IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v16y2024i14p5881-d1432467.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/16/14/5881/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/16/14/5881/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. 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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Morgane Innocent & Agnès François-Lecompte & Nolwenn Roudaut, 2020. "Comparison of human versus technological support to reduce domestic electricity consumption in France," Post-Print hal-02450849, HAL.
    2. Grégoire Wallenborn & Catherine Rousseau & Karine Thollier, 2006. "Détermination de profils de ménages pour une utilisation plus rationnelle de l’energie," ULB Institutional Repository 2013/192217, ULB -- Universite Libre de Bruxelles.
    3. Boudet, Hilary S. & Flora, June A. & Armel, K. Carrie, 2016. "Clustering household energy-saving behaviours by behavioural attribute," Energy Policy, Elsevier, vol. 92(C), pages 444-454.
    4. Fischbacher, Urs & Schudy, Simeon & Teyssier, Sabrina, 2021. "Heterogeneous preferences and investments in energy saving measures," Resource and Energy Economics, Elsevier, vol. 63(C).
    5. Dalia Streimikiene & Tomas Balezentis, 2020. "Willingness to Pay for Renovation of Multi-Flat Buildings and to Share the Costs of Renovation," Energies, MDPI, vol. 13(11), pages 1-16, May.
    6. Oikonomou, Vlasis & Rietbergen, Martijn & Patel, Martin, 2007. "An ex-ante evaluation of a White Certificates scheme in The Netherlands: A case study for the household sector," Energy Policy, Elsevier, vol. 35(2), pages 1147-1163, February.
    7. Gulshan Maqbool & Zulqarnain Haider, 2021. "The Impact of Individual Behavior on Household Energy Saving," Journal of Economic Impact, Science Impact Publishers, vol. 3(1), pages 39-46.
    8. Ju-Hee Kim & Younggew Kim & Seung-Hoon Yoo, 2021. "Using a choice experiment to explore the public willingness to pay for the impacts of improving energy efficiency of an apartment," Quality & Quantity: International Journal of Methodology, Springer, vol. 55(5), pages 1775-1793, October.
    9. Guo Li & Wenling Liu & Zhaohua Wang & Mengqi Liu, 2017. "An empirical examination of energy consumption, behavioral intention, and situational factors: evidence from Beijing," Annals of Operations Research, Springer, vol. 255(1), pages 507-524, August.
    10. Schleich, Joachim & Gassmann, Xavier & Faure, Corinne & Meissner, Thomas, 2016. "Making the implicit explicit: A look inside the implicit discount rate," Energy Policy, Elsevier, vol. 97(C), pages 321-331.
    11. Mohamed, Ahmed M.A. & Al-Habaibeh, Amin & Abdo, Hafez & Elabar, Sherifa, 2015. "Towards exporting renewable energy from MENA region to Europe: An investigation into domestic energy use and householders’ energy behaviour in Libya," Applied Energy, Elsevier, vol. 146(C), pages 247-262.
    12. Nieves García-de-Frutos & José Manuel Ortega-Egea & Javier Martínez-del-Río, 2018. "Anti-consumption for Environmental Sustainability: Conceptualization, Review, and Multilevel Research Directions," Journal of Business Ethics, Springer, vol. 148(2), pages 411-435, March.
    13. Felipe Encinas & Carlos Marmolejo-Duarte & Carlos Aguirre-Nuñez & Francisco Vergara-Perucich, 2020. "When Residential Energy Labeling Becomes Irrelevant: Sustainability vs. Profitability in the Liberalized Chilean Property Market," Sustainability, MDPI, vol. 12(22), pages 1-17, November.
    14. Shujie Zhao & Qingbin Song & Chao Wang, 2019. "Characterizing the Energy-Saving Behaviors, Attitudes and Awareness of University Students in Macau," Sustainability, MDPI, vol. 11(22), pages 1-11, November.
    15. Kenichi Mizobuchi & Kenji Takeuchi, 2012. "The Influences of Economic and Psychological Factors on Energy-Saving Behavior: A Field Experiment in Matsuyama, Japan," Discussion Papers 1206, Graduate School of Economics, Kobe University.
    16. Simona-Roxana Ulman & Costica Mihai & Cristina Cautisanu, 2020. "Peculiarities of the Relation between Human and Environmental Wellbeing in Different Stages of National Development," Sustainability, MDPI, vol. 12(19), pages 1-26, October.
    17. Véronique Vasseur & Anne-Francoise Marique, 2019. "Households’ Willingness to Adopt Technological and Behavioral Energy Savings Measures: An Empirical Study in The Netherlands," Energies, MDPI, vol. 12(22), pages 1-25, November.
    18. Manfred Lenzen & Robert A. Cummins, 2013. "Happiness versus the Environment—A Case Study of Australian Lifestyles," Challenges, MDPI, vol. 4(1), pages 1-19, May.
    19. Nair, Gireesh & Gustavsson, Leif & Mahapatra, Krushna, 2010. "Factors influencing energy efficiency investments in existing Swedish residential buildings," Energy Policy, Elsevier, vol. 38(6), pages 2956-2963, June.
    20. Seán Schmitz & Sophia Becker & Laura Weiand & Norman Niehoff & Frank Schwartzbach & Erika von Schneidemesser, 2019. "Determinants of Public Acceptance for Traffic-Reducing Policies to Improve Urban Air Quality," Sustainability, MDPI, vol. 11(14), pages 1-16, July.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:16:y:2024:i:14:p:5881-:d:1432467. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.