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:
Empowering the Energy Consumer Professor Gregory O’Hare CLARITY: Centre for Sensor Web Technologies Context Sensitive Service Delivery November
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;
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
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
Home Deployment 7 Kettle Mirowave + toaster Electric oven + shower Shower + vacuum Kettle Electric Oven Electric Boiler READY: Recognition of Electrical Appliances DYnamically
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
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
In Home Display CONTEXTUAL COMPARISON COST TO USER HISTORICAL QUERIES
Effecting Change 5-15% Reduction in Electricity Consumption
12 Usr1 BACK FROM TRAINING Usr2 Life Patterns GETTING READY FOR SATURDAY MUSIC SESSION
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
Smart Cushion Colour Examples
My More Photogenic Colleagues…..
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
CLARITY EU - EnPROVE EnPROVE: Maximising return of investment (ROI) when investing on energy saving solution
CLARITY - FIEMSER CSTB THALES TECNALIA Labein Fraunhofer Philips Acciona TENESOL FIEMSER (Friendly Intelligent Energy Management System for Existing Residential Buildings)
CLARITY - HOBNET RACTI Ericsson Mandat International Sensimode University College Dublin University of Geneva University of Edinburgh
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
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
WSN Middleware: SIXTH
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
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.
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.
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.
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.
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
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.
Wattics : A Clarity Spin Out
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;