A Robust Data Management System for Integrating Campus Sustainability Goals

1. A Robust Data Management System for Integrating Campus Sustainability Goals David Trombly, PhD

2. Key points No longer enough to run systems – need to provide good data Everyone has data problems How we are trying to overcome our data problems Examples of data being put to good use

3. Data More data utilized properly will help users and decision makers save energy and water

5. Commencements 6/12 3:13PM

6. How is your data?

7. How does SUSTAINABILITY apply to data management?

9. “Hub” for Smart City Data Substation Metering Building Utility Metering Heating & Cooling Plant Operations PI System Water & Wastewater Operations HVAC Operations & Smart Thermostats Exterior Lighting Controls Interior Lighting & Occupancy Building Level Campus Level

10. “Hub” for Smart City Data Substation Metering Building Utility Metering Heating & Cooling Plant Operations PI System HVAC Operations Water & Wastewater Operations Smart Thermostats Exterior Lighting Controls Interior Lighting & Occupancy Building Level Campus Level

11. Standard Operating Procedures Data management plan: Naming conventions Standard attributes of tags Standard data source setup System configuration Roles and responsibilities Clearly defined ownership Security Network setup Data resiliency procedures – buffering, backups, redundancy

12. Standard Operating Procedures Utilities dashboard PI SDK Electricity meter ION System PI System Energy dashboard PI WS ION OPC Server PI OPC Interface Steadfast.ucdavis.edu Billing system QA/QC Legend Windows users Utilities SCADA network Campus network TCP/IP, Port 5450 Modbus Data flow diagrams

13. Quality Assurance/Control • One system of record • Commissioning/calibration • Data accuracy • Equipment installation • Equipment maintenance • Internal audits • Data quality • Point configuration (poling frequency, data transformations)

14. Meta data – Context

15. Meta data – Process Flow Diagrams System Utilization

16. Management of change • Systems to document changes to: • Tags • Process flow • Data flow • Equipment inventory • Meta data • Data infrastructure • Notification/communication of changes

17. Automation Automatic transfer of meta data from system of record Automatic updates to SOPs, data flow diagrams, equipment inventory Notifications and alarms for data management system issues, data quality issues, and especially asset specific issues revealed by the metrics, analytics Automation allows data scientists to focus on the cool stuff

18. What you can do with a little good data

24. Key points No longer enough to run systems – need to provide good data Everyone has data problems How we are trying to overcome our data problems Examples of data being put to good use

25. David Trombly Joshua Morejohn jdmorejohn@ucdavis.edu Energy Manager Facilities Management, UC Davis dmtrombly@ucdavis.edu Associate Engineer Utilities, UC Davis

26. UC Davis (Main Campus) 34k students, 23k Faculty & Staff 1,000+ buildings, 180 over 10,000SF 11.3M SF total, 5,300 acres land Founded 1905, avg building age: 41 years old Steam production: 700M lbs/yr (NG boilers) Chilled water production: 30M ton-h/yr (elect chillers) Purchased utilities cost (elect & gas): $30M/year

27. Standard Operating Procedures Data management plan: Naming conventions Standard attributes of tags Standard data source setup System configuration Roles and responsibilities Clearly defined ownership Security Network setup

28. Meta data – Equipment Inventory

29. System Utilization

30. Management of change Data lifetime Communication/notification

35. Stakeholder buy-in Training Marketing data products Sharing data with students, faculty, staff, operators, senior management Developing user products, dashboards, tools Developing metrics, analytics

36. Tools

37. Tools

38. Standard Operating Procedures Data resiliency Collaboration with IT Backups Redundancy Who to contact during problems Buffering What to check after system is restored