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

Jovan Chew, Anurag Sharma (), Dhivya Sampath Kumar, Wenjie Zhang, Nandini Anant and Jiaxin Dong
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Jovan Chew: Cluster of Engineering, Singapore Institute of Technology, Singapore 138683, 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

Sustainability, 2024, vol. 16, issue 14, 1-21

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.

Keywords: data analytics; energy consumption behaviours; energy management; personality attributes; residential demand (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
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