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

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...

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Vydané v:Journal of global information management Ročník 29; číslo 2; s. 166 - 193
Hlavní autori: Emrouznejad, Ali, Gholami, Roya, Nishant, Rohit
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Hershey IGI Global 01.03.2021
Predmet:
Behavior
Big Data
Clusters
Consumers
Data analysis
Data mining
Demand side management
Electric power distribution
Electric utilities
Electricity
Electricity consumption
Energy consumption
Energy efficiency
Energy management
Households
Informatics
Information management
Information processing
Information retrieval
Information systems
Intelligence
Open access publishing
Residential energy
Smart meters
Sustainability
ISSN:1062-7375, 1533-7995
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Popis
Shrnutí: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.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
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ISSN:1062-7375
1533-7995
DOI:10.4018/JGIM.2021030109