Use of the Probability of Breakdown Concept in Ramp Metering
Metering, Monitoring and Making Sense of Energy Use in ‘Mixed-Use’ Buildings Rajesh K. Gupta...
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Transcript of Metering, Monitoring and Making Sense of Energy Use in ‘Mixed-Use’ Buildings Rajesh K. Gupta...
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
Yuvraj Agarwal,Rajesh Gupta Thomas
Our Team
BharathSeemanta JohnSathya Kaisen
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
Buildings consume significant energy
>70% of total US electricity consumption >40% of total carbon emissions
Bruce Nordman, LBNL
BuildSys
1 PC per 200 sq. foot1 PC = $1001W saved = ~2W less imported
= 5W less produced.
$3/sq. foot
Looking across 5 types of buildings
From: Yuvraj Agarwal, et al, BuildSys 2009, Berkeley, CA.
more IT
Two Steps to Improving Energy Efficiency
1. 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
2. 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
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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.
Collaborating Processors
Somniloquydaemon
Host processor,RAM, peripherals, etc.
Operating system, including networking
stack
Apps
Network interface hardware
Secondary processorEmbedded CPU, RAM,
flash
Embedded OS, including
networking stack
wakeupfilters
Appln. stubs
Host PC
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.
SomniloquySleepServers
X86
ARMMaintain availability across the entire protocol stack, e.g. ARP(layer 2), ICMP(layer 3), SSH (Application layer)
Somniloquy exploits heterogeneity to save power and maintain availability
3 225 447 669 891 111313351557177920010
40
80
120
160
200
Host Only Somniloquy
Time (seconds)
Pow
er C
onsu
mpti
on
(Watt
s)
1 600 1200 1800 2400
92% less energy than using host PC.
Increase battery life from <6 hrs to >60 hrs
Stateful applications:Web download “stub” on the gumstix 200MB flash, download when Desktop PC is asleep
Wake up PC to upload data whenever needed
SleepServers for Enterprises: Architecture
Respond: ARPs, ICMP, DHCPWake-UP: SSH, RDP, VoIP callProxy: Web/P2P downloads, IM
Average Power 26 Watts
Average Power 96 Watts
DE Total estimated Savings for CSE (>900PCs) : $60K/year
Deployed SleepServers across 50 usersEnergy Savings: 27% - 85% (average 70%)
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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
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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
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Buildings 2.0: Occupancy-Driven Smart BuildingsUse occupancy and activity to drive energy efficiency in HVAC system usage.
Increased HVAC when a room has more occupants.
Reduced cooling when a room is empty.
When there are less people in the room, reduce cooling. When there are more, increase cooling as required to maintain comfort.
OccupancyPerformabilityAdaptive Envelope
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HVAC: Central control and Static Schedules
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HVAC ON
5:15AM 6:30PM
Some people actually
arrive 2 hours later!
HVAC starts at this time Un-Occupied Periods
HVAC stops at this time
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Energy Consumption in a Mixed-Use Building
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• 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
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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
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Occupancy Driven HVAC control
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Key Design Requirements: • Inexpensive (less than 10$) • Battery powered – 4-5 year life • Multiple sensors for accuracy
Synergy Occupancy Node • CC2530 based design • 8051 uC + 802.15.4 radio • Zigbee compliant stack • PIR + Magnetic reed switch
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Deployment across 2nd floor of CSE
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- 50 Offices, 20 Labs. - 8 Synergy Base Stations
Control individual HVAC zones based on real-time occupancy information!
Floormap: 2nd Floor
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Implementation: Interfacing with the EMS
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NAE NAE
Windows Server with OPC Tunneller
BACnet OPC DA Server
HVAC Control
Occupancy Data Analysis Server (ODAS)
Database
Sheeva Plug base stations
Occupancy nodes
Metasys ADXNAE …
Database
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
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HVAC Energy Savings
Estimated 40% savings if deployed across entire CSE!Detailed occupancy can be used to drive other systems.
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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
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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
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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.
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An exciting time to be doing research in
embedded systems with tremendous potential to solve
society’s most pressing problems.
Thank You
Rajesh Gupta