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FigureEnergy: Understanding Energy Consumption using Interactive Feedback

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FigureEnergy: Understanding Energy Consumption using Interactive feedback

The FigureEnergy Logger

My colleague and I recently got our paper accepted at the Ubicomp conference and it’s to be presented in September. The paper presents our work on energy feedback interfaces that aims to differentiate itself from previous attempts, mainly by industry, to design energy feedback interfaces. The challenge we set ourselves was to create something that actually made sense to users rather than take an ‘engineer’s’ point of view of what feedback should be (both of us being electrical engineers did force us to think outside the box!).

In effect, the key, we believe, to understanding your energy consumption is to move away from the ‘graph’. Indeed, the graph can be the most interesting visualisation for daily consumption data from a smart meter measuring your electricity consumption from a economist’s, engineer’s, or mathematician’s point of view but not from the general public’s.

Hence, to change all this, we thought of a ‘change of representation’. Just as in AI, one would change the representation of a problem to try and solve it using a clever technique, by changing the representation we aim to make it easier for people to understand how they are using energy, compare energy consumption activities, and reflect on such activities. By so doing, they may be able to focus their energy reduction techniques on the right activities (e.g., did you know your set-top box drains 70W when on standby and consumes much more than your phone chargers you carefully remove every time you are not using them – why not unplug your set-top box as well?).

The FigureEnergy system allows you to find out these issues through the use of annotations of historical data, where basically, you annotate an energy consumption graph by dragging your mouse and selecting a period of time over which you performed some energy consuming activity. For example, you may annotate ‘Washing machine run with setting 1’ from 5:00 to 6:20 and ‘Dinner with Friends’ from 8:00pm to 11:00 pm on a Friday night.

The use of annotations is an extension of the work of Enrico’s on tangible interfaces (see website). It’s an interesting approach that’s based on ‘constructivism’ – the idea that you learn better when you practise reconstructing in your mind how to do something – here you are reconstructing your energy consumption activities by annotating them on a graph. Using these annotations, we then change this graph-based representation of your data to a ‘block-based’ one where each energy consumption activity is a rectangle whose size is proportional to the energy consumed by the activity. Users were found to like this representation as they could relate to it more easily than to numbers (e.g., my washing machine consumes 1kWh while my kettle consumed 0.6 kWh – is that really bad? Or are these standard consumptions? If the washing machine block is twice as big as the kettle and you can see them compared to your TV, then you know where most of your costs are).

The results reported in the paper do confirm our intuitions that the use of interactive feedback results in greater understanding (e.g., people start making sense of how much devices consume not just in terms of power -i.e, through spikes – but also in terms of duration – i.e., blocks of energy usage).

Given the good ‘feedback’ (no pun intended) on FE, we are planning several key improvements, one of which would be the extension to using CurrentCost  meters. At present we are using AlertMe meters to get aggregate energy readings and they’ve worked well for us.

Get the full paper at:

Costanza, Enrico, Ramchurn, Sarvapali D. and Jennings, Nicholas R. (2012)Understanding domestic energy consumption through interactive visualisation: a field study. In, Ubicomp 2012, 14th ACM International Conference on Ubiquitous Computing, Pittsburgh, US, 05 – 08 Sep 2012.10pp. (In Press)

Written by agentsinthesmartgrid

June 26, 2012 at 10:00 pm

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