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Data-Driven Evaluation of Residential HVAC System Efficiency Using Energy and Environmental Data

Author

Listed:
  • Huyen Do

    (Department of Civil Construction and Environmental Engineering, Iowa State University, Ames, IA 50011, USA
    Faculty of Project Management, The University of Danang-University of Science and Technology, Danang 50000, Vietnam)

  • Kristen S. Cetin

    (Department of Civil Construction and Environmental Engineering, Iowa State University, Ames, IA 50011, USA)

Abstract

In the U.S., the heating, ventilation, and air conditioning (HVAC) system is generally the largest electricity-consuming end-use in a residential building. However, homeowners are less likely to have their HVAC system serviced regularly, thus inefficiencies in operation are also more likely to occur. To address this challenge, this research works towards a non-intrusive data-driven assessment method using building assessors’ data, HVAC electricity demand data, and outdoor environmental data. Building assessors’ data is first used to estimate the HVAC system size, then estimate the electricity demand curve of the HVAC system. A comparison of the proposed electricity demand curve development method demonstrates strong agreement with physics-based HVAC model results. An HVAC efficiency rating is then proposed, which compares the model-predicted and actual performance data to define whether an HVAC system is operating as expected. As a case study, detailed data for 39 occupied, conditioned residential buildings in Austin, Texas, was used demonstrating the identification of the presence of potential HVAC inefficiencies. The results prove beneficial for utilities to help target residential HVAC systems in need of service or energy efficiency upgrades, as well as for homeowners as a continuous assessment tool for HVAC performance.

Suggested Citation

  • Huyen Do & Kristen S. Cetin, 2019. "Data-Driven Evaluation of Residential HVAC System Efficiency Using Energy and Environmental Data," Energies, MDPI, vol. 12(1), pages 1-15, January.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:1:p:188-:d:195732
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    References listed on IDEAS

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    1. Cesare Biserni & Paolo Valdiserri & Dario D’Orazio & Massimo Garai, 2018. "Energy Retrofitting Strategies and Economic Assessments: The Case Study of a Residential Complex Using Utility Bills," Energies, MDPI, vol. 11(8), pages 1-15, August.
    2. Swan, Lukas G. & Ugursal, V. Ismet, 2009. "Modeling of end-use energy consumption in the residential sector: A review of modeling techniques," Renewable and Sustainable Energy Reviews, Elsevier, vol. 13(8), pages 1819-1835, October.
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    Cited by:

    1. Huang, Luling & Nock, Destenie & Cong, Shuchen & Qiu, Yueming (Lucy), 2023. "Inequalities across cooling and heating in households: Energy equity gaps," Energy Policy, Elsevier, vol. 182(C).
    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. Serafín Alonso & Antonio Morán & Miguel Ángel Prada & Perfecto Reguera & Juan José Fuertes & Manuel Domínguez, 2019. "A Data-Driven Approach for Enhancing the Efficiency in Chiller Plants: A Hospital Case Study," Energies, MDPI, vol. 12(5), pages 1-28, March.
    4. 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.
    5. Abdulrahman Alanezi & Kevin P. Hallinan & Kefan Huang, 2021. "Automated Residential Energy Audits Using a Smart WiFi Thermostat-Enabled Data Mining Approach," Energies, MDPI, vol. 14(9), pages 1-23, April.

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