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A novel operation approach for the energy efficiency improvement of the HVAC system in office spaces through real-time big data analytics

Author

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  • Li, Wenzhuo
  • Koo, Choongwan
  • Hong, Taehoon
  • Oh, Jeongyoon
  • Cha, Seung Hyun
  • Wang, Shengwei

Abstract

Since a traditional centralized control system (e.g., building energy management system) with a fixed schedule and manual control is not appropriate to irregularly occupied rooms, it is expected to have a large amount of energy saving potential in operating the HVAC system. To overcome this challenge, this study aimed to develop a novel operation approach for the energy efficiency improvement of the HVAC system in office spaces. The real-time indoor environmental indicators were collected and analyzed to evaluate the current operation status of the HVAC system as well as to propose a novel control strategy in two ways. The significant findings can be illustrated as follows. First, it could be stated that occupants would tend to establish a lower set-point temperature for a cooler indoor environment. To solve this issue, a basic control strategy was proposed to detect the anomaly detection of the HVAC system and to automatically adjust the indoor temperature within a preferred range. Second, it could be evaluated that the HVAC system would be kept operating since occupants would forget to turn off the HVAC system after the meetings. To solve this issue, an advanced control strategy was proposed to operate the automatic on/off control of the HVAC system by considering the indoor temperature and CO2 concentration in real time. The proposed strategies can contribute to a large amount of energy savings in operating the HVAC system of irregularly occupied spaces.

Suggested Citation

  • Li, Wenzhuo & Koo, Choongwan & Hong, Taehoon & Oh, Jeongyoon & Cha, Seung Hyun & Wang, Shengwei, 2020. "A novel operation approach for the energy efficiency improvement of the HVAC system in office spaces through real-time big data analytics," Renewable and Sustainable Energy Reviews, Elsevier, vol. 127(C).
    • Handle: RePEc:eee:rensus:v:127:y:2020:i:c:s1364032120301787
    DOI: 10.1016/j.rser.2020.109885
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    References listed on IDEAS

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    Cited by:

    1. Mir M. Ali & Kheir Al-Kodmany & Paul J. Armstrong, 2023. "Energy Efficiency of Tall Buildings: A Global Snapshot of Innovative Design," Energies, MDPI, vol. 16(4), pages 1-23, February.
    2. Hanaa Talei & Driss Benhaddou & Carlos Gamarra & Houda Benbrahim & Mohamed Essaaidi, 2021. "Smart Building Energy Inefficiencies Detection through Time Series Analysis and Unsupervised Machine Learning," Energies, MDPI, vol. 14(19), pages 1-21, September.
    3. Tien, Paige Wenbin & Wei, Shuangyu & Calautit, John Kaiser & Darkwa, Jo & Wood, Christopher, 2022. "Real-time monitoring of occupancy activities and window opening within buildings using an integrated deep learning-based approach for reducing energy demand," Applied Energy, Elsevier, vol. 308(C).
    4. Bie, Yiming & Liu, Yajun & Li, Shiwu & Wang, Linhong, 2022. "HVAC operation planning for electric bus trips based on chance-constrained programming," Energy, Elsevier, vol. 258(C).
    5. Luo, X.J. & Oyedele, Lukumon O. & Ajayi, Anuoluwapo O. & Akinade, Olugbenga O. & Owolabi, Hakeem A. & Ahmed, Ashraf, 2020. "Feature extraction and genetic algorithm enhanced adaptive deep neural network for energy consumption prediction in buildings," Renewable and Sustainable Energy Reviews, Elsevier, vol. 131(C).
    6. Grillone, Benedetto & Danov, Stoyan & Sumper, Andreas & Cipriano, Jordi & Mor, Gerard, 2020. "A review of deterministic and data-driven methods to quantify energy efficiency savings and to predict retrofitting scenarios in buildings," Renewable and Sustainable Energy Reviews, Elsevier, vol. 131(C).
    7. Li, Wenzhuo & Tang, Rui & Wang, Shengwei & Zheng, Zhuang, 2023. "An optimal design method for communication topology of wireless sensor networks to implement fully distributed optimal control in IoT-enabled smart buildings," Applied Energy, Elsevier, vol. 349(C).
    8. Lee, Junsoo & Kim, Tae Wan & Koo, Choongwan, 2022. "A novel process model for developing a scalable room-level energy benchmark using real-time bigdata: Focused on identifying representative energy usage patterns," Renewable and Sustainable Energy Reviews, Elsevier, vol. 169(C).
    9. Jeeyoung Lim & Joseph J. Kim & Sunkuk Kim, 2021. "A Holistic Review of Building Energy Efficiency and Reduction Based on Big Data," Sustainability, MDPI, vol. 13(4), pages 1-18, February.

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