software defined buildings pretreat
DESCRIPTION
Software Defined Buildings Pretreat. David E. Culler Randy Katz Francesco Borrelli 1-11-2013. “A House Is a Machine for Living In.” - Le Corbusier. Who Is Here - Introductions. UCB Software Defined Buildings Project UCB Center for the Built Environment - PowerPoint PPT PresentationTRANSCRIPT
Software Defined Buildings Pretreat
David E. CullerRandy KatzFrancesco Borrelli1-11-2013
“A House Is a Machine for Living In.” - Le Corbusier
Who Is Here - Introductions
UCB Software Defined Buildings Project UCB Center for the Built Environment UC California Institute for Energy and
Environment LBNL – EETD, Sustainability, … NREL Integral Group Intel, IBM, … Univ. of L’Aquila
Project Goals &Technology Transfer
3
UC Berkeley Project Team CollaboratorsSponsorsFriends
PeopleProject Status
Work in ProgressPrototype Technology
Early Access to TechnologyPromising Directions
Reality CheckFeedback
Agenda9:30 - 10:00 Coffee10:00 - 10:30 Introductions/Project Overview: David Culler, Randy Katz, Francesco Borrelli10:30 – 12:15 Platform
BOSS: software architecture for security and reliability -- Stephen Dawson-HaggertyBuilding Application Stack - an initial runtime and API -- Andrew KrioukovPrivacy and Security -- Prashanth MohanOverview of MPC in Buildings -- Tony Kelman
12:15 – 1:15 Lunch1:15 – 2:15 Modeling, Learning, and Control
Berkeley Library for Optimization Modeling -- Sergey VichikPhysical and empirical modeling -- Jason TragerAnomaly detection and relationship inference -- Jorge OrtizLocalization -- Kaifei Chen
2:15 – 3:00 Infrastructure and ApplicationsMPC Lab HVAC system (µBuilding) -- Yudong MaDemand-controlled ventilation, and its grid potential -- Jay TanejaBuilding application targets -- Andrew Krioukov
3:00 – 3:30 Break/Demos3:30 – 4:00 Feedback and Discussion4:00 – 5:00 Beer/Wine Reception and Informal Discussion
Buildings … Where we spend 90% of our lives Where we spend 70% of our electricity Where we spend 40% of our energy Where we spend 40% of our CO2
emissions Where we spend a lot of our $’s
And once they are built all we can do … is use their hard-wired capabilities, decorate, or “retrofit”
1/11/13SDB pretreat 5
How can we make them fundamentally more agile machines? Programmable Separation of the hardware capabilities
(primitives) From the universe of potential behaviors
(applications) Allow them to be tailored to our desires
To the full extent of the underlying capabilities
Become a good citizen of a broader ecosystem
1/11/13SDB pretreat 6
BMS
Cyber Physical BuildingLi
ght
Tran
spor
tProcess Loads
Occupant Demand
Legacy Instrumentation & Control Interfaces
Pervasive Sensing
Activity/Usage
Streams
Local Controllers
PlanningVisualization
Occupant Satisfaction
Multi-Objective Model-Driven
Control
Building IntegratedOperating System
HVA
C
Elec
tric
al
Secu
rity,
Fau
lt, A
nom
aly
Det
ect &
Man
agem
ent
Control / Schedule
External
Human-B
uilding
Interfac
eBIM proxydrvrs
Energy Environment Outdoor EnvironmentPersonal Environment
Mapping
SoftZones
Physical Info Bus
privacy-pres. query
Elements of a Software Defined Building
1/11/13SDB pretreat 7
Physical Models
Empirical Models
Appsandbox
Pieces that we know we can do Building Application Programming Interface
cf., Andrew’s BAS Building Operation System & Services
Physical services and distributed device drivers Middle services: mapping, transactions, RAS Application services: baselining, ensemble, … cf., Stephen’s BOSS
Innovate in Model-Driven Predictive Control With interesting objectives: supply-following, cf,., Tony’s MPC
Rich Human-Building Interaction Location, personal and ambient devices, gestures, … cf.,
Kaifei Introduce meaningful security
Although a broad open research agenda in each1/11/13
SDB pretreat 8
Wide Open Challenges Scale
~110 M buildings in US To make a difference everything has to be
automated after insertion of basic capability Heterogeneity
in Design, Implementation, Use, … Automated metadata acquisition & context Learning throughout lifecycle
Uncertainty In time, space, use, behavior, …
Empowerment and Balance Privacy, security, autonomy, control, opportunity
1/11/13SDB pretreat 9
Some Building Applications
Whole-Building Integrated Optimized Control Supply-following Utilization of Passives and Occupancy
In situ model building Prognostics, diagnostics, logistics
Personalized building interactions Cell phone as HBCI
Localization via WiFi, sensing, participation Free space gestures
Softzones, Microzones …
1/11/13SDB pretreat 10
Components of a BOSS Physical Information Bus Historian Model Building Apparatus Control Application Sandbox Transaction Monitor Mapper Privacy Preserving Query Processing Personalized and Physicalized Human-
Building Interface
1/11/13SDB pretreat 11
Automated metadata ingestion and representation for buildings (Arka)
The problem: Large manual effort needed to construct building metadata in
order to run applications. Lots of Different Disconnected metadata sources :
BIM model, BMS software, CAD drawings, BACNet discovery, etc. Imagery, Occupant Interactions, …
Solution : Design adapters for ingest from these diverse information
sources. Automate rudimentary building metadata database formation Refine over the lifecycle
Maintain a standard representation of a building against which applications can be written.
