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Metering, Monitoring and Making Sense of Energy Use in ‘Mixed-Use’ Buildings

Metering, Monitoring and Making Sense of Energy Use in ‘Mixed-Use’ Buildings. Rajesh K. Gupta Professor & Chair, Computer Science & Engineering Associate Director, California Institute for Telecommunications & Information Technology University of California, San Diego. Our Team . Bharath.

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Metering, Monitoring and Making Sense of Energy Use in ‘Mixed-Use’ Buildings

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  1. Metering, Monitoring and Making Sense of Energy Use in ‘Mixed-Use’ Buildings • Rajesh K. Gupta • Professor & Chair, Computer Science & Engineering • Associate Director, California Institute for Telecommunications & Information Technology University of California, San Diego Our Team Bharath Seemanta Thomas Kaisen John Sathya Yuvraj Agarwal, Rajesh Gupta

  2. Buildings are an important research focus • All electricity in the US: 3,500 TWh • ~500 power plants @7TWh • Buildings: 2,500 TWh • All electronics: 290 TWh BuildSys 1 PC per 200 sq. foot 1 PC = $100 1W saved = ~2W less imported = 5W less produced. Bruce Nordman, LBNL $3/sq. foot • Buildings consume significant energy • >70% of total US electricity consumption • >40% of total carbon emissions

  3. Energy Dashboard http://energy.ucsd.edu

  4. Looking across 5 types of buildings more IT From: Yuvraj Agarwal, et al, BuildSys 2009, Berkeley, CA.

  5. Modern Buildings Are IT Dominated:50% of peak load, 80% of baseload

  6. Two Steps to Improving Energy Efficiency • Reduce energy consumption by IT equipment • Servers and PCs left on to maintain network presence • Key Idea: “Duty-Cycle” computers aggressively • SleepSever: maintains seamless network presence • Reduce energy consumption by the HVAC system • Energy use is not proportional to number of occupants • Key Idea: Use real-time occupancy to drive HVAC • Synergy wireless occupancy node

  7. Duty Cycling: Processors, HVAC • Why not power-down machines that are not working? • Or power-down building HVAC systems • Runs into several use model problems • “Always ON” abstraction of the internet • Unlike light-bulb, ‘when not in room, turn off the light’ • Use model for the user/application and the infrastructure are different • Network, enterprise system maintenance: distributed control of duty-cycling has its own usability problems.

  8. Collaborating Processors • Fundamental Problem: Our Notions of Power States • Hosts (PCs) are either Awake (Active) or Sleep (Inactive) • Power consumed when Awake = 100X power in Sleep! • Users want machines with the availability of active machine, power of a sleeping machine. Host PC wakeupfilters Appln. stubs Apps Somniloquy daemon Embedded OS, including networking stack Operating system, including networking stack Embedded CPU, RAM, flash Host processor, RAM, peripherals, etc. Somniloquy SleepServers Secondary processor X86 Network interface hardware ARM Maintain availability across the entire protocol stack, e.g. ARP(layer 2), ICMP(layer 3), SSH (Application layer)

  9. Stateful applications: Web download “stub” on the gumstix 200MB flash, download when Desktop PC is asleep Wake up PC to upload data whenever needed 92% less energy than using host PC. 1 600 1200 1800 2400 Increase battery life from <6 hrs to >60 hrs • Somniloquy exploits heterogeneity to save power and maintain availability

  10. SleepServersfor Enterprises: Architecture Respond:ARPs, ICMP, DHCP Wake-UP:SSH, RDP, VoIP call Proxy: Web/P2P downloads, IM

  11. Average Power 96 Watts Average Power 26 Watts DE Total estimated Savings for CSE (>900PCs) : $60K/year Deployed SleepServersacross 50 users Energy Savings: 27% - 85% (average 70%)

  12. Scenario: CSE Energy Use Reductions • Deploy Somniloquy / Sleepserver • Machine room : 142 kW  71 kW • PC Plug loads : 130 kW  70 kW • Ventilation system: • New fans, chillers : 65 kW  52 kW • Lighting: • Fluorescent lighting  LED • Motion-detector controlled hallway lighting evenings & weekends: 50 kW  11 kW 80 kBTU/ft2 42 kBTU/ft2

  13. Could CSE become a ZNEB? • Solar energy : 2700 m2 roof 111 kBTU/ft2 • Solar PhotoVoltaic: 20% efficient 22 kBTU/ft2 • How do we achieve 42 kBTU/ft2 ? • Tracking solar PV : add 30% irradiance 28 kBTU/ft2 • Increase PV efficiency : 29% efficient 42 kBTU/ft2 Dramatic improvements in energy efficiency and solar conversion efficiency needed for ZNEB

  14. Wait for global warming or better solar cells? Is that it?

  15. Buildings 2.0: Occupancy-Driven Smart Buildings Use occupancy and activity to drive energy efficiency in HVAC system usage. Reduced cooling when a room is empty. Increased HVAC when a room has more occupants. Occupancy Performability Adaptive Envelope When there are less people in the room, reduce cooling. When there are more, increase cooling as required to maintain comfort.

