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

Energy Dashboardhttp://energy.ucsd.edu

Looking across 5 types of buildings

From: Yuvraj Agarwal, et al, BuildSys 2009, Berkeley, CA.

more IT

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 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

Wait for global warming or better solar cells?

Is that it?

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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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Accuracy of Occupancy Detection

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• Over 96% occupancy accuracy with Synergy node

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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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Occupancy affects energy use.

Plug loads vary but can be detected accurately.

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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

gupta@ucsd.edu