thermal modeling for a hvac controlled real-life auditoriumlu/cse521s/slides/auditorium.pdf · = 3...

Thermal Modeling for a HVAC Controlled Real-life Auditorium

ChenyangLu

EndeavoronSmartBuilding

1.  Instrumenting an auditorium

2.  Modeling spatiotemporal thermal dynamics

3.  Occupancy-based energy saving for HVAC

4.  Micro-metering an apartment

Challenges

Ø Heat, Ventilation and Air Conditioning (HVAC) consumes 33% of building energy.

Ø HVAC control relies on accurate thermal models.

Ø  Large open spaces have complex spatiotemporal dynamics.q  Examples: auditoriums, theatres, open offices, lobbies.

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Week-long temperature trace at different locations in an auditorium.

Spa6alVaria6oninanAuditorium

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•  Temperature differs by ~2°C despite HVAC control.•  Unique challenges in large open spaces.

ExperimentalApproach

1.  Deploy 34 sensors in an auditorium for over three months.

2.  Collect multimodal data to capture fine-grained spatiotemporal dynamics under HVAC control .

3.  Identify thermal model based on data from all sensors.

4.  Simplify model through sensor selection.

5

Instrumen6nganAuditoriumØ  Emerson wireless sensors: temperature, humidity.Ø HVAC sensors: air flow rate and temperature.

Ø Wireless camera: occupancy and lighting (on/off).

6

Thermostats

Camera

WirelessSensors

Brauer Hall1/2013 - 5/2013

WirelessMonitoringSystem

Base Station

Particle Sensor

Temperature Sensor

Temperature Sensor

Brauer Hall Database

Wireless LinksWireless Links

Wire

less L

inks

Wireless Links

Auditorium

CO2 Sensor CO2 Sensor

Data Analysis

Empirical Study

Humidity SensorTemperature Sensor

surveillance camera

Occupancy

7

Instrumen6ngtheAuditoriumØ  Environmentalmonitoring

q  34temperaturesensorsq  15humiditysensorsq  1condensa;onpar;clecounterq  2CO2sensors

Ø  HVAC:airflowrate,airtemperature

Ø Occupancyfromcamera

Ø  Databaseq  Sensorscon;nuouslyfeeddatatodatabaseovertheInternetq  Visualiza;onthroughwebinterface

8

LargeMul6-modalDataset

Ø Longitude: >8 months of data

Ø Fine grainedq Temperature: 1 reading per 1/3 degree changeq Humidity: 1 reading per 1% degree change

q Particle: 3 readings/secondq CO2: 2 readings/hour

q HVAC air flow: 4 readings/hour

q Occupancy: 4 photos/hour

9

Temperature&HumiditySensor

10

Ø  Emerson wireless thermostatsq  Repurposed for distributed monitoring

Ø  Capture fine-grained spatiotemporal dynamicsq  Improve HVAC model and control

2/25/13 – 3/3/13

WirelessCondensa6onPar6cleCounter

11

butanol

particle number concentration (µgm-3)display

inlet

wireless transmitter

Instrumentspecifica6ons•  Uses butanol, single-count, and

photometrictechnology,tocountairborne par;cle numberswith adiameterfrom0.07to3µm

•  Fastresponse;me(<13seconds)

•  Semi-portable

•  High-resolu;on(1Hz)data

Particle sources

people

furnishings (chairs, carpet)

hot food outdoors (traffic, dust)

HVAC

resuspension

•  RetrofiHedwithBluetooth.

•  HelpunderstandimpactsofHVAConairquality

Par6cleNumberConcentra6ons

12

HVAC switching to off mode (~ 9pm)

Sunday

Midterm exam

OneWeekofDataTraces(2/25–3/3)

13 13

Temperature

Occupancy

CO2

Particle

Air flow

PriorModelingApproaches

Ø  Principle-driven: rely on detailed knowledge of building design and materials.

Ø Data-driven: estimate model based on data.q  Assume same temperature per room: ignore spatial variations and

interactions within a large space.q  Divide space into zones: reply on known inter-zone interactions.

15

ModelIden6fica6on

Ø Model identification based on training dataq  Minimize modeling error with least square optimization

q  Solved using CVX toolbox for Matlab

Ø  Tradeoff between model complexity and accuracyq  1st order model à simple

q  2nd order model à capture more complex dynamics

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T(k+1) = AT(k) + BU(k)

Temperature T(k)

U(k): air flow rate & temperature, occupancy, light.

