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Modeling Residential Electricity Consumption from Public Demographic Data for Sustainable Cities

Author

Listed:
  • Muhammad Ali

    (School of Engineering and Information Technology, University of New South Wales, Canberra 2612, Australia)

  • Krishneel Prakash

    (School of Engineering and Information Technology, University of New South Wales, Canberra 2612, Australia)

  • Carlos Macana

    (School of Engineering and Information Technology, University of New South Wales, Canberra 2612, Australia)

  • Ali Kashif Bashir

    (Department of Computing and Mathematics, Manchester Metropolitan University, Manchester M15 6BH, UK)

  • Alireza Jolfaei

    (Department of Computing, Macquarie University, Sydney 2113, Australia)

  • Awais Bokhari

    (Sustainable Process Integration Laboratory—SPIL, NETME Centre, Faculty of Mechanical Engineering, Brno University of Technology—VUT Brno, Technická 2896/2, 61669 Brno, Czech Republic)

  • Jiří Jaromír Klemeš

    (Sustainable Process Integration Laboratory—SPIL, NETME Centre, Faculty of Mechanical Engineering, Brno University of Technology—VUT Brno, Technická 2896/2, 61669 Brno, Czech Republic)

  • Hemanshu Pota

    (School of Engineering and Information Technology, University of New South Wales, Canberra 2612, Australia)

Abstract

Demographic factors, statistical information, and technological innovation are prominent factors shaping energy transitions in the residential sector. Explaining these energy transitions requires combining insights from the disciplines investigating these factors. The existing literature is not consistent in identifying these factors, nor in proposing how they can be combined. In this paper, three contributions are made by combining the key demographic factors of households to estimate household energy consumption. Firstly, a mathematical formula is developed by considering the demographic determinants that influence energy consumption, such as the number of persons per household, median age, occupancy rate, households with children, and number of bedrooms per household. Secondly, a geographical position algorithm is proposed to identify the geographical locations of households. Thirdly, the derived formula is validated by collecting demographic factors of five statistical regions from local government databases, and then compared with the electricity consumption benchmarks provided by the energy regulators. The practical feasibility of the method is demonstrated by comparing the estimated energy consumption values with the electricity consumption benchmarks provided by energy regulators. The comparison results indicate that the error between the benchmark and estimated values for the five different regions is less than 8% (7.37%), proving the efficacy of this method in energy consumption estimation processes.

Suggested Citation

  • Muhammad Ali & Krishneel Prakash & Carlos Macana & Ali Kashif Bashir & Alireza Jolfaei & Awais Bokhari & Jiří Jaromír Klemeš & Hemanshu Pota, 2022. "Modeling Residential Electricity Consumption from Public Demographic Data for Sustainable Cities," Energies, MDPI, vol. 15(6), pages 1-16, March.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:6:p:2163-:d:772321
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    References listed on IDEAS

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    1. Kim, Tae-Young & Cho, Sung-Bae, 2019. "Predicting residential energy consumption using CNN-LSTM neural networks," Energy, Elsevier, vol. 182(C), pages 72-81.
    2. Fumo, Nelson & Rafe Biswas, M.A., 2015. "Regression analysis for prediction of residential energy consumption," Renewable and Sustainable Energy Reviews, Elsevier, vol. 47(C), pages 332-343.
    3. Boogen, Nina & Datta, Souvik & Filippini, Massimo, 2021. "Estimating residential electricity demand: New empirical evidence," Energy Policy, Elsevier, vol. 158(C).
    4. Liu, Yang & Wang, Wei & Ghadimi, Noradin, 2017. "Electricity load forecasting by an improved forecast engine for building level consumers," Energy, Elsevier, vol. 139(C), pages 18-30.
    5. Zhang, Wenwen & Robinson, Caleb & Guhathakurta, Subhrajit & Garikapati, Venu M. & Dilkina, Bistra & Brown, Marilyn A. & Pendyala, Ram M., 2018. "Estimating residential energy consumption in metropolitan areas: A microsimulation approach," Energy, Elsevier, vol. 155(C), pages 162-173.
    6. Narayan, Paresh Kumar & Smyth, Russell, 2005. "The residential demand for electricity in Australia: an application of the bounds testing approach to cointegration," Energy Policy, Elsevier, vol. 33(4), pages 467-474, March.
    7. Alice Mugnini & Gianluca Coccia & Fabio Polonara & Alessia Arteconi, 2020. "Performance Assessment of Data-Driven and Physical-Based Models to Predict Building Energy Demand in Model Predictive Controls," Energies, MDPI, vol. 13(12), pages 1-18, June.
    8. Muhammad Naveed Iqbal & Lauri Kütt & Matti Lehtonen & Robert John Millar & Verner Püvi & Anton Rassõlkin & Galina L. Demidova, 2021. "Travel Activity Based Stochastic Modelling of Load and Charging State of Electric Vehicles," Sustainability, MDPI, vol. 13(3), pages 1-14, February.
    9. Niemierko, Rochus & Töppel, Jannick & Tränkler, Timm, 2019. "A D-vine copula quantile regression approach for the prediction of residential heating energy consumption based on historical data," Applied Energy, Elsevier, vol. 233, pages 691-708.
    10. Deb, C. & Schlueter, A., 2021. "Review of data-driven energy modelling techniques for building retrofit," Renewable and Sustainable Energy Reviews, Elsevier, vol. 144(C).
    11. Nivine Attoue & Isam Shahrour & Rafic Younes, 2018. "Smart Building: Use of the Artificial Neural Network Approach for Indoor Temperature Forecasting," Energies, MDPI, vol. 11(2), pages 1-12, February.
    12. Xu, Guangyue & Wang, Weimin, 2020. "China’s energy consumption in construction and building sectors: An outlook to 2100," Energy, Elsevier, vol. 195(C).
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