Have mechanisms for conflict resolution of ingested information Explicitly represent uncertainty in the building representation
CAD files
BIM Model
Google Sketchup Model
BMS web tier
Adapter 1
Adapter 2
Adapter 3
Adapter 4
Adapter nBACNet
points
HVAC appModeling
software
Version Control
Renderer
Type checkerConstra
int Propaga
torManual Input
Lighting App
INFORMATION SOURCES
BUILDING REPRESENTATION :
gbXML+
Representation of: (1) Temporal
Uncertainty (changes during building lifecycle)
(2) Data Ingestion Uncertainty (inomplete/incorrect information sources)
(3) Spatial uncertainty [e.g exact schedules of a building within a larger campus]
UNCERTAINTY REDUCER
BUILDING APPS
CON
FLIC
T
RESO
LUTI
ON
Data Ingestion Example: ALC web portal adapter
INPUT SOURCE : ALC BMS web page of
building “DOE Annex”
<Airloop systemType="VariableAirVolume"> <AirLoopEquipment equipmentType="VAVBox” id=“doe_vav_b-4-01”> <ShellGeometry> <ClosedShell> <PolyLoop> <CartesianPoint> <Coordinate> 13 </Coordinate> <Coordinate> 51 </Coordinate> <Coordinate flag="AddedByIngestor"> 10.0 </Coordinate> ………</AirLoop>
REPRESENTATION
bacnet ID
Ingestion Uncertaintyof the z-coordinateADAPTER
CAD files
BIM Model
Google Sketchup Model
BMS web tier
Adapter 1
Adapter 2
Adapter 3
Adapter 4
Adapter nBACNet
points
HVAC appModeling
software
Version Control
Renderer
Type checkerConstra
int Propaga
torManual Input
Lighting App
INFORMATION SOURCES
BUILDING REPRESENTATION :
gbXML+
Representation of: (1) Temporal
Uncertainty (changes during building lifecycle)
(2) Data Ingestion Uncertainty (inomplete/incorrect information sources)
(3) Spatial uncertainty [e.g exact schedules of a building within a larger campus]
UNCERTAINTY REDUCER
BUILDING APPS
CON
FLIC
T
RESO
LUTI
ON
Can we Make Buildings Greener?
Environment
Humans Building
PredictiveController
Predictions on Building Dynamics, Weather, Occupancy, Comfort
Model Predictive Control / Learning Average energy consumption reduction of 60-
85%over DDC mode levels.
Source: “Model Predictive Control for Mid-Size Commercial Building HVAC.” Experimental work done by Dr. Borrelli group in conjunction with UTC Research Center and UC Berkeley.Published February 2012. US Army Corp of Engineers, Champaign, IL
Basic Idea
Avoid Region
Two Combined Effects : Anticipation and Coordination
y(k + 1) = y(k) + bww(k) + buu(k); y(k) 2 Y(k)At step t decide on u(t) based on prediction on w(t),..., w(t+N), Y(t),…,Y(t+N)
human, environment constraints
Avoid Region
Avoid Region
y(k)
time
control action
Advantages:
• Predictive• Systematic: no if-then-else and extensive trial and error tuning• Multivariable, Model Based• Guarantees: Performance and Constraint satisfaction• Large success in the process industry• Flexible/ Easy to Integrate
P re d ic ted o u tp u ts
M a n ip u la te d ( t+ k )uIn p u ts
t t+ 1 t+m t+ p
fu tu rep a s t
t+ 1 t+ 2 t+ 1+m t+ 1+p
Model Predictive Control (MPC)
Challenges
www.mpc.berkeley.edu
• “System” Knowledge – Right Model Abstraction• Predictions are uncertain• Large-scale• Scalability• Limited computational resources• Certification
“Better” Strategy
www.mpc.berkeley.edu
Autonomous Driving Volvo Experiments 2012
2012 IEEE Control System Technology Award
Discussion
1/11/13SDB pretreat 24