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Data-driven evaluation of HVAC operation and savings in commercial buildings

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  • Khalilnejad, Arash
  • French, Roger H.
  • Abramson, Alexis R.

Abstract

Commercial buildings consumed 36% of electricity, or 1.35 trillion kWh, in the United States in 2017, and almost 30% of this energy was wasted. Much of this loss can be attributed to inefficient heating ventilation and air conditioning (HVAC) systems. By improving the operational conditions of HVAC, significant savings can be achieved. However, most buildings and building equipment do not use costly sub-meters to monitor and address performance issues, and on-site auditing can be expensive and insufficient. Alternatively in this study, we propose a data-driven method to identify savings opportunities using only whole building meter data and without setting foot in the building. For this purpose, we introduced two algorithms that virtually quantify the value of a thermostat setpoint setback and HVAC rescheduling. Additionally, we developed novel methods for detecting occupancy patterns and quantifying the baseload of the HVAC operation. Using a clustering algorithm, we identified those buildings for which HVAC savings was significant and further categorized the buildings based on their potential for savings. A population study of over 432 commercial buildings demonstrated a median percentage energy savings of 1.6% from a baseload reduction and 2.1% from HVAC rescheduling. Additionally, results indicate that retail buildings have the highest potential for savings among the building types studied.

Suggested Citation

  • Khalilnejad, Arash & French, Roger H. & Abramson, Alexis R., 2020. "Data-driven evaluation of HVAC operation and savings in commercial buildings," Applied Energy, Elsevier, vol. 278(C).
    • Handle: RePEc:eee:appene:v:278:y:2020:i:c:s0306261920310175
    DOI: 10.1016/j.apenergy.2020.115505
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. He, Xianya & Huang, Jingzhi & Liu, Zekun & Lin, Jian & Jing, Rui & Zhao, Yingru, 2023. "Topology optimization of thermally activated building system in high-rise building," Energy, Elsevier, vol. 284(C).
    2. Khalilnejad, Arash & French, Roger H. & Abramson, Alexis R., 2021. "Evaluation of cooling setpoint setback savings in commercial buildings using electricity and exterior temperature time series data," Energy, Elsevier, vol. 233(C).
    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. Alessandro Franco & Lorenzo Miserocchi & Daniele Testi, 2021. "HVAC Energy Saving Strategies for Public Buildings Based on Heat Pumps and Demand Controlled Ventilation," Energies, MDPI, vol. 14(17), pages 1-20, September.
    5. Felix Garcia-Torres & Ascension Zafra-Cabeza & Carlos Silva & Stephane Grieu & Tejaswinee Darure & Ana Estanqueiro, 2021. "Model Predictive Control for Microgrid Functionalities: Review and Future Challenges," Energies, MDPI, vol. 14(5), pages 1-26, February.
    6. Arash Khalilnejad & Ahmad M Karimi & Shreyas Kamath & Rojiar Haddadian & Roger H French & Alexis R Abramson, 2020. "Automated pipeline framework for processing of large-scale building energy time series data," PLOS ONE, Public Library of Science, vol. 15(12), pages 1-22, December.
    7. Anatolijs Borodinecs & Arturs Palcikovskis & Andris Krumins & Deniss Zajecs & Kristina Lebedeva, 2024. "Assessment of HVAC Performance and Savings in Office Buildings Using Data-Driven Method," Clean Technol., MDPI, vol. 6(2), pages 1-12, June.
    8. Chen, Yibo & Gao, Junxi & Yang, Jianzhong & Berardi, Umberto & Cui, Guoyou, 2023. "An hour-ahead predictive control strategy for maximizing natural ventilation in passive buildings based on weather forecasting," Applied Energy, Elsevier, vol. 333(C).
    9. Zhang, Shuyang & Zhang, Lun & Zhang, Xiaosong, 2022. "Clustering based on dynamic time warping to extract typical daily patterns from long-term operation data of a ground source heat pump system," Energy, Elsevier, vol. 249(C).

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