  16. HVAC: Central control and Static Schedules HVAC ON 5:15AM 6:30PM HVAC starts at this time HVAC stops at this time Un-Occupied Periods Some people actually arrive 2 hours later!

  17. Energy Consumption in a Mixed-Use Building • HVAC loads significant: Electrical ( >25%) and Thermal • Electrical (air handlers, fans, etc), thermal (chilled water loop) • HVAC load independent of the actual occupancy of building

  18. Relating HVAC Energy Use and Occupancy • Controlled experiment in CSE over 3 days: Fri, Sat, Sun • Friday: Operate HVAC system normally • Weekend: HVAC duty-cycled on a floor-by-floor basis • 1 floor (10am – 11am), 2 floors (11am – 12pm), ….., ….. • Occupancy affects HVAC energy • Points to the benefits of fine-grained control

  19. Occupancy Driven HVAC control • Synergy Occupancy Node • CC2530 based design • 8051 uC + 802.15.4 radio • Zigbeecompliant stack • PIR + Magnetic reed switch • Key Design Requirements: • Inexpensive (less than 10$) • Battery powered – 4-5 year life • Multiple sensors for accuracy

  20. Accuracy of Occupancy Detection • Over 96% occupancy accuracy with Synergy node

  21. Deployment across 2nd floor of CSE Floormap: 2nd Floor - 50 Offices, 20 Labs. - 8 Synergy Base Stations Control individual HVAC zones based on real-time occupancy information!

  22. Occupancy affects energy use. Plug loads vary but can be detected accurately.

  23. Implementation: Interfacing with the EMS Occupancy Data Analysis Server (ODAS) Database Occupancy nodes Windows Server with OPC Tunneller Sheeva Plug base stations Database BACnet OPC DA Server HVAC Control … Metasys ADX NAE NAE NAE • Occupancy Data Analysis Server • Database to store mapping , MetaSys EMS – proprietary protocols • OPC tunnel to communicate with EMS • Actuation based on modifying status for individual thermal zones • Use priorities levels -- co-exist with current campus policies. • Occupancy data not visible externally

  24. HVAC Energy Savings Estimated 40% savings if deployed across entire CSE! Detailed occupancy can be used to drive other systems. HVAC Energy Consumption (Electrical and Thermal) during the baseline day. HVAC Energy Consumption (Electrical and Thermal) for a test day with a similar weather profile. HVAC energy savings are significant: over 13% (HVAC-Electrical) and 15.6% (HVAC-Thermal) for just the 2nd floor

  25. Summary • HVAC energy not proportional to occupancy • Use of static schedules is common • Significant energy wasted • Fine-grained occupancy driven HVAC control • Occupancy node: accurate, low cost, wireless • Interface with existing building SCADA systems • Evaluation: Deployment in the CSE building/UCSD • 11.6% (electrical) and 12.4% (thermal) savings • Estimate over 40% savings across entire building

  26. Some (Recent) Pointers • “Evaluating the Effectiveness of Model-Based Power Characterization”, USENIX Advanced Technical Conference (ATC), 2011. • "Duty-Cycling Buildings Aggressively: The Next Frontier in HVAC Control" , ACM/IEEE IPSN/SPOTS, 2011. • "Occupancy-Driven Energy Management for Smart Building Automation" , ACM BuildSys 2010. • "SleepServer: A Software-Only Approach for Reducing the Energy Consumption of PCs within Enterprise Environments" , USENIX ATC, 2010. • "Cyber-Physical Energy Systems: Focus on Smart Buildings" , DAC 2010. • "The Energy Dashboard: Improving the Visibility of Energy Consumption at a Campus-Wide Scale“, ACM BuildSys 2009. • "Somniloquy: Augmenting Network Interfaces to Reduce PC Energy Usage" , NSDI 2009.

  27. Thank You An exciting time to be doing research in embedded systems with tremendous potential to solve society’s most pressing problems. Rajesh Gupta gupta@ucsd.edu

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