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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Feedback/Questions?

Jorge Ortiz <jortiz@cs.berkeley.edu>