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Modeling Residential Energy Consumption: An Application of IT-Based Solutions and Big Data Analytics for Sustainability

Modeling Residential Energy Consumption: An Application of IT-Based Solutions and Big Data Analytics for Sustainability

Roya Gholami, Rohit Nishant, Ali Emrouznejad
Copyright: © 2021 |Volume: 29 |Issue: 2 |Pages: 28
ISSN: 1062-7375|EISSN: 1533-7995|EISBN13: 9781799859017|DOI: 10.4018/JGIM.2021030109
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MLA

Gholami, Roya, et al. "Modeling Residential Energy Consumption: An Application of IT-Based Solutions and Big Data Analytics for Sustainability." JGIM vol.29, no.2 2021: pp.166-193. http://doi.org/10.4018/JGIM.2021030109

APA

Gholami, R., Nishant, R., & Emrouznejad, A. (2021). Modeling Residential Energy Consumption: An Application of IT-Based Solutions and Big Data Analytics for Sustainability. Journal of Global Information Management (JGIM), 29(2), 166-193. http://doi.org/10.4018/JGIM.2021030109

Chicago

Gholami, Roya, Rohit Nishant, and Ali Emrouznejad. "Modeling Residential Energy Consumption: An Application of IT-Based Solutions and Big Data Analytics for Sustainability," Journal of Global Information Management (JGIM) 29, no.2: 166-193. http://doi.org/10.4018/JGIM.2021030109

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Abstract

Smart meters that allow information to flow between users and utility service providers are expected to foster intelligent energy consumption. Previous studies focusing on demand-side management have been predominantly restricted to factors that utilities can manage and manipulate, but have ignored factors specific to residential characteristics. They also often presume that households consume similar amounts of energy and electricity. To fill these gaps in literature, the authors investigate two research questions: (RQ1) Does a data mining approach outperform traditional statistical approaches for modelling residential energy consumption? (RQ2) What factors influence household energy consumption? They identify household clusters to explore the underlying factors central to understanding electricity consumption behavior. Different clusters carry specific contextual nuances needed for fully understanding consumption behavior. The findings indicate electricity can be distributed according to the needs of six distinct clusters and that utilities can use analytics to identify load profiles for greater energy efficiency.