an architecture to enable multi-view integration for sensor deployments in buildings
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An Architecture to Enable Multi-view Integration for Sensor Deployments in Buildings. Jorge Ortiz Tapia Conference Doctoral Consortium University of California, Berkeley April 3, 2011. Building energy consumption highly fragmented. - PowerPoint PPT PresentationTRANSCRIPT
An Architecture to Enable Multi-view Integration for Sensor
Deployments in BuildingsJorge Ortiz
Tapia Conference Doctoral ConsortiumUniversity of California, Berkeley
April 3, 2011
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• Integration of disparate data source
• Enable integrated data collection
• Simple integration with external systems (input and output)
• Building Management System captures Heat/cooling and ventilation
• Lighting systems• Miscellaneous electrical loads• Weather data, price, etc.
Building energy consumption highly fragmented
HVAC: 31.4%
+
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Main research question
• What is a good system architecture to– 1) Integrate multiple physical data streams– 2) Combine mathematical and physical
modeling with real-time data processing– 3) support a diverse set of monitoring and
control applications?• Energy efficient buildings
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What are the metrics?
• Generality– Supports the integration of many input/output sources
• Ease of use– Add/remove input sources, add/remove output targets– Use the metadata to make more informed queries– Querying/Cleaning/Modeling/Sharing the data
• Organizing principle: Everything looks like a distributed file system
4
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High-level system Architecture
weather
price
BMSZigbee
WirelessHART
Message Dispatcher
Storage
Security
Incoming Streams Manager
Data
repr
esen
tatio
n la
yer
weather
price
BMSZigbee
WirelessHART
Metadata Management
Stream processing
Model Manager
IntegrationModelingApplicationInterface
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• Fault detection study[Schein2005]
• Fault: Simultaneous heating and cooling– PID controllers on separate schedules
Why integrate?
Heating coil valvePosition varies
Outside-air mixerPosition varies
Cooling coil remains off
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Other opportunities for integration
• Human-activity classification– Electrical activity [Patel2007]– HVAC air pressure [Patel2008]– Water usage[Froehlich2008]– IP traffic and circuit-level activity [Kim2010]
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Savings with intelligent control
• SmartThermostat[Lu2010]– Combines motion sensors and contact switches to
reduce HVAC energy consumption by 28%
• Distributed Wireless Control for Building Energy Management [Marchiori2010]– Devices that share contextual information can
control a space more intelligently and save energy (up to 40% on certain appliances).
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Integration With CurrentSystems is Hard
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Commercial BMS Architecture
Field Level
Routing/Controllers
Applications
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Problems with BMS’s
• Not designed to collect all the data– Memory limit at control layer, application layer– Most information is lost through sense-point
“bundling” (averaging)• Burden on operator to manage
– Building manager is not a data analyst!– Must decide which signals to “trend/unbundle”,
monitor (set trigger)• Multi-signal fault detection done by human
operator
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The world is a nasty place
• State-of-art not designed for data collection• 30% of sensors are broken[BEMS2000]
– Mixed air reading errors +2.8 Celsius increases cooling energy consumption by 60% [Kao1983]
– Mixed are reading errors -2.8 Celsius increases heating energy consumption by 30% [Kao1983]
• Imperfect data, imperfect view of physical state– Large accouting errors, false diagnoses, poor
forecasting12
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Integration fills in the gaps• BLAST, DOE-2, EnergyPlus
– Integrates building data with model processing• BMS + EnergyPlus [ESL2002]
Fault Detection
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Lessons learned so far…
• Current work optimizes a particular system– More input sources used by control mechanism
• Cross-system information flow should be standard and not special case
• Developing “smarter” ad-hoc system is time consuming– Many mechanisms are duplicated
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System Architecture
11/30/10
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Building multiview integration
Electrical Load Tree
Environment and Activity
Climate plant
/SodaHall
/hvac
/CT /Chiller
/loadtree
/panel /xform
/spaces
/floor3/floor4
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Organizing the metadata
/SodaHall
/inventory
{“desc”:”inventory inside SDH”“timestamp”: …}
{“desc”:”Lamp”“timestamp”: …}
