Last week Jack Kelly, PhD student in the Department of Computing here at Imperial College London, presented at our weekly energy seminars on his work on disaggregated electricity bills. To complement the talk he has written us a blog post on his research on how disaggregated electricity bills may, or may not, help change consumer behaviour. You can also find the slides from his talk online.
I should probably start by explaining what a “disaggregated” electricity bill is. Simply put it is an electricity bill which shows how much energy individual appliances use in your home. You could put meters on each appliance. But this is expensive and tedious. It would be more convenient to take the data from a single smart meter which measures your whole home’s electricity demand and to tease this signal apart into the energy consumption of different electrical appliances. This is what disaggregation aims to do. One use is to provide domestic electricity users with an itemised energy bill and hence help people to save energy.
There are many other uses of disaggregation but I am most interested in its ability to reduce energy demand.
I started working on the computer science of energy disaggregation five years ago: first as my MSc project and then as a PhD. My interest was sparked early on when I found several papers which suggested that disaggregation may help the general population to save a significant amount of energy: Maybe 10% or more. This sounded very exciting to me because I was then – and still am – terrified about climate change and I really wanted to help engineer solutions to mitigate climate change. A 10% reduction in the UK’s domestic electricity consumption would equate to a CO2 emission reduction of about five million tonnes of CO2 per year. Not bad for a bit of code running on a handful of servers!
As my PhD progressed, I buried my head in the computer science and didn’t pay much attention to the social science of energy disaggregation.
Then, towards the end of last year (2015), I was having a pint with a colleague whose opinions I greatly respect. Towards the end of the evening, he let slip that he believes that “energy disaggregation is dead!” By which he meant that it does not help the general population to save energy. This statement shocked me. As soon as I returned to my computer, I set about trying to prove my friend wrong!
My mission to prove him wrong grew into an exhaustive, systematic review of the literature on the effectiveness of disaggregated energy feedback on energy reduction. It is this review which I presented at the Energy Futures Labs seminar on Tuesday and is also published this week in a workshop paper called “Does disaggregated electricity feedback reduce domestic electricity consumption? A systematic review of the literature” that will be presented at the 3rd International NILM Workshop in Vancouver on the 14th and 15th May 2016.
I examined twelve studies on the efficacy of disaggregated energy feedback. The average electricity reduction across these studies is 4.5%. However, 4.5% may be a positively-biased estimate of the savings achievable across the entire population because all twelve studies are likely to be prone to opt-in bias hence none of them test the effect of disaggregated feedback on the general population.
Disaggregation may not even be required to achieve these savings. Aggregate feedback, such as an in-home-display that shows total usage and cost in real-time, drives 3% reductions. Four of the studies directly compared this aggregate feedback against disaggregated feedback and found that aggregate feedback (displayed on an in-home-display) is at least as effective as disaggregated feedback (displayed on a website). This is possibly because websites are viewed less often than in-home-displays (in the short-term, at least) and because some users do not trust fine-grained disaggregation (although this may be an issue with the specific user interface studied).
Disaggregated electricity feedback may help a motivated sub-group of the population (‘energy enthusiasts’) to save more energy but fine-grained disaggregation may not be necessary to achieve these energy savings.
Disaggregation has many uses beyond those discussed in my talk and my paper but, on the specific question of promoting energy reduction in the general population, there is no robust evidence that current forms of disaggregated energy feedback are more effective than aggregate energy feedback.
The effectiveness of disaggregated feedback may increase if the general population become more energy-conscious (e.g. if energy prices rise or concern about climate change deepens); or if users’ trust in fine-grained disaggregation improves; or if innovative new approaches or alternative disaggregation strategies (e.g. disaggregating by behaviour rather than by appliance) out-perform existing feedback.
My systematic review suggests that there are gaps in our knowledge about the effectiveness of disaggregation. Current evidence suggests that disaggregated feedback is less effective than aggregate feedback. But there are several reasons to believe that existing studies may not be showing us the full picture. I think that it is crucial that the community continues to study the effectiveness of disaggregated feedback using randomised controlled trials.