Data Acquisition Methods: Lessons Learned from Deployment of Cloud HVAC Analytics at UCSB

Transcription

1 Data Acquisition Methods: Lessons Learned from Deployment of Cloud HVAC Analytics at UCSB Tuesday, June 27 th 2017 Michael Georgescu (Ecorithm) Jordan Sager (UCSB) California Higher Education & Sustainability Conference (CHESC 2017)

2 Motivation & Project Background Today s talk: Geared to FM teams interested in or at early stages of FDD implementation This effort: Mid late 2016: UCSB FM deploys analytics software on two campus buildings as part of MBCx project Early present: project extends to two laboratory buildings with bandwidth for full campus rollout Important aspects of the project are: Integration approach & system architecture Scalability Persistence of savings vs. traditional retro-cx projects Cost-effectiveness & time-effectiveness 2

3 Building Analytics at UCSB Deployment of monitoring-based commissioning through advanced analytics of trended BMS data At the outset: BAS integration Analytics Reporting Moving forward: Mechanical drawings/schedules Spatial data Work order system interface Rethinking data acquisition 3

4 Education and Social Science Building ESSB buildings selected for initial deployment due to a history of temperature complaints 5,686 points trending on 5-minute intervals using native functionality in BMS and stored in cloud database Advanced analytics software interacting with database to monitor building plant, AHU (6), and terminal units (175) for MBCx project 4

5 Education and Social Science Building OCTOBER WEEK 3 WEEK 5 WEEK 7 WEEK 11 WEEK 11 JANUARY WEEK 15 FEBRUARY WEEK 19 MARCH WEEK 23 APRIL WEEK 27 WEEK 29 WEEK 31 WEEK Monthly TCI Nov Dec Jan Feb Mar Apr May 42.7% 64.6% 77.6% 83.9% 80.2% 88.2% 87.8% TCI: Avg. % of Spaces within comfort band (70F - 76F) 5

6 MBCx Results 1 st Year Outcomes: Frequency of new issues lowered by 50% Occupant comfort improved by 45% HVAC Maintenance requests lowered by 60% Approved incentive amount of $40,289 with energy savings of 137,368 kwh & 7,321 therms Data integration and consolidation facilitated easy information access Feedback to Ecorithm helped improve analytics, FDD definitions and workflow integration 6

7 Energy Efficient Operations National Renewable Energy Laboratory Innovation for Our Energy Future Lessons Learned from Case Studies of Six High-Performance Buildings P. Torcellini, S. Pless, M. Deru, B. Griffith, N. Long, and R. Judkoff A national laboratory of the U.S. Department of Energy Office of Energy Efficiency & Renewable Energy Technical Report NREL/TP June 2006 Properly applied off the shelf or state-of-theart technologies are available to achieve low-energy buildings. However, these strategies must be applied together NEED FOR INTEGRATION OF BEST-IN- CLASS COMPONENTS There was often a lack of software to allow the technologies to work well together NREL is operated by Midwest Research Institute! Battelle Contract No. DE-AC36-99-GO10337 NEED INTEGRATED CONTROL SOFTWARE AND UNCERTAINTY ANALYSIS 7

8 Information Architecture Central Data Store Native Name from BMS DeviceType DeviceName PointName City Center/TU_VAV/L12/_1211/DMPR Point1 COMD FPB FPB_L12_1211 DamperCommand Point2 City Center/HP/L15/_1501/DAT HP HP_L15_1501 DischargeAirTemp BMS Trends / Data Model Web Reporting Architectural Information Work Orders Fault Detection 8

9 Gathering BMS Trend Data VPN Firewall UCSB Network FM Network Central Data Store BMS Bacnet Traffic Local Volttron Host Data Push Original trending at ESSB using BMS functionality was cumbersome BMS data collection now performed with DOE s Volttron* software Compared to native BMS functionality, Volttron* allows trending of data with no measurable congestion on BMS network 10,000 data points trended on a 5-minute interval * 9

10 Data Onboarding: Contextualizing Building Data Data onboarding allows buildings with dissimilar systems to be uniformly represented within the analytics system Relationship Building Chiller_01.ChilledWater SupplyTemp AHU_01 FPB_L12_1211 FPB_L12_1212 HP_L15_1501 DamperCommand Occupied AirFlow Etc. Equipment and point hierarchies setup correlating systems/devices that are physically interconnected Device-relational mapping allows data to be converted into a logical representation enabling analysis, visualizations, and other complex behaviors to be designed 10

11 Floor Plan Generation Floorplans Equipment Schedules Riser Diagrams Space / Equipment Adjacency Information 11

12 Building Output (ex. Temperature) Advanced Analytics Automated Fault Detection and Diagnostics (AFDD) Learning algorithms used to identify and diagnose faults in buildings or in any dynamical system with the goal of bringing the system back to Intended Design 12

13 Web Reporting Tuesday, June 27, 2017 California Higher Education & Sustainability Conference (CHESC 2017) 13

14 Work Orders 14

15 Moving Forward As building managers and operators, we know a building s energy systems can become inefficient quickly! Lessons learned at UCSB in sustaining performance: Defining a plan for collecting and integrating BMS, energy, and other data sources enables analytics to be applied to everyday operations Involving campus stake holders (FM, IT, Occ.) ensured that all approvals are met cost effectively and without delays Making analytics mainstream: we hope to uncover additional opportunities and improve building performance as adoption grows 15

16 Thank You Jordan Sager UCSB Campus Energy Manager (805) Michael Georgescu, Ph.D. Ecorithm Director of Engineering & Research (818)