Zaidan & Mohsen A. Jafari, 2021. "The Impact of Occupancy-Driven Models on Cooling Systems in Commercial Buildings," Energies, MDPI, vol. 14(6), pages 1-20, March.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:6:p:1722-:d:520669
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    References listed on IDEAS

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    1. Ghahramani, Ali & Zhang, Kenan & Dutta, Kanu & Yang, Zheng & Becerik-Gerber, Burcin, 2016. "Energy savings from temperature setpoints and deadband: Quantifying the influence of building and system properties on savings," Applied Energy, Elsevier, vol. 165(C), pages 930-942.
    2. Kusiak, Andrew & Xu, Guanglin, 2012. "Modeling and optimization of HVAC systems using a dynamic neural network," Energy, Elsevier, vol. 42(1), pages 241-250.
    3. Lakeridou, Michelle & Ucci, Marcella & Marmot, Alexi, 2014. "Imposing limits on summer set-points in UK air-conditioned offices: A survey of facility managers," Energy Policy, Elsevier, vol. 75(C), pages 354-368.
    4. Kusiak, Andrew & Li, Mingyang & Tang, Fan, 2010. "Modeling and optimization of HVAC energy consumption," Applied Energy, Elsevier, vol. 87(10), pages 3092-3102, October.
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    Cited by:

    1. Ali Ghofrani & Esmat Zaidan & Mohsen Jafari, 2021. "Reshaping energy policy based on social and human dimensions: an analysis of human-building interactions among societies in transition in GCC countries," Palgrave Communications, Palgrave Macmillan, vol. 8(1), pages 1-26, December.

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