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Energy Consumption Analysis and Characterization of the Residential Sector in the US towards Sustainable Development

Author

Listed:
  • Khaled Bawaneh

    (Clark Atlanta University, Atlanta, GA 30314, USA)

  • Samir Das

    (Southeast Missouri State University, Cape Girardeau, MO 63701, USA)

  • Md. Rasheduzzaman

    (Southeast Missouri State University, Cape Girardeau, MO 63701, USA)

Abstract

In 2023, residential and commercial sectors together consumed approximately 27.6% of total United States (U.S.) energy, equivalent to about 20.6 quadrillion Btu. Factoring in the electrical system energy losses, the residential sector represented approximately 19.7% of total U.S. energy consumption during that time. There were approximately 144 million housing units in the United States in 2023, which is increasing yearly. In this study, information on energy usage in the United States residential sector has been analyzed and then represented as energy intensities to establish benchmark data and to compare energy consumption of varying sizes and locations. First, public sources were identified and data from these previously published sources were aggregated to determine the energy use of the residential sector within the US. Next, as part of this study, the energy data for seven houses/apartments from five different United States climate zones were collected firsthand. That data were analyzed, and the energy intensity of each home was calculated and then compared with the energy intensities of the other homes in the same states using Residential Energy Consumption Survey (RECS) data. The energy intensity for each facility was calculated based on the actual energy bills. Finally, the study evaluated the carbon footprint associated with residential energy consumption in all 50 states to reinforce the importance of sustainable development initiatives.

Suggested Citation

  • Khaled Bawaneh & Samir Das & Md. Rasheduzzaman, 2024. "Energy Consumption Analysis and Characterization of the Residential Sector in the US towards Sustainable Development," Energies, MDPI, vol. 17(11), pages 1-24, June.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:11:p:2789-:d:1410042
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    References listed on IDEAS

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    1. Fateme Dinmohammadi & Yuxuan Han & Mahmood Shafiee, 2023. "Predicting Energy Consumption in Residential Buildings Using Advanced Machine Learning Algorithms," Energies, MDPI, vol. 16(9), pages 1-23, April.
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