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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

Data in the Smart Grid

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This is just a short post in response to this article in the IEEE Smart grid newsletter on ‘Slicing and Dicing Smart Grid Data’,  by Christos Polyzois which states that:

If a utility tried to collect detailed consumption data at a central site in real-time (interactively) once every 15 minutes (let alone more frequently), each customer would generate almost 100 transactions per day. This number is a multiple of the number of banking, credit card and airline transactions that a household generates per day, which implies that the smart grid would necessitate an IT infrastructure (computing and communications) larger than that of the banking, credit card, and airline industries together.

The author calls for the different types of data to be identified and managed differently:

Broadcast data. To balance the grid, data concerning grid status need to be communicated to market participants. That includes information on price changes, critical peak events, reliability signals, and–in the future-carbon content. Availability of this data makes the electricity market efficient by allowing consumers to make informed decisions, leading to better allocation and utilization of resources.

Comment: Some of this data is already broadcast over the internet in the UK at least – however tools are not readily available to filter down this data to users. AlertMe and GridCarbon are two apps that at least show the users how they fare against others near them and how much carbon the grid generates in real-time respectively. However, it is unclear what the benefits of broadcasting the data are – more user evaluations are needed to determine the impact of this information. Of course, if an agent (managing a storage device or a micro-generation facility) obtains such signals, it is likely to ‘react’ to them without consideration for what other agents ‘reacting’ to the same signals would do. In fact, if all agents react to a low price at the same time (as we’ve shown in our work – see my previous post), significant peaks could be generated in the system and hence, such signals could be detrimental to the system. Hence, the reaction to such signals need to be well studied before assuming that ‘better allocation and utilisation’ of resources will be achieved.

Billing interval data. For price signals to make markets efficient, some form of variable pricing must be in effect (time of use, critical peak pricing, peak time rebates, and so on). A utility needs to take readings at the beginning and end of a period during which a particular price is in effect, so that the utility can bill its customers properly.

The idea of billing a user for its periodic consumption aims to filter down the costs in the balancing market to the consumers. At the moment they are agnostic to such costs because of flat price/TOU pricing profiles. BUT will the users actually react to more accurate billing – will they care about a difference of 5 pence for half an hour? Again, on the utility’s side, accurate billing is good but it needs to be accompanied by consumer education or feedback (through the smart meter) to be of any long term value. It is to the advantage of the utility to do so as, making consumption flatter, will help it buy more energy in bulk in the forward market and possibly trade in futures as well.

Detailed consumption data. Energy customers need to know their current rate of electric consumption so that they can adjust it; they may also use automation controls to set their preferences so as to avoid having to constantly take actions.

Again, here the issue of human behaviour come up. It is not clear from existing results that users will indeed react to real-time prices or  want to automate the control of devices – they’d prefer to defer them themselves and might not let this deferment to be more than a certain number of hours (Note: I’ll add some references on this later).

Aggregate statistical data. Energy service providers may show customers their consumption by month, give them comparisons with neighbors or with historical time series, and other analytic results based on historic usage.

The impact of such data will probably help make users aware of their energy consumption and it is important to study the impact that the social network of a user can have on her energy consumption. Google PowerMeter and AlertMe try to do the same but, so far, no serious evaluation has been done in this space.


Written by agentsinthesmartgrid

February 22, 2011 at 10:09 pm

Posted in Data