Modeling Residential Electricity Consumption from Public Demographic Data for Sustainable Cities

Muhammad Ali, Krishneel Prakash, Carlos Macana, Ali Kashif Bashir, Alireza Jolfaei, Awais Bokhari, Jiří Jaromír Klemeš and Hemanshu Pota
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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

Energies, 2022, vol. 15, issue 6, 1-16

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.

Keywords: demographic data; energy transitions; information management; residential sector; smart cities (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
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