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Unveiling the Dynamics of Residential Energy Consumption: A Quantitative Study of Demographic and Personality Influences in Singapore Using Machine Learning Approaches

Author

Listed:
  • Jovan Chew

    (Cluster of Engineering, Singapore Institute of Technology, Singapore 138683, Singapore
    Electrical Power Engineering, Newcastle University in Singapore, Singapore 567739, Singapore)

  • Anurag Sharma

    (Electrical Power Engineering, Newcastle University in Singapore, Singapore 567739, Singapore)

  • Dhivya Sampath Kumar

    (Cluster of Engineering, Singapore Institute of Technology, Singapore 138683, Singapore)

  • Wenjie Zhang

    (Department of Electrical and Electronic Engineering, The Hong Kong Polytechnic University, Hong Kong 999077, China)

  • Nandini Anant

    (Agency for Science, Technology and Research (A*STAR), Singapore 138632, Singapore)

  • Jiaxin Dong

    (Cluster of Engineering, Singapore Institute of Technology, Singapore 138683, Singapore)

Abstract

In the pursuit of instigating a progressive transition towards a more sustainable future, policy officials all over the world are fervently advocating the use of energy conservation techniques targeted at residential customers. Keeping this in mind, a quantitative study was conducted in this work using the data from Singapore, which aims to investigate the relationships between a resident’s pattern of energy utilisation and numerous demographic parameters as well as personality attributes. Moreover, the study was conducted with existing machine learning and data analytics approaches, including k-prototype unsupervised learning and statistical hypothesis tests. The obtained results denote a persuasive correlation between the consumption behaviour of the consumer for different appliances and factors such as income, energy knowledge, usage frequency, personality, etc. For instance, there is a higher probability of a consumer acting frugally and sparingly if they believe their energy consumption is insignificant. These findings can help policymakers identify the appropriate target populations for raising energy awareness in Singapore.

Suggested Citation

  • Jovan Chew & Anurag Sharma & Dhivya Sampath Kumar & Wenjie Zhang & Nandini Anant & Jiaxin Dong, 2024. "Unveiling the Dynamics of Residential Energy Consumption: A Quantitative Study of Demographic and Personality Influences in Singapore Using Machine Learning Approaches," Sustainability, MDPI, vol. 16(14), pages 1-21, July.
    • Handle: RePEc:gam:jsusta:v:16:y:2024:i:14:p:5881-:d:1432467
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    References listed on IDEAS

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    1. Wang, Zhaohua & Zhang, Bin & Yin, Jianhua & Zhang, Yixiang, 2011. "Determinants and policy implications for household electricity-saving behaviour: Evidence from Beijing, China," Energy Policy, Elsevier, vol. 39(6), pages 3550-3557, June.
    2. Ziqi Jia & Ling Song, 2020. "Weighted k-Prototypes Clustering Algorithm Based on the Hybrid Dissimilarity Coefficient," Mathematical Problems in Engineering, Hindawi, vol. 2020, pages 1-13, July.
    3. Poortinga, Wouter & Steg, Linda & Vlek, Charles & Wiersma, Gerwin, 2003. "Household preferences for energy-saving measures: A conjoint analysis," Journal of Economic Psychology, Elsevier, vol. 24(1), pages 49-64, February.
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