Hierarchical Monitoring and Diagnostics for Sustainable-Energy Building Applications

1. Hierarchical Monitoring and Diagnostics for Sustainable-Energy Building Applications Gregory Provan Cork Complex Systems LabComputer Science Department, University College Cork, Cork, Ireland. Collaborators: M. Behrens, M. Boubekeur, A Mady, J. Ploennigs

2. FIRE LIGHTING SECURITY LIFTS ACCESS 24/7 Monitoring ENERGY HVAC Middleware Interfaces Integrated Control Environment Objectives • Global Model for a Sustainable Building • Ensure consistent data exchange • Enable integration of all tasks • Enable future developments • New software/hardware capabilities

3. Contributions of ITOBO Project:ITOBO—Information Technology for Optimized Building Operations • Total building solution • End-to-end integrated energy solution • Integrated modelling framework • Middleware framework • Significant University/Industry collaboration • 25% of funding comes from industry • Demonstration of solutions in industrial settings • Large office complex: HSG-Zander headquarters (Frankfurt, Germany) • Manufacturing: Cylon Controls (Dublin, Ireland); INTEL (Dublin, Ireland) • Modern “Green” building: ERI (Cork, Ireland) • Refurbishment project: UCC campus building (Cork, Ireland)

4. ITOBO Implementations Technologies developed • Systems integration and middleware • Wireless devices and networking • Data warehousing and analysis • Energy modelling • Preference and maintenance analysis • Advanced controls and diagnostics Domains addressed • Lighting • HVAC • Overall Energy andUser Comfort modeling

5. Contributions: Diagnostics Novel hierarchical diagnostics/control methodology for sustainable-energy building applications Derive models from detailed Building Information Model Advantages Cheaper method for initial and continuous commissioning Continuously update model parameters via building data-warehouse Use pre-defined building component libraries Building modifications result in updates to embedded code through re-compilation

6. Overview • Motivation • Building systems • Needs for integrated control and diagnostics • Overall methodology • Generate diagnosis/control models from centralized Building Information Model • Model-transformation • Lighting & HVAC System examples • Monitoring and parameter-estimation • High-level fault isolation • Summary and conclusions

7. As-Built vs. As-Designed Energy Performance • Source: Turner and Frankel, Energy Performance of LEED for New Construction Buildings, 2008

8. Smart Buildings RequireAdvanced Monitoring/Diagnostics • Current advanced technologies are highly failure-prone • Technology abandoned if non-functional • Example: Automated windows which are too noisy • Buildings never perform as well as intended • Poor commissioning, faults, poorly-adjusted systems • Places greater need on good diagnostics and control reconfiguration

9. Example: Interaction of HVAC and Lighting • Lighting-Blinds and HVAC are coupled • Closing blinds decreased internal temperature (cools room) • Control system must integrate blinding and HVAC

10. Current BMS Alarms • Building Management Systems (BMS) employ rule-based diagnostics • Problem: “nuisance” alarms • Thousands of alarms generated per day • Alarms are deleted/ignored • Diagnostics are viewed as a nuisance • Faults are corrected only when they cause significant problems • Alarms may indicate real problems and energy-inefficiencies

11. Generalised View of Fault • Fault: “correctable” source of energy waste • Example: sub-optimal control settings • Unoccupied HVAC, lighting • Diagnosis • Isolating root-cause faults • Identifying sub-optimal controls • Integration of diagnosis and control reconfiguration

12. Multiple Modelling Formalisms • Lighting/security • Discrete and continuous signals • HVAC • Continuous signals Rotating machinery: signal processing

13. Hierarchical Diagnostics • Low-level: monitoring and anomaly detection • Identification of anomalous operations • Machinery faults: pumps, chillers, etc. • Method: rule-based alarms • High-level: fault isolation • Analysis of root-causes of complex, system-level anomalies • Example: room too cold due to window actuators not closing windows fully (vs. heater fault, etc.) • Method: MBD, FDI • Identify components whose abnormal performance results in sub-optimal energy usage

14. Parameter Drift and False Alarms • Current practice • “Commission” embedded control/diagnostics at building launch • Problem: parameter drift and/or redeployment • Building parameters change over time • Building operation/configuration changes • Monitoring rules no longer apply • High false-alarm rate due to asynchrony between actual and assumed building parameters

15. Scenario Specification (i.e. Use Case Diagrams ) End-to-End Process High Level Multi-Modelling Code Generation Requirement Specification Analysis and Optimisation Integrated Simulation and Validation Monitoring & Alarms Diagnostics Control & Reconfiguration Wireless/Wired Sensor and Actuator network Physical Plant

16. FIRE LIGHTING SECURITY LIFTS ACCESSS 24/7 Monitoring ENERGY HVAC Model Generation Process BIM Building Meta-Model MODEL TRANSFORMATION HIERARCHICAL DIAGNOSTICS SYSTEM System-Level Diagnostics Data Warehouse Monitoring and Anomaly Detection Parameter Estimation

17. FIRE LIGHTING SECURITY LIFTS ACCESSS 24/7 Monitoring ENERGY HVAC Model Analysis Process HIERARCHICAL DIAGNOSTICS SYSTEM BIM System-Level Diagnostics & Control Reconfiguration Data Warehouse Monitoring and Anomaly Detection Real-Time Monitoring Continuous Parameter Estimation

18. Methodology • Define detailed meta-model • Use pre-specified component library • Auto-generate all monitoring/ diagnosis/ control models from meta-model • Use model-transformation • Can generate embeddable code for wireless network retro-fit applications • Estimate model parameters using building data • Support continuous commissioning through continuous parameter updating

19. Meta-Model Specification • System topology • Hybrid-systems control specification • Dynamical plant model • Continuous and discrete control models • Diagnostics data • Failure models • Component failure rates u2 u1 u (Act=fail-off) [C=Cext] (Act=fail-on) [C=C*] P(Act=fail-off)=0.005 P(Act=fail-on)=0.001

20. Example: Automated Lighting System

21. Light Control Loop Lux Sensor Lamp Bulb Light Actuator Presence Sensor [(Z  alarm)]  [(Presence = f)  (C=L*)]  (L=L* ±3 )] Continuous-valued alarm monitoring [(MA  fail-low)]  (C=L*) (SL= low)] Discrete-valued fault isolation

22. Example: Simplified HVAC System Chiller Room Pump /t = f- f {On,Off} >* {Y,N}  Focus on pump/actuator sub-system

23. HVAC Monitoring Model Temperature Sensor Chiller Actuator Presence Sensor [(Z  alarm)]  [(Presence = f)  (C=*)  (=* ±3 )] [(Z  alarm)]  [(Presence = t)  (C=*)  (<* ±3 )] [(Z  alarm)]  [(Presence = t)  (C *)  (t>t*)] Continuous-valued alarm monitoring

24. Conclusion • Model-based generation of diagnostics and control has many advantages • Cheaper method for building commissioning • Enables continuous commissioning • Maintains consistency between BMS and BIM given building modifications • Technical feasibility • Monitoring: computationallyeasy • Uses building schematics and machine learning for parameter estimation • Root-cause diagnostics/reconfiguration: hard • Complex model-reduction necessary