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Empowering the Energy Consumer Professor Gregory O’Hare CLARITY: Centre for Sensor Web Technologies Context Sensitive Service Delivery November 2011 1.

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Presentation on theme: "Empowering the Energy Consumer Professor Gregory O’Hare CLARITY: Centre for Sensor Web Technologies Context Sensitive Service Delivery November 2011 1."— Presentation transcript:

1 Empowering the Energy Consumer Professor Gregory O’Hare CLARITY: Centre for Sensor Web Technologies Context Sensitive Service Delivery November

2 The Challenge How to empower the consumer; How to effect behavioural change; How to create and integrate the necessary infrastructure to support Autonomic Energy Management;

3 Architecture Main Fuse box Energy Monitor Local Processing: Load recognition, energy cost breakdown DB WWW Load descriptor database and Remote processing: Personalised recommendations, best tariff plan, load comparison Load descriptor database and Remote processing: Personalised recommendations, best tariff plan, load comparison Traditional Approach Retrofit building with intelligent sockets Our Approach Use a single plug-and-play electrical energy monitor connected to the main fuse box Traditional Approach Retrofit building with intelligent sockets Our Approach Use a single plug-and-play electrical energy monitor connected to the main fuse box

4 22 Participants

5 Appliance Signature A blend of derived parameters constitute the Unique Appliance Signature 1.Real Power P 2.Power Factor Pf 3.And so forth… 5

6 Deployment

7 Home Deployment 7 Kettle Mirowave + toaster Electric oven + shower Shower + vacuum Kettle Electric Oven Electric Boiler READY: Recognition of Electrical Appliances DYnamically

8 Testing the efficiency of the machine learning technique Display of neural network data : Fridge Microwave Kettle Heater Appliance activity Raw output: Direct output from READY Filter: Filtered output from READY > 83% accuracy READY testing Patented Technology: PCT/IE2011/ PCT/IE2011/ Patented Technology: PCT/IE2011/ PCT/IE2011/000041

9 CLARITY Deployments 22 domestic participants 15,840 sensor readings per house per day! We’re now gathering over 2 MILLION readings/week Data accurate to within 1% of Smart Meter Normal 5-7pm peak in electricity consumption

10 In Home Display CONTEXTUAL COMPARISON COST TO USER HISTORICAL QUERIES

11 Effecting Change 5-15% Reduction in Electricity Consumption

12 12 Usr1 BACK FROM TRAINING Usr2 Life Patterns GETTING READY FOR SATURDAY MUSIC SESSION

13 Ambient Feedback Through Smart Textiles MATERIAL SCIENCE Luminex light-emitting fabric Woven optical fibres ELECTRONIC ENGINEERING 4 pin multi-colour LED Zigbee communications Current power consumption compared against expected levels

14 Smart Cushion Colour Examples

15 My More Photogenic Colleagues…..

16 Leverage & Awards  Enterprise Ireland Commercialisation Plus Award  Three FP7 Awards in the Intelligent Building Space HOBNET, EnPROVE, FIEMSER  Dr Ruzzelli Winner of Globe Forum ‘Ireland Innovator’2010  Anthony Schoofs Ph.D Student Winner of prestigious Globe Sustainability Research Award 2011

17 CLARITY EU - EnPROVE EnPROVE: Maximising return of investment (ROI) when investing on energy saving solution

18 CLARITY - FIEMSER CSTB THALES TECNALIA Labein Fraunhofer Philips Acciona TENESOL FIEMSER (Friendly Intelligent Energy Management System for Existing Residential Buildings)

19 CLARITY - HOBNET  RACTI  Ericsson  Mandat International  Sensimode  University College Dublin  University of Geneva  University of Edinburgh

20 Autonomic Home Energy Management –Sharing of sensor data between appliances Door/window sensors from security system relevant to heating Smart lighting occupancy sensors used to turn off computer monitors/TVs –Outcome-oriented scheduling Scheduling based on when an outcome is desired E.g. User wants dishes washed before breakfast at 8am: program can be scheduled at any time before then. E.g. User wants house to be 20 degrees when they get home from work at 6pm: schedule heating to come on at appropriate time based on pricing, environmental conditions etc. –Balancing of conflicting appliances

