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Rising electricity consumption has increased fossil fuel production and emissions, with negative environmental impacts (Hinrichs and Kleinbach, 2012). In 2013, residential energy accounted for 29% of the United Kingdom’s total energy consumption (DECC, 2013). However, 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 pricing incentives that can motivate behavior changes (Albadi and El-Saadany, 2008; Gholami et al., 2020) toward more energy efficiency and better load management (Corbett, 2013). Energy efficiency programs focus on reducing the energy use of specific appliances (Energy Information Association, 2011). The programs are typically long term and do not explicitly time demand. Instead, they can motivate efficient consumption patterns and reduce energy consumption by substituting more efficient equipment and providing consumers with specific products suited for their demands (Corbett, 2013; Fritz et al., 2017).
Load management attempts to balance demand with supply in “real time” often involves demand-response programs where consumers may find their electrical services interrupted or changed based on various real-time signals such as price, availability, and grid conditions (Energy Information Association, 2011). Given the application of 15-minute smart meter readings, smart meters provide 35,000 load data points annually. By identifying typical customers, utilities may be better able to forecast energy demands and differentiate among procurement strategies (Fritz et al., 2017). The data gathered through IT is central to the success of any initiative for reducing electricity consumption by providing information that service providers can utilize to design incentives and users can use to modify their consumption.
Studies of energy consumption behavior have had a limited focus. Household energy consumption research is typically one-dimensional (Abrahamse et al., 2005). For example, intervention studies from a psychological perspective (Olander and Thøgerson, 1995) tend to focus predominantly on changing individual-level attitudes. However, equally important are macro-level demographic or societal factors contributing to household energy use and shaping the physical infrastructure that conditions behavioral choices and energy consumption (Abrahamse et al., 2005). Factors such as dwelling characteristics (e.g., floor size and housing type), socioeconomic characteristics (e.g., age of residents) and behavioral characteristics (e.g., appliance usages) are all vital for understanding and forecasting variations of domestic electricity consumption.
The emergence of green IS and energy informatics has led to increased focus on electricity consumption. Green IS indicates the use of information systems to promote environmental sustainability (e.g., Elliot, 2011, Jenkin et al., 2011, Melville, 2010, Watson et al., 2012). Energy Informatics proposes that energy consumption should be coupled with advanced information systems to improve energy efficiency and reduce emissions (Watson et al., 2010a). Electricity demand forecasting relies extensively on historical data related to factors such as weather patterns, economic conditions, prices, and customer behaviors (Hyndman and Fan, 2009).
Most early demand-side research focused on the outcomes of using smart meters (Chou et al., 2017; Dehdarian 2018; Hielscher and Sovacool, 2019; Kuo et al., 2018; Murray et al., 2018). Although customer attitudes and responses to smart meters determine the ultimate success of demand-side management, the research tells only part of the story (Corbett, 2013). Smart meters also provide utilities with a large amount of new data and information processing capacities that can be used in demand-side management, an information processing activity requiring collection, analysis, and dissemination of information (Corbett, 2013; 2018).