Lawrence Berkeley National Laboratory

Demand-side flexibility has been suggested as a tool for peak demand reduction and large-scale integration of low-carbon electricity sources. Deeper insight into the activities and energy services performed in households could help to understand the scope and limitations of demand-side flexibility. Measuring and Evaluating Time- and Energy-use Relationships (METER) is a 5-year, UK-based research project and the first study to collect activity data and electricity use in parallel at this scale. We present statistical analyses of these new data, including more than 6250 activities reported by 450 individuals in 173 households, and their relationship to electricity use patterns. We use a regularization technique to select influential variables in regression models of average electricity use over a day and of discretionary use across 4-h time periods to compare intra-day variations. We find that dwelling and appliance variables show the strongest associations to average electricity consumption and can explain 49% of the variance in mean daily usage. For models of 4-h average “de-minned” consumption, we find that activity variables are consistently influential, both in terms of coefficient magnitudes and contributions to increased model explanatory power. Activities relating to food preparation and eating, household chores, and recreation show the strongest associations. We conclude that occupant activity data can advance our understanding of the temporal characteristics of electricity demand and inform approaches to shift or reduce it. We stress the importance of considering consumption as a function of time of day, and we use our findings to argue that a more nuanced understanding of this relationship can yield useful insights for residential demand flexibility.