1. 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. Project Goals & Technology Transfer 3 UC Berkeley Project Team Collaborators Sponsors Friends 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” 1/11/13 SDB pretreat 5

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 1/11/13 SDB pretreat 6

7. BMS Cyber PhysicalBuilding Light Transport Process Loads Occupant Demand Legacy Instrumentation & Control Interfaces Pervasive Sensing Activity/Usag e Streams Local Controllers Planning Visualization Occupant Satisfaction Multi-Objective Model-Driven Control Building Integrated Operating System HVAC Electrical Security, Fault, Anomaly Detect &Management Control / Schedule External Human-Building Interface BIM proxydrvrs Energy EnvironmentOutdoor Environment Personal Environment Mapping SoftZones Physical Info Bus privacy-pres. query Elements of a Software Defined Building 1/11/13 SDB pretreat 7 Physical Models Empirical Models App sandbox

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 1/11/13 SDB pretreat 8

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 1/11/13 SDB pretreat 9

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  … 1/11/13 SDB pretreat 10

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 1/11/13 SDB pretreat 11

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

14. Data Ingestion Example: ALC web portal adapter INPUT SOURCE : ALC BMS web page of building “DOE Annex” <AirLoopEquipment equipmentType="VAVBox” id=“doe_vav_b-4-01”> 13 51 10.0 ……… REPRESENTATION bacnet ID Ingestion Uncertainty of the z-coordinate ADAPTER

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

16. Can we Make Buildings Greener? Environment Humans Building Predictive Controller Predictive Controller 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 Two Combined Effects : Anticipation and Coordination At step t decide on u(t ) based on prediction on w(t),..., w(t+N ), Y (t),…, Y (t+N) human, environmentconstraints Avoid Region time control action

19. 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 Model Predictive Control (MPC)

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

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

22. Autonomous Driving Volvo Experiments 2012

23. 2012 IEEE Control System Technology Award

24. Discussion 1/11/13 SDB pretreat 24