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.
Highlights
Rising electricity consumption has increased fossil fuel production and emissions, with negative environmental impacts (Hinrichs and Kleinbach, 2012)
The findings indicated that smart meters offer IS-enabled information processing capacities that significantly impact the effectiveness of demand-side management for utilities
We examine the effectiveness of the data mining models in investigating the underlying factors of residential electricity consumption
Summary
Rising electricity consumption has increased fossil fuel production and emissions, with negative environmental impacts (Hinrichs and Kleinbach, 2012). Utility providers can use information systems, analytics, “smart grids,” and “demand-side management” to accurately forecast electricity consumption and costs, increase productivity while reducing consumption, and enhance their financial bottom line while reducing negative environmental impacts (Corbett, 2013; Nishant et al, 2014; Gholami et al, 2016). The smart grid is a green IT artifact that can be used to reduce environmental pollution (Corbett, 2010), while demand-side management involves several IT artifacts such as smart meters and meter data management systems to focus on downstream consumption-end activities related to the value chain, with the objective of understanding, influencing, and managing consumer demand (Canever et al, 2008). Demand-side management strategies involve demand response activities such as electricity.
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