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Domotics (Home Automation and Energy conservation)

Domotics (Home Automation and Energy conservation). René Kamphuis ECN-DEGO; Energie onderzoek Centrum Nederland Duurzame Energie in de Gebouwde Omgeving Integrale Concepten/IT & Energie kamphuis@ecn.nl. Developments/drivers Domotics and energy management Automation of energy-functions

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Domotics (Home Automation and Energy conservation)

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  1. Domotics (Home Automation and Energy conservation) • René Kamphuis • ECN-DEGO; Energie onderzoek Centrum Nederland • Duurzame Energie in de Gebouwde Omgeving • Integrale Concepten/IT & Energie • kamphuis@ecn.nl

  2. Developments/drivers Domotics and energy management Automation of energy-functions Potential Conclusions/benchmark figures Contents

  3. Development: Embedding of renewables

  4. Step 1: Internet plus World Wide Web Universal base infrastructure Step 2: Pervasive/ubiquitous computing Every appliance has built-in “invisible” computing power Step 3: Ambient intelligence Local intelligence plus global communication Semantic Web: next generation web Data exchange-> Behavior based on Web-semantics Intelligent Agents: computers and other appliances “talk”, negotiate, take decisions and cooperate with each other and people Step 4: Information systems => Information “eco”Systems Clusters of cooperating people and systems Systems are not aware of user context Development: WWW

  5. Development: Price volatility liberalised energy markets

  6. Price dynamics: Enlarged

  7. Price dynamics: Clustered

  8. Price dynamics: residential customer view

  9. What does the user want: Top-7 of energy related services 1. Energy at the lowest possible cost …… 2. (Automated) Energy saving advice 3. Control of power usage to cheaper periods 4. Low cost Internet using power line carrier data-communication 5. Green energy ….. 6. Electronic warning/burglary alarm 7. Real-time energy Usage survey …. 8….14 Different control functions at a distance

  10. Centralised energy management • Common in utility building sector • Energie management projects typically yield a 10-20 % saving • Better embedding of small scale renewable generation and storage • Cost efficient price-/contractcontrol • Costreduction for customers • Risico management energycompany • Energy cost are becoming more important; variation in usage/load factor • Feedback about usage leads to (manifest) reduction • Role of IT • Automated saving • Follow user preferences intelligently • Connects to external information

  11. What can conservation rate can be reached ??

  12. Input: Pricepaths (t) Required demand(t) and --demand articulation(t)-- Cleaning and drying Cooling Home heating and air condition Embedding of local generators (PV, micro-CHP) Output Setpoints appliances Switching moments E-Box energy control module simulations; http://www.ecn.nl/library/reports/2003/c03017.html

  13. Validation: Electrically produced hot tapwater (€ct/100 liter)

  14. E-Box Heating + powerdelivery/consumption (€ct/dag)

  15. E-Box Concerted consumption and generation

  16. E-Box Buffering (€ct/dag)

  17. Input: Pricepaths (t) Required demand(t) and demand articulation(t) Cleaning and drying Cooling Building heating and air condition Embedding of local generators (PV, micro-CHP) Output Setpoints appliances Switching moments E-Box energy control module simulations

  18. Dependent upon appliances (ten’s of Euro’s/jaar) Frequency Controllability Embedding in life and usage style Connection with lighting control Even in double-tariff strategy considerable contribution: Potential increases with more tariffdifferentiation Usage of buffers Possibilities voor 1-1 concerted clusters Direct feedback to users Energy management potential

  19. Predictive strategy determination for control of thermal comfort within an enhanced context heating/cooling ventilation automated solar entrance Integrated in user life style Baseerd upon a similar concept for utility buildings (SMART-project) User put in role of judge of inner climate; not as a system C-Box: Thermal innerclimate module http://www.ecn.nl/library/reports/2003/c03036.html

  20. C-Box: Thermal innerclimate module • Comfort model scope: • Physical building model (pre-emptive; predictive) • Walls • Air transport • Usage pattern of home • User model (individually, comfort acc. to Fanger) • lifestyle • activity pattern • External data • Expected temperature, cloud coverage (external DG-RES) • Real-time energy prices • Optimalisation upon utility function: • Thermal inner comfort and conservation target

  21. C-Box: Thermal innerclimate module; variants 1: 2: 3:

  22. C-Box: Thermal innerclimate module; conclusions • Variant 2: • Automatically gaining of energy reduction target (w.r.t. graaddagen) • Winter: • 5 % savings due to more elaborated control using PMV instead of T • Early-/afterseason: • 9 % savings by better control using knowledge about heating characteristics and utilizing solar radiation • Variant 3: • Summer: • Cooling of building mass by enhanced nightventilation • Avoidance of overheating in the afternoon • Demand ventilation • More exact, momentaneous comfort control • In case of ideal interaction with users and more actuators: an extra 5-15 procent savings

  23. Mobile care applications Tele-xxxx gateways Home automation for energy saving will not be invested in, if not combined with other applications !!

  24. C-domotics Conventional Lock, Alarming, home automation living functions E-domotics Conventional and emphasis on E-saving Same as C-, but now interfaced to energy consuming appliances and using in a smart way to conserve energy AE-domotics Advanced E-domotics Added intelligenence geared at energy conservation (expected outside temperatuure/ solar incidence) NIDO-project: http://www.ineigenomgevingoudworden.nl/upload/attachments/1083669207.pdf

  25. Heating -> better control to desired comfort-profile over 24 hours Demand ventilation on the basis of measured air quality Lighting Sunblinds Maintain thermal comfort by avoiding overheating Preheating; winter/spring/autumn Not into account: Avoided cooling in summer Automation of energy functions; Where is there a potential saving??

  26. NOVEM referentie galerijwoning Consumption data Physical characteristics (walls, glazings, doors) Environment (whole reference year) Lifestyle inhabitants (SCP-figures time spending) Economic Average Spillin BEK- and BAK-consumption figures; electricity an natural gas Simulation met TRNSYS;Obtain reliable estimates

  27. Potential.Cost and environmental key figures

  28. Summary NIDO (percentual savings)

  29. No silver bullet Has to be integrated with other in home applications to be viable Need for a PC-like control gateway computer Savings closely related to life style Potential from 0 up to 10-15 percent in the Dutch sitation Conclusions

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