21 Smart Meter Penetration Rates  North America will grow at a compound annual rate of 31.3 percent until 2015 to reach 78.3 million units at the end of the period.  North America has the world’s highest penetration  Asia-Pacific is projected to see the number of smart meters soar from a low level to million units by  North America will grow at a compound annual rate of 31.3 percent until 2015 to reach 78.3 million units at the end of the period.  North America has the world’s highest penetration  Asia-Pacific is projected to see the number of smart meters soar from a low level to million units by  European Parliament proposes that, 80% of all electricity customers should have smart meters by  2009 Sweden became the first country to achieve 100% penetration  Spain and Ireland are expected to display high volumes from 2011  European Parliament proposes that, 80% of all electricity customers should have smart meters by  2009 Sweden became the first country to achieve 100% penetration  Spain and Ireland are expected to display high volumes from 2011 Residential Energy Management: Home Area Networks: Analysis and Forecasts, Parks Associates Ablondi & Abid, 4Q, 2010 Smart Energy Homes A Market Dynamics Report, On World Oct. 2010, Hatler, Gurganious & Chi Residential Energy Management: Home Area Networks: Analysis and Forecasts, Parks Associates Ablondi & Abid, 4Q, 2010 Smart Energy Homes A Market Dynamics Report, On World Oct. 2010, Hatler, Gurganious & Chi

22 WSN Middleware: SIXTH

23 Intelligent Acquisition and Supply of Energy : Excess Microgeneration Homes with micro- generation capability may produce excess energy. Example: solar generation peaks during the daytime, but peak consumption is in mornings and evenings. Dilemma whether to: –Store excess energy (batteries, thermal storage, water heater) –Sell excess to utilities

24 Opportunistic Decision Making: Heat Planning Strategies Planning of heating. Desired temperature of 20 degrees Celsius by 8am. Example: tariff changes shortly before specified outcome. Option 1 (Full Heat): heat at full power to reach target temperature at exactly 8am.

25 Opportunistic Decision Making: Heat Planning Strategies Option 2 (Half Heat): Heat at a slower rate over a longer period. Less peak energy usage. Overall cost may be lower.

26 Opportunistic Decision Making: Heat Planning Option 3 (Heat and Maintain): Heat to desired temperature by tariff changeover. Peak consumption only to maintain heat, rather than raise temperature. More overall energy use, but costs are lower.

27 Opportunistic Decision Making: Heat Planning Strategies Full Heat strategy consumes all its energy at peak tariff. Half Heat balances consumption better between peak and off- peak prices. Heat and Maintain uses more energy overall, but most is off-peak. Storage adds complexity.

28 Intelligent Acquisition and Supply of Energy: Time of Use (TOU) Pricing –Installation of smart meters in existing homes allows for pre- published Time Of Use (TOU) pricing with a single supplier; –Example: CNT Energy Power Smart Pricing Program (Illinois, USA) TOU prices available for every hour of the day, published in advance the evening before –Only 30% of customers checked prices daily –Dynamic pricing will only change behaviour if handled automatically –TOU data could be scrapped from a variety of energing websites (UK, commercial)http://bmreports.com/bwx_reporting.htm https://il.thewattspot.com/login.do?method=showChart (US, residential)https://il.thewattspot.com/login.do?method=showChart (Ireland, commercial)http://www.sem-o.com/Pages/default.aspx

29 Intelligent Acquisition and Supply of Energy: Dynamic Pricing Strategies –Real-time Dynamic Pricing with Demand Response (suits Permanent/Immediate Consumption); –Pre-negotiation of blocks of energy for particular times (suits Schedulable/Permanent Consumption); –Conditional Tariffs Penalty-based: low price if peak consumption kept below a particular threshold with punitive rates if this is exceeded. Reward-based: keep peak consumption below a particular threshold and receive preferential off-peak rates, loyalty based incentives; –Tariff description standards and negotiation protocols need to be agreed upon across utilities and HEM manufacturers.

30 Wattics : A Clarity Spin Out

31 Conclusions Challenge: Dynamic pricing ultimately only changes behaviour if handled automatically; Effecting Behavioural Change is difficult; Opportunity: If in-home energy management is autonomic, dynamic pricing has greater scope for influencing consumption patterns; Collaborative Intelligent decision making between a network of smart objects underpins this opportunity;


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