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Evaluation of cooling setpoint setback savings in commercial buildings using electricity and exterior temperature time series data

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

Abstract

Commercial buildings account for a significant amount of total energy produced in the US, and the Heating Ventilation and Cooling (HVAC) systems are one of the most significant components of their overall consumption. In this study, we proposed a new data-driven approach to evaluate HVAC cooling systems in commercial buildings and identify savings opportunities. The focus is an investigation of the impact of thermostat setpoint setback but using only whole building, electricity data taken at 15-min intervals for the analysis. We conducted a comparative study of setpoint setback characteristics on 432 commercial buildings with 5 building usage types across the United States. To accomplish this, both piecewise and Random Forest regression algorithms were employed using electricity and exterior temperature datasets to identify operational characteristics and the effective setpoints in the building to determine the corresponding savings opportunities. Both occupied and unoccupied time periods were studied across cooling degree days (CDD), when air conditioning is typically operational. The results show that in commercial buildings, on average, cooling systems account for 9.5% of total consumption. When a one degree setback during the cooling season is applied, an average of approximately 1.1% of annual consumption is achieved; retail and office buildings demonstrate the highest potential for savings. Additionally, we identified that the number of cooling degree days and base to peak ratio (BPR) are the most important variables for predicting the magnitude of the consumption of cooling systems.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:energy:v:233:y:2021:i:c:s0360544221013657
    DOI: 10.1016/j.energy.2021.121117
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    References listed on IDEAS

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    1. 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.
    2. 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.
    3. 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).
    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.
    5. R. J. Erhardt, 2015. "Mid‐twenty‐first‐century projected trends in North American heating and cooling degree days," Environmetrics, John Wiley & Sons, Ltd., vol. 26(2), pages 133-144, March.
    6. Liu, Da & Sun, Kun, 2019. "Random forest solar power forecast based on classification optimization," Energy, Elsevier, vol. 187(C).
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    1. Triolo, Ryan C. & Rajagopal, Ram & Wolak, Frank A. & de Chalendar, Jacques A., 2023. "Estimating cooling demand flexibility in a district energy system using temperature set point changes from selected buildings," Applied Energy, Elsevier, vol. 336(C).

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