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Occupancy-Based HVAC Control with Short-Term Occupancy Prediction Algorithms for Energy-Efficient Buildings

Author

Listed:
  • Jin Dong

    (Energy and Transportation Science Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA
    These authors contributed equally to this work.)

  • Christopher Winstead

    (Computational Sciences and Engineering Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA
    These authors contributed equally to this work.)

  • James Nutaro

    (Computational Sciences and Engineering Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA
    These authors contributed equally to this work.)

  • Teja Kuruganti

    (Computational Sciences and Engineering Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA
    These authors contributed equally to this work.)

Abstract

This study aims to develop a concrete occupancy prediction as well as an optimal occupancy-based control solution for improving the efficiency of Heating, Ventilation, and Air-Conditioning (HVAC) systems. Accurate occupancy prediction is a key enabler for demand-based HVAC control so as to ensure HVAC is not run needlessly when when a room/zone is unoccupied. In this paper, we propose simple yet effective algorithms to predict occupancy alongside an algorithm for automatically assigning temperature set-points. Utilizing past occupancy observations, we introduce three different techniques for occupancy prediction. Firstly, we propose an identification-based approach, which identifies the model via Expectation Maximization (EM) algorithm. Secondly, we study a novel finite state automata (FSA) which can be reconstructed by a general systems problem solver (GSPS). Thirdly, we introduce an alternative stochastic model based on uncertain basis functions. The results show that all the proposed occupancy prediction techniques could achieve around 70% accuracy. Then, we have proposed a scheme to adaptively adjust the temperature set-points according to a novel temperature set algorithm with customers’ different discomfort tolerance indexes. By cooperating with the temperature set algorithm, our occupancy-based HVAC control shows 20% energy saving while still maintaining building comfort requirements.

Suggested Citation

  • Jin Dong & Christopher Winstead & James Nutaro & Teja Kuruganti, 2018. "Occupancy-Based HVAC Control with Short-Term Occupancy Prediction Algorithms for Energy-Efficient Buildings," Energies, MDPI, vol. 11(9), pages 1-20, September.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:9:p:2427-:d:169621
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    References listed on IDEAS

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    1. 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.
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    4. Mohammed M. Olama & Teja Kuruganti & James Nutaro & Jin Dong, 2018. "Coordination and Control of Building HVAC Systems to Provide Frequency Regulation to the Electric Grid," Energies, MDPI, vol. 11(7), pages 1-15, July.
    5. Hamed Nabizadeh Rafsanjani & Changbum R. Ahn & Mahmoud Alahmad, 2015. "A Review of Approaches for Sensing, Understanding, and Improving Occupancy-Related Energy-Use Behaviors in Commercial Buildings," Energies, MDPI, vol. 8(10), pages 1-34, October.
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    Citations

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    Cited by:

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    2. Paige Wenbin Tien & Shuangyu Wei & John Calautit, 2020. "A Computer Vision-Based Occupancy and Equipment Usage Detection Approach for Reducing Building Energy Demand," Energies, MDPI, vol. 14(1), pages 1-28, December.
    3. Ahmad Esmaeilzadeh & Brian Deal & Aghil Yousefi-Koma & Mohammad Reza Zakerzadeh, 2022. "How Multi-Criterion Optimized Control Methods Improve Effectiveness of Multi-Zone Building Heating System Upgrading," Energies, MDPI, vol. 15(22), pages 1-27, November.
    4. Muhammad Saidu Aliero & Muhammad Asif & Imran Ghani & Muhammad Fermi Pasha & Seung Ryul Jeong, 2022. "Systematic Review Analysis on Smart Building: Challenges and Opportunities," Sustainability, MDPI, vol. 14(5), pages 1-28, March.
    5. Aniela Kaminska, 2019. "Impact of Heating Control Strategy and Occupant Behavior on the Energy Consumption in a Building with Natural Ventilation in Poland," Energies, MDPI, vol. 12(22), pages 1-18, November.
    6. Anastasios Dounis, 2019. "Special Issue “Intelligent Control in Energy Systems”," Energies, MDPI, vol. 12(15), pages 1-9, August.
    7. Dongsu Kim & Jongman Lee & Sunglok Do & Pedro J. Mago & Kwang Ho Lee & Heejin Cho, 2022. "Energy Modeling and Model Predictive Control for HVAC in Buildings: A Review of Current Research Trends," Energies, MDPI, vol. 15(19), pages 1-30, October.
    8. Wei Wang & Xiaofang Shan & Syed Asad Hussain & Changshan Wang & Ying Ji, 2020. "Comparison of Multi-Control Strategies for the Control of Indoor Air Temperature and CO 2 with OpenModelica Modeling," Energies, MDPI, vol. 13(17), pages 1-20, August.
    9. Panagiotis Korkidis & Anastasios Dounis & Panagiotis Kofinas, 2021. "Computational Intelligence Technologies for Occupancy Estimation and Comfort Control in Buildings," Energies, MDPI, vol. 14(16), pages 1-33, August.
    10. 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).
    11. Jiang, Zixin & Deng, Zhipeng & Wang, Xuezheng & Dong, Bing, 2023. "PANDEMIC: Occupancy driven predictive ventilation control to minimize energy consumption and infection risk," Applied Energy, Elsevier, vol. 334(C).
    12. Marek Borowski & Piotr Mazur & Sławosz Kleszcz & Klaudia Zwolińska, 2020. "Energy Monitoring in a Heating and Cooling System in a Building Based on the Example of the Turówka Hotel," Energies, MDPI, vol. 13(8), pages 1-20, April.
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