EcoFeed : a better energy consumption feedback system

Abstract

Remotely readable electricity meters have become commonplace. They generate a lot of data that is currently of little use to the consumers. At most, they have an opportunity to see their energy consumption dynamics over time as charts or graphs. This visualization is uninformative and does not reflect how everyday actions affect household‘s energy consumption. In this work, we propose a system that utilizes machine learning in order to create a better, near real-time visual feedback to the end users on their energy consumption. We call our solution EcoFeed. EcoFeed is aimed at providing the consumers with a better idea of their energy consumption and how their actions affect it. Studies have shown that when presented with better feedback, people tend to change their behaviour towards energy conservation and thus live more sustainable life. The main constraint we followed while developing EcoFeed was to make it easily implementable in real life. Hence, EcoFeed is developed using existing open-source technologies and utilizes only smart meters data and data from open sources. We have conducted a survey to evaluate how well EcoFeed communicates energy consumption to people and how it performs against the conventional visualization - a graph. Survey results show that EcoFeed is much better at communicating energy consumption to the end-users.