Empowering the Energy Consumer

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

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 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 Local Processing: Load recognition, energy cost breakdown WWW Energy Monitor DB

4. 22 Participants

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

6. Deployment

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

8. Testing the efficiency of the machine learning technique READY testing Appliance activity > 83% accuracy Raw output: Direct output from READY Patented Technology: PCT/IE2011/000024 PCT/IE2011/000041 Filter: Filtered output from READY Display of neural network data : Fridge Microwave Kettle Heater

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

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

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 116.6 million units by 2015. • European Parliament proposes that, 80% of all electricity customers should have smart meters by 2020. • 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

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 • http://bmreports.com/bwx_reporting.htm (UK, commercial) • https://il.thewattspot.com/login.do?method=showChart(US, residential) • http://www.sem-o.com/Pages/default.aspx (Ireland, commercial)

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 energyfor 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;