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Software Defined Buildings Pretreat

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

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Software Defined Buildings Pretreat

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  1. Software Defined Buildings Pretreat David E. Culler Randy Katz Francesco Borrelli 1-11-2013 “A House Is a Machine for Living In.” - Le Corbusier

  2. 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

  3. UC Berkeley Project Team Collaborators Sponsors Friends Project Goals &Technology Transfer People Project Status Work in Progress Prototype Technology Early Access to Technology Promising Directions Reality Check Feedback

  4. Agenda 9:30 - 10:00 Coffee 10:00 - 10:30 Introductions/Project Overview: David Culler, Randy Katz, Francesco Borrelli 10:30 – 12:15 Platform BOSS: software architecture for security and reliability -- Stephen Dawson-Haggerty Building Application Stack - an initial runtime and API -- Andrew Krioukov Privacy and Security -- Prashanth Mohan Overview of MPC in Buildings -- Tony Kelman 12:15 – 1:15 Lunch 1:15 – 2:15 Modeling, Learning, and Control Berkeley Library for Optimization Modeling -- Sergey Vichik Physical and empirical modeling -- Jason Trager Anomaly detection and relationship inference -- Jorge Ortiz Localization -- Kaifei Chen 2:15 – 3:00 Infrastructure and Applications MPC Lab HVAC system (µBuilding) -- Yudong Ma Demand-controlled ventilation, and its grid potential -- Jay Taneja Building application targets -- Andrew Krioukov 3:00 – 3:30 Break/Demos 3:30 – 4:00 Feedback and Discussion 4:00 – 5:00 Beer/Wine Reception and Informal Discussion

  5. 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” SDB pretreat

  6. 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 SDB pretreat

  7. Elements of a Software Defined Building Energy Environment Outdoor Environment Personal Environment External Human-BuildingInterface Planning Visualization Control / Schedule Occupant Satisfaction Multi-Objective Model-Driven Control SoftZones Activity/Usage Streams Empirical Models Occupant Demand privacy-pres. query Building Integrated Operating System Physical Models Security, Fault, Anomaly Detect &Management Pervasive Sensing BMS Process Loads drvrs proxy sandbox Mapping Physical Info Bus Electrical BIM Transport Legacy Instrumentation & Control Interfaces HVAC Light LocalControllers Cyber Physical Building App SDB pretreat

  8. 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 each SDB pretreat

  9. 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 SDB pretreat

  10. 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 • … SDB pretreat

  11. 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 SDB pretreat

  12. 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

  13. HVAC app INFORMATION SOURCES BUILDING REPRESENTATION : gbXML + Representation of: Temporal Uncertainty (changes during building lifecycle) Data Ingestion Uncertainty (inomplete/incorrect information sources) Spatial uncertainty [e.g exact schedules of a building within a larger campus] Modeling software BUILDING APPS Adapter 1 CAD files Renderer Adapter 2 BIM Model Lighting App Adapter 3 Google Sketchup Model UNCERTAINTY REDUCER CONFLICT RESOLUTION Adapter 4 BMS web tier Version Control Type checker Adapter n Constraint Propagator BACNet points Manual Input

  14. Data Ingestion Example: ALC web portal adapter REPRESENTATION INPUT SOURCE : ALC BMS web page of building “DOE Annex” <AirloopsystemType="VariableAirVolume"> <AirLoopEquipmentequipmentType="VAVBox” id=“doe_vav_b-4-01”> <ShellGeometry> <ClosedShell> <PolyLoop> <CartesianPoint> <Coordinate> 13 </Coordinate> <Coordinate> 51 </Coordinate> <Coordinateflag="AddedByIngestor"> 10.0 </Coordinate> ……… </AirLoop> bacnet ID Ingestion Uncertainty of the z-coordinate ADAPTER

  15. HVAC app INFORMATION SOURCES BUILDING REPRESENTATION : gbXML + Representation of: Temporal Uncertainty (changes during building lifecycle) Data Ingestion Uncertainty (inomplete/incorrect information sources) Spatial uncertainty [e.g exact schedules of a building within a larger campus] Modeling software BUILDING APPS Adapter 1 CAD files Renderer Adapter 2 BIM Model Lighting App Adapter 3 Google Sketchup Model UNCERTAINTY REDUCER CONFLICT RESOLUTION Adapter 4 BMS web tier Version Control Type checker Adapter n Constraint Propagator BACNet points Manual Input

  16. Can we Make Buildings Greener? Humans Building Predictive Controller Environment Predictions on Building Dynamics, Weather, Occupancy, Comfort

  17. 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

  18. Basic Idea Avoid Region Avoid Region Avoid Region time control action human, environment constraints At step t decide on u(t) based on prediction on w(t),..., w(t+N), Y(t),…,Y(t+N) Two Combined Effects : Anticipation and Coordination

  19. Model Predictive Control (MPC) • 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

  20. Challenges • “System” Knowledge – Right Model Abstraction • Predictions are uncertain • Large-scale • Scalability • Limited computational resources • Certification www.mpc.berkeley.edu

  21. “Better” Strategy www.mpc.berkeley.edu

  22. Autonomous DrivingVolvo Experiments 2012

  23. 2012 IEEE Control System Technology Award

  24. Discussion SDB pretreat

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