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

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. [ABSTRACT FROM AUTHOR]