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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

On Demand-Side Management

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Since power systems specialists tend to be very good at controlling the grid and the suppliers on the grid (either via energy markets or through regulation), the consumer-side of the problem has always been quite elusive. The term ‘demand-side management’ (DSM)   aims to capture all the possible mechanisms by which demand can be contolled (effectively, ‘management’ seems to be the non technical term for the more controversial ‘control’ term that could have been used).  This may be an over-generalisation given that the ways in which demand can be controlled can be further broken down into a number of categories:

  1. DSM via pricing mechanisms – to incentivise consumers simply based on costs to reduce peak demand or shift consumption to later times.
  2. DSM via decentralised coordination mechanisms – the actors involved could be exchanging messages in order to agree to curtail demand (as in a Virtual Power Plant) or to provide energy back to the grid at times where it is needed (e.g., electric vehicles coalescing to provide energy back to the grid).
  3. DSM via centralised control of generation/consumption – the most drastic form of DSM.

The above categorisation shows that DSM, as it will be carried out in the smart grid, will have to take into accound that consumers will not just be consumers and would also be producers, hence the term prosumers. The term prosumer was coined by futurologist Alvin Toffler [1]. Toffler defined the prosumer as someone who blurs the distinction between a “consumer” and a “producer.”  In many other domains, a prosumer means various things (e.g., someone who works at home and at the work place [2] or even a professional consumer –see wikipedia entry ). However, in the energy domain, and in the smart grid in particular, prosumers will become complex first-class objects of their own. These prosumers are characterised by their:

  • Demand elasticity – how they respond to prices based on how they view their need for energy but also how they can shift consumption across any given time period by virtue of their energy storage capacity or the deferrability of their devices with respect to their impact on their comfort.
  • Supply capacity and availability – their micro-generation capability may widely vary – starting with individuals generating energy from their solar panels which are affected by the weather to VPPs of facebook friends banding together to sell energy from their combined CHP/solar panel installations. Note that supply = reduction in demand and hence supply capacity/availability is equivalent to how these actors reduce their demand according to their storage/deferrability of their loads.
  • Social context – as highlighted in the previous point the actors may wish to band together to form VPPs or even decide to run energy cooperatives/consumer groups keen on finding ways to reduce their consumption proactively or sell back to the grid their extra generation (curtailment) capacity. However, the conscious embedding of energy-related activities into the traditional social activities of consumer will require a significant paradigm shift. Similar to the shift caused by mobile phones which allows people nowadays to maintain their social relationships all the time (e.g., checking-in using Facebook places and FourSquare), electric vehicles or smart meters could ideally, one day, allow people to all coordinate to shift their energy and literally ‘check-in’ into some demand-reduction exercise in real-time. However, while the motivation to ‘check-in’ and form groups is natural when it comes to sharing interests in movies and books, it is not so much the case when it comes to energy. The benefit of switching off lights, turning down the thermostat by 2 degrees sounds greatly appealing on the first day you hear it but is quickly forgotten later on. Something (possibly an intelligent agent) has to keep this up.

The prosumer is a complex animal not yet fully understood and likely to morph into something most people (engineers in particular) are scared about. Building more predictability into the system is of immense importance. Some of the main reasons are as follows:

  • If prosumers decide to all charge their batteries or defer their loads at times when prices are low, they can and will cause higher peaks than currently exist. This is one of the main worries expressed in the power systems community [3]. Indeed, the grid makes significant savings in terms of generation capacity needed given that the energy consumption of typical consumers is so diverse – that is, they tend to consume their absolute maximum at different times of the day. This means that, when their demand is aggregated, it is never the case that the total demand at any point in time is equal to the sum of everyone’s peak demand! DSM may break this natural diversity.  To illustrate this see the figure below which shows the peaks that would have been generated in the grid if consumers were simply reacting to prices (i.e., choosing to consume at specific times of the day given that they could defer their usage of electricity at any time irrespective of the cost of comfort incurred) on the grid for example [4]. One of the key ways such peaks may be reduced is to engineer inertia into the shifting of loads using machine learning techniques as we showed in [4].

Figure showing the consequences of applying Time-of-use pricing, Spot-pricing (day-ahead prices), and a flat price (as it is currently).

  • If prosumers decide to buy new batteries and new CHP units/solar panels  in the home, their consumption/supply capacity and availability may change significantly throughout the day (depending on how users decide to use energy) and throughout the year (as new and old devices are used, fail, or serviced). Hence, the traditional mechanisms used to make sure supply capacity is available days, weeks, or months ahead may not apply anymore. The environment will be much more dynamic. One of the key ways this may be managed is to ensure that there is enough redundancy in the system, that is, making sure that the supply from multiple providers is contracted and used if needed.
  • Social activities will dictate how prosumers create peaks or contribute energy (or reduce energy consumption) to the grid if prosumers are given enough incentives to do so. Incentives, however, cannot simply come in the form of prices as, not all social activities can be matched to cost savings. For example, having dinner in a nearby restaurant instead of the sushi bar you prefer 3 miles away, may not be the action you decide to take in case going to the nearby restaurant will save you 5 dollars and help the grid reduce carbon emissions. People like to be together and will do anything to do so (e.g., fans of Justin Bieber travelling to see him in London from all corners of the UK). At this point in time, there is no obvious solution to this. The socio-technical challenge involved is immense.

The use of intelligent agents in managing devices, storage, and generation capacity is a foregone conclusion. Several major challenges, however, still lie ahead in terms of modelling energy usage optimisation problems and addressing the key human-computer interaction issues involved.

UPDATE: I saw this article on the Prosumer in the smart grid today! :

[1] A. Toffler, W. Longul and H. Forbes (1981) “The third wave”, Bantam Books New York.

[2] William Gerhardt (2008) “Prosumers: A New Growth Opportunity”, CISCO.

[3] G. Strbac (2008) “Demand side management: Benefits and challenges“, Energy Policy, Volume 36, Issue 12, December 2008, Pages 4419-4426, Foresight Sustainable Energy Management and the Built Environment Project.

[4] Ramchurn, S., Vytelingum, P., Rogers, A. and Jennings, N. (2011) Agent-Based Control for Decentralised Demand Side Management in the Smart Grid. In: The Tenth International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2011), 2-6 May 2011, Taipei, Taiwan. (In Press)

Written by agentsinthesmartgrid

February 19, 2011 at 11:27 am