{“desc”:”Phone”“timestamp”: …}
{“desc”:”Outlet”“timestamp”: …}
{“desc”:”Acme”“timestamp”: …} r-node
s-node
/hvac
/CT /Chiller
/vent
/loadtree
/panel /xform
/outlet
/spaces
/floor3/floor4
/power/mote123
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Data collection and querying
/inventory/mote123
DB
PID1
PID1
PID2
PID2
PID3
PID3
PID4
PID4
Tim
e
GET?query=true&ts_timestamp=gt:now-100,ls=now
GET/SDH/spaces/*?query=true&props_metertype=powermeter
{“metertype”:”powermeter”,“desc”:”Electric power meter”,“timestamp”: 1290500046}
/power /temp/hum/par
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Data representation layer
• Narrow-waist for data representation– Simple Metering and Actuation Profile (sMAP)
sMAP
Electrical
Weather
GeographicalWater
EnvironmentalStructural Actuator
Occupancy
Phys
ical
In
form
ation
/ # list resource under URI root [GET] /data # list sense points under resource data [GET] / [sense_point] # select a sense points [GET] /meter # meters provide this service [GET] / [channel] # a particular channel [GET] /reading # meter reading [GET] /format # calibration and units [GET/POST] /parameter # sampling parameter [GET/POST] /profile # history of readings [GET] /report # create and query periodic reports [GET/POST]
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RESTful + JSONInterface
{
"operation":"create_publisher","resourceName”:”power"
}
PUT http://is4server.com/is4/devices/mote123/
{”pubid":"550e8400",
}
REPLY: 201 Created
PUT http://is4server.com/is4/devices/mote123/power
{"Reading": 120,
}
POST http://is4server.com/is4/devices/mote123/power?pubid=550e8400
{“desc”:”Temperature mote”,"Reading": 120,“timestamp”: 1290500046}
GET http://is4server.com/is4/devices/mote123/power
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Sharing real-time feeds
POST http://is4server.com/sub
{"streams":[550e8400],"url":"http://128.32.37.21:8011/
sub.php"}
{”subid":"41d4",
}
http://is4server.com/sub/41d4
REPLY: 201 Created
mote123/power
price
BMSZigbee
StreamFS
http://128.32.37.21:8011/sub.php
POST
550e8400
41d4
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Data cleaning and distillation staging
InterpolateF1(x)
ExtrapolateF2(x)
/distillers /inventory/mote123
/power /current/interp /filter
/inventory/mote123/power | /distillers/interp | /distillers/filter
| http://128.32.37.21:8011/sub.php
/procstage/983hfq
Java/Javascript/Python
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Putting it all together (1)
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/floor4
/room410
/therm/mote01
GET/…/floor4/room410/*/*?query=true&prop_type=temp
/temp /temp
InterpolateF1(x)
InterpolateF1(x)
/…/floor4/room410/room410/therm/temp
/…/floor4/room410/room410/mote01/temp
/…/floor4/room410/room410/mote01/temp?query=true&ts_timestamp=lte:t1,gte:t7
/…/floor4/room410/room410/therm/temp?query=true&ts_timestamp=lte:t1,lte:t7
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Putting it all together (2)
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/…/floor4/room410/*/*?query=true&type=device| /distillers/ts_getall?ts_timestamp=lte:t1,gte:t7
| /distiller/interp_all?attr=timestamp&unit=1
| /distiller/join?attr=timestamp
| http://viewer.com/viewer.phpt1
t2
t3
t5
t4
t6
t7
mot
e01/
tem
p
ther
m/t
emp In-time pipe-chainContinuous pipe-chain
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Current Status
• Public StreamFS implementation– http://is4server.com
• Sutardja Dai Hall BMS HVAC system– 400+ live streams
• Cory Hall Electrical data– 2500+ live streams
• Streaming data rate– ~600-700 Kbps
• Almost 300 GB stored
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Open questions andupcoming work
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Standard distillationelements
• Provide regression, interpolation, extrapolation functions over space and time values
• Provide join and filter functions
x
y
User
x
y
Consistent uniform view
Apply regression;Compute “temp” at grid points
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Streams, models, and resampling
• Real-time query optimization– Raw stream data– Post-processed distillation data– Model output data
• Physical, mathematical, probabilistic models
• Prior work– MauveDB, FunctionDB
• Focus on model expression and model-query optimization– TelegraphCQ, Eddies, Psoup
• Focus on raw streaming query optimization
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Message-passingScalability
Processing ElementProcessing
Element
Streams Subscribers
Processing ElementProcessing
Element
• Scaling with processing time (p), the number of streams (s), and the number of subscribers (t)
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Efficient graph and timeseries queries
Inter-relationship analysisThrough graph queries
Time-series correlationanalysis
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Architecture is an enabler
• Proactive detection of inefficient energy use• Enables exploration for energy-saving
opportunities by focusing on the data
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