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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.
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Instrumen6nganAuditoriumØ Emerson wireless sensors: temperature, humidity.Ø HVAC sensors: air flow rate and temperature.
Ø Wireless camera: occupancy and lighting (on/off).
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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
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Instrumen6ngtheAuditoriumØ Environmentalmonitoring
q 34temperaturesensorsq 15humiditysensorsq 1condensa;onpar;clecounterq 2CO2sensors
Ø HVAC:airflowrate,airtemperature
Ø Occupancyfromcamera
Ø Databaseq Sensorscon;nuouslyfeeddatatodatabaseovertheInternetq Visualiza;onthroughwebinterface
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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
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Temperature&HumiditySensor
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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
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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
EndeavoronSmartBuilding
1. Instrumenting an auditorium
2. Modeling spatiotemporal thermal dynamics
3. Occupancy-based energy saving for HVAC
4. Micro-metering an apartment
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.
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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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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.
EndeavoronSmartBuilding
1. Instrumenting an auditorium
2. Modeling spatiotemporal thermal dynamics
3. Occupancy-based energy saving for HVAC
4. Micro-metering an apartment
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
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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
29
EndeavoronSmartBuilding
1. Instrumenting an auditorium
2. Modeling spatiotemporal thermal dynamics
3. Occupancy-based energy saving for HVAC
4. Micro-metering an apartment
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.
31
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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