Smart Demand: Lessons From Water

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Presentation at 'Smart Demand' Workshop, iHeat 2012, 13th November 2012, Murray Edwards College, Buckingham House Conference Centre, Cambridge. (http://www.cir-strategy.com/events/heat/)

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  • Just so we’re clear on the numbers…
  • No use trying to reduce water the same way for these households - can the heavy showerers be reduced? The heavy WC users? -> What are people actually doing with these appliances…?
  • No… we need a way to get at what we term… (Warde, 2005) etc Constraints - the way we wash our bodies & our clothes may be closely linked via occupations, commuting modes, children’s activities etc Power showers enable new washing experiences but uses more water… They are why people appear irrational in terms of water use - don’t respond (for example) to price signals.
  • Frequency - how often shower/bathe/flannel wash etc Diversity - range of different kinds of performances/reasons (always shower, mixture etc) Technology - power shower, bath, shower, flannel Outsourcing - washing outside the home Chart = distribution of whole sample on these dimensions
  • Generates these 6 washing clusters - differentiated along the dimensions NB: project has done same exercise for laundry & gardening too
  • Interestingly simple regression model suggests membership of these clusters explains about 20% of the variation on litres/day consumed by the 69 households for whom we have linked data. So by thinking about clusters of practices we’re starting to get a handle on some of that variation
  • Simple daily showers = reference group for logitistic models Basically few demographics are good predictors of being in a given cluster Environmental values (attitudes to water/energy etc) are poor predictors overall NB: much historical social housing does not have a shower so expect more ‘baths’ for this tenure group (arrow)
  • Some examples from ongoing work by Southampton group 9 eco-houses in Havant - same build standards, same equipment incl PV, varying people
  • Massively varying overall consumption patterns
  • And also massively varying rates of PV export, not always (or even mostly) related to overall levels of consumption. Timing is key! -> habits and routines
  • e.g. HH 2
  • e.g. HH 4
  • Jamie? Systematics - school/work routines constrain WHEN cooking can happen. C.f. comparisons with S. Europe siesta/mid-day cooking etc But also distributed networks of demand -> a greater part of the way we use energy in the kitchen is ‘constructed’ through wider network of influences incl. media chefs, what is considered ‘good cooking’ (and by whom?), taste & fashion etc
  • SPRG = social practices work on water (& energy by others, esp cooling) DANCER = applying some of these ideas to energy interventions
  • Smart Demand: Lessons From Water

