Paper Info
Title
Modeling Residential Energy Consumption: An Application of IT-Based Solutions and Big Data Analytics for Sustainability
Abstract
AbstractSmart 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.
Year
DOI
Venue
2021
10.4018/JGIM.2021030109
Periodicals
Keywords
DocType
Volume
Consumption Pattern, Data Mining, Modeling Energy Consumption, Smart Grid, Smart Meter
Journal
29
Issue
ISSN
Citations 
2
1062-7375
0
PageRank 
References 
Authors
0.34
0
3
Name
Order
Citations
PageRank
Roya Gholami1636.13
Rohit Nishant200.34
Ali Emrouznejad366459.69