Metering Monitoring and Making Sense of Energy Use

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 Yuvraj Agarwal, Rajesh Gupta Thomas Bharath Seemanta Sathya John Kaisen

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

Energy Dashboard http: //energy. ucsd. edu

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

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

Two Steps to Improving Energy Efficiency 1. Reduce energy consumption by IT equipment q q q 2. Servers and PCs left on to maintain network presence Key Idea: “Duty-Cycle” computers aggressively Sleep. Sever: maintains seamless network presence Reduce energy consumption by the HVAC system q q q Energy use is not proportional to number of occupants Key Idea: Use real-time occupancy to drive HVAC Synergy wireless occupancy node 6

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

Collaborating Processors q q Fundamental Problem: Our Notions of Power States q Hosts (PCs) are either Awake (Active) or Sleep (Inactive) q Power consumed when Awake = 100 X power in Sleep! Users want machines with the availability of active machine, power of a sleeping machine. Somniloquy Sleep. Servers Host PC Apps Somniloquy daemon Operating system, including networking stack X 86 Host processor, RAM, peripherals, etc. Maintain availability across the entire protocol stack, e. g. ARP(layer 2), ICMP(layer 3), SSH (Application layer) wakeup filters Appln. stubs Secondary processor Embedded OS, including networking stack Network interface hardware Embedded CPU, RAM, flash ARM

Power Consumption (Watts) Host Only Stateful applications: Web download “stub” on the gumstix Somniloquy 200 150 200 MB flash, download when Desktop PC is asleep 100 50 1 92% less 600 601 1200 1800 1201 Wake up PC to upload data whenever needed 2400 1801 Timethan (seconds) energy using 2401 host PC. Increase battery life from <6 hrs to >60 hrs Somniloquy exploits heterogeneity to save power and maintain availability

Sleep. Servers for Enterprises: Architecture Respond: ARPs, ICMP, DHCP Wake-UP: SSH, RDP, Vo. IP call Proxy: Web/P 2 P downloads, IM

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

Scenario: CSE Energy Use Reductions • Deploy Somniloquy / Sleepserver – Machine room – PC Plug loads 80 k. BTU/ft 2 : 142 k. W 71 k. W : 130 k. W 70 k. W • Ventilation system: – New fans, chillers : 65 k. W 52 k. W • Lighting: – Fluorescent lighting LED – Motion-detector controlled hallway lighting 42 k. BTU/ft 2 evenings & weekends: 50 k. W 11 k. W 12

Could CSE become a ZNEB? • Solar energy : 2700 m 2 roof • Solar Photo. Voltaic: 20% efficient • How do we achieve 42 k. BTU/ft 2 ? – Tracking solar PV : add 30% irradiance – Increase PV efficiency : 29% efficient 111 k. BTU/ft 2 22 k. BTU/ft 2 28 k. BTU/ft 2 42 k. BTU/ft 2 Dramatic improvements in energy efficiency and solar conversion efficiency needed for ZNEB 13

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

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. 15

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

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 17 17

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 (10 am – 11 am), 2 floors (11 am – 12 pm), …. . • Occupancy affects HVAC energy – Points to the benefits of fine-grained control 18 18

Occupancy Driven HVAC control Synergy Occupancy Node • CC 2530 based design • 8051 u. C + 802. 15. 4 radio • Zigbee compliant stack • PIR + Magnetic reed switch Key Design Requirements: • Inexpensive (less than 10$) • Battery powered – 4 -5 year life • Multiple sensors for accuracy 19 19

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

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Deployment across 2 nd floor of CSE Floormap: 2 nd Floor - 50 Offices, 20 Labs. - 8 Synergy Base Stations Control individual HVAC zones based on real-time occupancy information! 22 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 Sheeva Plug base stations Windows Server with OPC Tunneller Database BACnet OPC DA Server HVAC Control NAE … NAE Metasys ADX Occupancy Data Analysis Server • Database to store mapping , Meta. Sys 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 24

HVAC Energy Savings 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 2 nd floor Estimated 40% savings if deployed across entire CSE! Detailed occupancy can be used to drive other systems. 25 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 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 Build. Sys 2010. • "Sleep. Server: 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 Build. Sys 2009. • "Somniloquy: Augmenting Network Interfaces to Reduce PC Energy Usage" , NSDI 2009. 27 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