Watts your usage? A field study of householders’ literacy for residential electricity data

Abstract

Smart metering studies typically focus on quantifying behavior change. However, little is known about how users understand energy information and analyze and interpret feedback from energy data visualizations. To investigate this, we gave 13 participants from nine UK households an electricity power clamp meter. Prior to installing and using the device, we conducted interviews with participants to gauge their understanding of their home electricity consumption and found that participants varied considerably from limited to substantial energy literacy. Two weeks after the clamp meter had been installed, we conducted a contextual inquiry in which we asked participants to explain the web-based time series visualization of their recorded electricity data. We found that the visualization proved unfit: participants relied on memories and suggested likely routines, while widely being unable to reliably identify specific events in the data visualization. In follow-up interviews 3 months later, we found that participants' understanding of their home electricity consumption had hardly changed. Finally, we invited participants to generate ideas how smart electricity feedback could be optimized. They named different forms of disaggregation, higher temporal resolution, and interactivity as design requirements. In summary, these results suggest that people find home energy data very difficult to understand and link to everyday actions and behaviors.

Exploring domestic energy consumption feedback through interactive annotation

“…The research objective was to inquire how people make sense of and reflect on energy data. In contrast to earlier work, our study found that participants did rather well in terms of explaining their data patterns: Overall, annotations were plausible which is in contrast with the field study interviews by Herrmann et al (2018), who concluded that householders perform rather poorly in making sense of their energy data when presented as a time series line graph. The core difference between the two studies is that thanks to FigureEnergy as an interactive prototype, in our study participants could annotate and hence actively reflect on their data patterns as often as they wanted, and they were encouraged to do so.…”

“…The aim of this exploratory field study Energy Efficiency is to gain insights into householders' ability to make sense of their consumption data, when given the opportunity to actively engage with it and reflect on it over the course of several days or weeks. This is in extension to existing research which shows limited ability in householders to explain their data spontaneously (Herrmann et al, 2018). Before describing this study in detail, we first give a brief description of FigureEnergy.…”

“…However, energy literacy is typically low in the general population and previous work found that householders find it challenging to relate to energy consumption data. For example, Herrmann et al (2018) conducted a contextual inquiry with householders using a commercial electricity tracking tool. They asked participants to spontaneously make sense of a time series line graph showing the household's consumption.…”

Identifying Challenges in Engaging Users to Increase Self-Consumption of Electricity in Microgrids

“…Microgrids are not passive networks of electrical currents but are dynamic systems that are as responsive to the elements that power them as they are to the actors who use them. From an economic perspective, electricity is a commodity that is traded, bought and sold, but an everyday user may perceive it is an unintuitive, intangible good that must be used immediately and cannot be stored [6]. Consequently, users are not intrinsically reflective of the implications of their consumption patterns and will not reflexively alter these patterns.…”

Energy End-Use Flexibility of the Next Generation of Decision-Makers in a Smart Grid Setting: An Exploratory Study

“…The process of decarbonizing the economy will depend, to some extent, on the demand-side flexibility, which may be fostered through the use of time-differentiated tariffs, either with static and dynamic options [1]. In these pricing schemes, end-users are encouraged to adopt more flexible consumption patterns, adjusting their demand profile by reducing or increasing consumption in different time periods, shifting load operation to cheaper time periods or redefining thermostat settings [2], [3]. According to [2] and [4], some factors may influence end-users' enrolment in DR programs, such as: end-user's energy literacy level; the complexity of DR programs and dynamic tariffs; technology costs (when compared to savings and incentives provided); the effort required to search for dynamic pricing information and adjust electrical appliances usage accordingly; risk/loss aversion; and the inertia associated with behavioral change.…”