    1. 1. Smart Demand:Lessons from WaterDr Ben Andersonb.anderson@soton.ac.ukSustainable Energy Research GroupFaculty of Engineering and the Environment
    2. 2. The Menu The problem(s) with water Water ‘practices’ The problem with ‘demographics’ Lessons from water Implications for smart energy 2
    3. 3. The Menu The problem(s) with water Water ‘practices’ The problem with ‘demographics’ Lessons from water Implications for smart energy Source: DEFRA, 2008 3
    4. 4. The problem(s) with water… Source: DEFRA, 2011 Over abstraction With no action It costs to clean – Energy (carbon) Supply – Patchy (no grid) – Locally variable Demand – poorly understood 4
    5. 5. What do we know? Domestic water demand is rising Mean daily consumption – ~= 150 l/person/day – ~= 140 l/person/day (2030)? More single households – more total volume Source: DEFRA, 2011 5
    6. 6. What do we know? Domestic water demand is rising Mean daily consumption – ~= 150 l/person/day – ~= 140 l/person/day (2030)? More single households – more total volume And – Consumption = ƒ(occupancy) – But look at the ranges! Source: DEFRA, 2011 But that’s about it… Source: ESRC Sustainable Practices Group Water Survey, 2011 www.sprg.ac.uk 6
    7. 7. Well… almost ‘Expected’ appliance use – On average Actual appliance consumption – Mean l/day – For a few micro-measured households So… – Consumption = ƒ(occupancy) + ƒ(appliances) But Source: Shove & Medd, 2005 7
    8. 8. The trouble with averages… 5 ‘average’ households – but they do different things So to reduce demand… – What to target? – Who to target? – How to target them? Source: Shove & Medd, 2005 Now… – Consumption = ƒ(occupancy * wpd) + ƒ(appliances * wpd) – Where wpd = What People Do 8
    9. 9. But what do people do? Does this tell us? Social practices – Habits – Routines – Neither fully conscious nor reflective – Constraints & inter-dependences – “Why people don’t do what they ‘should’” (Jim Skea, 2011) 9
    10. 10. Washing practices 2011 survey – N = 1800 “7 a week” 7 showers + 1 bath Do washing practices cluster? Source: ESRC Sustainable Practices Group Water Survey, 2011 www.sprg.ac.uk 10
    11. 11. Washing practice clusters Dimensions Whole sample – Frequency – Diversity – Technology – Outsourcing Source: ESRC Sustainable Practices Group Water Survey, 2011 www.sprg.ac.uk 11
    12. 12. Washing practice clusters Dimensions – Frequency – Diversity – Technology – Outsourcing Source: ESRC Sustainable Practices Group Water Survey, 2011 www.sprg.ac.uk 12
    13. 13. Washing practice clusters Dimensions – Frequency – Diversity – Technology – Outsourcing Explain – ~ 20% l/day variation Source: ESRC Sustainable Practices Group Water Survey, 2011 www.sprg.ac.uk 13
    14. 14. But… Cluster membership – is not easy to predict Low Attentious High Low Out and Frequency Cleaning Frequency Frequency About Showering Bathing BathingAge  Number of children  Household Composition  GenderNumber of earnersNumber of cars  AccommodationTenure Environmental values   14
    15. 15. Lessons from water: Volume ~= ƒ(occupancy) + ε – ‘Attitudes’ are not that relevant Appliances provide a substrate for… – What people do - social practices Help to explain variation (ε) • Across ‘similar’ households • With similar appliances • And similar accommodation Are habitual, routine & not fully conscious nor reflective • So difficult to change 15
    16. 16. Implications for Energy Hot water! You can eco-tech all you like – But it’s what people do with it that matters Source: A.S. Bahaj, P.A.B. James (2007) “Urban energy generation: The added value of photovoltaics in social housing” Renewable and Sustainable Energy Reviews 11: 2121-2136 16
    17. 17. Implications for Energy Hot water! You can eco-tech all you like – But it’s what people do with it that matters Source: A.S. Bahaj, P.A.B. James (2007) “Urban energy generation: The added value of photovoltaics in social housing” Renewable and Sustainable Energy Reviews 11: 2121-2136 17
    18. 18. Implications for Energy Hot water! You can eco-tech all you like – But it’s what people do with it that matters Source: A.S. Bahaj, P.A.B. James (2007) “Urban energy generation: The added value of photovoltaics in social housing” Renewable and Sustainable Energy Reviews 11: 2121-2136 18
    19. 19. Implications for Energy Hot water! You can eco-tech all you like – But it’s what people do with it that matters H2 - low demand - little potential for shifting? Source: A.S. Bahaj, P.A.B. James (2007) “Urban energy generation: The added value of photovoltaics in social housing” Renewable and Sustainable Energy Reviews 11: 2121-2136 19
    20. 20. Implications for Energy Hot water! You can eco-tech all you like – But it’s what people do with it that matters H2 - low demand - little potential for shifting? H4 -high, peaky demand - potential for shifting? Source: A.S. Bahaj, P.A.B. James (2007) “Urban energy generation: The added value of photovoltaics in social housing” Renewable and Sustainable Energy Reviews 11: 2121-2136 20
    21. 21. Implications for Energy Hot water! You can eco-tech all you like – But it’s what people do with it that matters Smart Demand needs a handle on – Habits, routines – Barriers, constraints and flexibility 21
    22. 22. Implications for Energy Hot water! You can eco-tech all you like – But it’s what people do with it that matters Smart Demand needs a handle on – Habits, routines – Barriers, constraints and flexibility – Networks of demand And ways of ‘auto-targeting’ interventions – That don’t rely on ‘demographics’ + ‘values’ – A market of 1? – Smart Monitoring? 22
    23. 23. Thank you Dr Ben Anderson (b.anderson@soton.ac.uk) www.energy.soton.ac.uk – SPRG • Sustainable Practices Research Group • www.sprg.ac.uk – DANCER • Digital Agent Networking for Customer Energy Reduction (EPSRC) • dancerproject.wordpress.com 23

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