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Field evaluation of advanced controls for the retrofit of packaged air conditioners and heat pumps

Author

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  • Wang, Weimin
  • Katipamula, Srinivas
  • Ngo, Hung
  • Underhill, Ronald
  • Taasevigen, Danny
  • Lutes, Robert

Abstract

This paper documents the magnitude of energy savings achievable in the field by retrofitting existing packaged rooftop air conditioner and heat pump units (RTUs) with advanced control strategies not ordinarily used for RTUs. A total of 66 RTUs on 8 different buildings were retrofitted with a commercially available advanced controller for improving operational efficiency. The controller features enhanced air-side economizer control, multi-speed fan control, and demand-controlled ventilation. Of the 66 RTUs, 18 are packaged heat pumps and the rest are packaged air conditioners with gas heat. The eight buildings cover four building types and four climate conditions. Based on the performance data collected for approximately 1year, the normalized annual energy consumption savings ranged between 22% and 90%, with an average of 57% for all RTUs. The average fractional savings uncertainty was 12% at 95% confidence level. Normalized annual electricity savings were in the range between 0.47kWh/h (kWh per hour of RTU operation) and 7.21kWh/h, with an average of 2.39kWh/h. RTUs greater than 53kW and runtime greater than 14h per day had payback periods less than 3years even at electricity price of $0.05/kWh.

Suggested Citation

  • Wang, Weimin & Katipamula, Srinivas & Ngo, Hung & Underhill, Ronald & Taasevigen, Danny & Lutes, Robert, 2015. "Field evaluation of advanced controls for the retrofit of packaged air conditioners and heat pumps," Applied Energy, Elsevier, vol. 154(C), pages 344-351.
    • Handle: RePEc:eee:appene:v:154:y:2015:i:c:p:344-351
    DOI: 10.1016/j.apenergy.2015.04.129
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    Citations

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    Cited by:

    1. Kim, Donghun & Braun, James E., 2020. "Model predictive control for supervising multiple rooftop unit economizers to fully leverage free cooling energy resource," Applied Energy, Elsevier, vol. 275(C).
    2. Zhang, Sheng & Ai, Zhengtao & Lin, Zhang, 2021. "Novel demand-controlled optimization of constant-air-volume mechanical ventilation for indoor air quality, durability and energy saving," Applied Energy, Elsevier, vol. 293(C).
    3. Cui, Can & Zhang, Xin & Cai, Wenjian, 2020. "An energy-saving oriented air balancing method for demand controlled ventilation systems with branch and black-box model," Applied Energy, Elsevier, vol. 264(C).
    4. Dhumane, Rohit & Ling, Jiazhen & Aute, Vikrant & Radermacher, Reinhard, 2017. "Portable personal conditioning systems: Transient modeling and system analysis," Applied Energy, Elsevier, vol. 208(C), pages 390-401.
    5. Tejeda De La Cruz, Alberto & Riviere, Philippe & Marchio, Dominique & Cauret, Odile & Milu, Anamaria, 2017. "Hardware in the loop test bench using Modelica: A platform to test and improve the control of heating systems," Applied Energy, Elsevier, vol. 188(C), pages 107-120.
    6. Hui, Hongxun & Ding, Yi & Liu, Weidong & Lin, You & Song, Yonghua, 2017. "Operating reserve evaluation of aggregated air conditioners," Applied Energy, Elsevier, vol. 196(C), pages 218-228.
    7. Lim, Dae Kyu & Ahn, Byoung Ha & Jeong, Ji Hwan, 2018. "Method to control an air conditioner by directly measuring the relative humidity of indoor air to improve the comfort and energy efficiency," Applied Energy, Elsevier, vol. 215(C), pages 290-299.
    8. Hong, Ying-Yi & Chang, Wen-Chun & Chang, Yung-Ruei & Lee, Yih-Der & Ouyang, Der-Chuan, 2017. "Optimal sizing of renewable energy generations in a community microgrid using Markov model," Energy, Elsevier, vol. 135(C), pages 68-74.

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