Estimated temperature T(k+1)

1stvs.2ndOrderModel

Ø  2nd order model more accurately captures the spatiotemporal dynamics in the auditorium.

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Measuredvs.predictedtemperatureon2/28/13

ModelSimplifica6on

Ø Disadvantages of fine-grained models based on all sensorsq  Complex model is unsuitable for control design.

q  Challenge in maintaining numerous sensors.

Ø Approach: simplifying model through sensor selectionq  Sensor data have strong correlations.

q  Select a subset of sensors to capture spatiotemporal dynamics.

q  Identify thermal model based on selected sensors.

Ø Advantage of model simplificationq  Practical for HVAC control.

q  Only need to keep the selected sensors during operation.

q  Dense sensor network needed only initially to collect training data.

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SensorSelec6onbasedonClustering

1.  Spectral clustering based on sensor data.q  Value: group sensors with similar temperature values.

q  Correlation: group sensors whose data traces follow similar trends.

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Correla6on-basedSensorClustering

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Twoclusters Temperature correlation

SensorSelec6onbasedonClustering

1.  Spectral clustering based on sensor data.q  Value: group sensors with similar temperature values.

q  Correlation: group sensors whose data traces follow similar trends.

2.  Select a sensor from each cluster.q  Stratified Random Selection (SRS): randomly choose one.

q  Stratified Mean Selection (SMS): select the sensor whose data is the closest to the cluster mean.

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ModelSimplifica6on

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•  Clustering outperforms Random Selection (RS)•  Stratified Mean Selection (SMS) is more accurate than Stratified

Random Selection, especially for large clusters.

Summary:ThermalModeling

Ø  Large open spaces have complex spatiotemporal dynamics.

Ø  Data-driven thermal modeling for large open spaces. 1.  Sensor network captures spatiotemporal dynamics.

2.  Sensor selection based on data clustering.

3.  Model identification based on data of selected sensors.

Ø  Validated on data collected from a real-life auditorium.

Ø  Exciting opportunities aheadq  Optimize HVAC control

q  Leverage air quality sensing for more aggressive energy saving

23 Y.Fu,M.Sha,C.Wu,A.KuYa,A.Leavey,C.Lu,H.Gonzalez,W.Wang,B.Drake,Y.ChenandP.Biswas,ThermalModelingforaHVACControlledReal-lifeAuditorium,ICDCS2014.

HVACEnergyWaste

Ø Current HVAC operates on fixed scheduleq  On (occupied mode) during daytime (6am-9pm)q  Off (non-occupied mode) at night

Ø  But the auditorium is vacant 80% of the time during the day!

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Note:OccupancyFollowsCalendar

Ø Calendar predicts actual occupancy at >98% accuracyq  Validated by camera

Seminar

Class Meeting

26

Ø  Preconditioning: Start HVAC Tp before an eventq  Tp: time needed to reach the temperature set point

q  Tp = 3 hours for the auditorium based on data traces

Ø  Save energy: Turn off HVAC if >Tp till next eventq  Turn off HVAC immediately after the last event each dayq  HVAC remains off during weekends

Ø Avoid thrashing: remains on if next event is within Tp

q  Maintain comfort

q  Reduce unnecessary switching

ScheduleHVACbasedonCalendar

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Example

Turnoffaaerlastevent

Precondi;oning3hours

Intervalbetweeneventslessthan3hours

On

Off

Sun Sat

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q  Turning off HVAC immediately after last event à 36%q  Turning off HVAC on Sat/Sun à 34%

q  Turning on HVAC late in the morning à 8%

78%EnergySavingover6Weeks

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SmartHome:Objec6ves

Ø  Save energy while maintaining comfort.

Ø Close the loop: intelligent control of appliances.

Ø Human centered: incentivize residents to save energy.

Ø  Internet of Things: integrate sensors, appliances, cloud, and smartphones.

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TheInternetofThings

Weather station AC

Power meter

BT / BTL listener

15.4

WiFi microserver

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UI

Pilot–Components

Ø ACme – Berkeley power meterq  Based on the Epic core

q  Runs TinyOS

q  IPv6 over mesh network

Ø  Raspberry Piq  Very popular microserver

Ø  Ethernet connection to apartment routerØ Amazon EC2 as the cloud

Ø Measuring major appliances power�consumption

Power meter

microserver

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thermal modeling for a hvac controlled real-life auditoriumlu/cse521s/slides/auditorium.pdf · = 3...

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