Gregory Provan Cork Complex Systems Lab Computer Science Department, University College Cork, Cork,...
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Gregory Provan Cork Complex Systems Lab
Computer Science Department, University College Cork,Cork, Ireland.
Collaborators: M. Behrens, M. Boubekeur, A Mady, J. Ploennigs
Hierarchical Monitoring and Diagnostics for Sustainable-Energy
Building Applications
G. Provan, June 2010 Hierarchical Diagnostics and Control
Global Model for a Sustainable Building
Ensure consistent data exchange
Enable integration of all tasks
Enable future developments
New software/hardware capabilities
Objectives
ACCESS 24/7 Monitoring
FIRE
ENERGY HVAC
LIFTSSECURITY
LIGHTING
Integrated Control Environment
Middleware
Interfaces
G. Provan, June 2010 Hierarchical Diagnostics and Control
Contributions of ITOBO Project:
ITOBO—Information Technology for Optimized Building Operations
Total building solutionEnd-to-end integrated energy solution
Integrated modelling frameworkMiddleware framework
Significant University/Industry collaboration25% of funding comes from industry
Demonstration of solutions in industrial settingsLarge 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)
G. Provan, June 2010 Hierarchical Diagnostics and Control
ITOBO Implementations
Technologies developed
1. Systems integration and middleware
2. Wireless devices and networking
3. Data warehousing and analysis
4. Energy modelling
5. Preference and maintenance analysis
6. Advanced controls and diagnostics
Domains addressed
1. Lighting
2. HVAC
3. Overall Energy and User Comfort modeling
G. Provan, June 2010 Hierarchical Diagnostics and Control 5
Contributions: DiagnosticsNovel hierarchical diagnostics/control methodology
for sustainable-energy building applicationsDerive models from detailed Building Information Model
Advantages Cheaper method for initial and continuous commissioningContinuously update model parameters via building data-
warehouseUse pre-defined building component librariesBuilding modifications result in updates to embedded code
through re-compilation
G. Provan, June 2010 Hierarchical Diagnostics and Control 6
Overview
Motivation Building systems
Needs for integrated control and diagnostics
Overall methodologyGenerate diagnosis/control models from
centralized Building Information ModelModel-transformation
Lighting & HVAC System examplesMonitoring and parameter-estimationHigh-level fault isolation
Summary and conclusions
G. Provan, June 2010 Hierarchical Diagnostics and Control
As-Built vs. As-Designed Energy Performance
Source: Turner and Frankel, ENERGY PERFORMANCE OF LEED FOR NEW CONSTRUCTION BUILDINGS, 2008
G. Provan, June 2010 Hierarchical Diagnostics and Control
Smart Buildings RequireAdvanced
Monitoring/DiagnosticsCurrent advanced technologies are
highly failure-proneTechnology abandoned if non-functionalExample: Automated windows which are too noisy
Buildings never perform as well as intendedPoor commissioning, faults, poorly-adjusted
systems
Places greater need on good diagnostics and control reconfiguration
page 8
Example: Interaction of HVAC and
LightingLighting-Blinds
and HVAC are coupledClosing blinds decreased internal temperature (cools room)
Control system must integrate blinding and HVAC
page 9Barcelona Digital , May 2010
G. Provan, June 2010 Hierarchical Diagnostics and Control
Current BMS Alarms
Building Management Systems (BMS) employ rule-based diagnostics
Problem: “nuisance” alarmsThousands of alarms generated per dayAlarms are deleted/ignored
Diagnostics are viewed as a nuisanceFaults are corrected only when they cause significant problems
Alarms may indicate real problems and energy-inefficiencies
G. Provan, June 2010 Hierarchical Diagnostics and Control
Generalised View of Fault
Fault: “correctable” source of energy wasteExample: sub-optimal control settings
Unoccupied HVAC, lighting
Diagnosis Isolating root-cause faultsIdentifying sub-optimal controls
Integration of diagnosis and control reconfiguration
page 11
G. Provan, June 2010 Hierarchical Diagnostics and Control
Multiple Modelling Formalisms
Lighting/securityDiscrete and continuous signals
HVACContinuous signals
Rotating machinery: signal processing
G. Provan, June 2010 Hierarchical Diagnostics and Control
Hierarchical DiagnosticsLow-level: monitoring and anomaly
detectionIdentification of anomalous operationsMachinery faults: pumps, chillers, etc.Method: rule-based alarms
High-level: fault isolation1. 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
2. Identify components whose abnormal performance results in sub-optimal energy usage
G. Provan, June 2010 Hierarchical Diagnostics and Control
Parameter Drift and False Alarms
Current practice“Commission” embedded control/diagnostics at building launch
Problem: parameter drift and/or redeploymentBuilding parameters change over timeBuilding operation/configuration changes
Monitoring rules no longer apply
High false-alarm rate due to asynchrony between actual and assumed building parameters
G. Provan, June 2010 Hierarchical Diagnostics and Control
End-to-End Process
Scenario Specification (i.e. Use Case Diagrams )
High Level Multi-Modelling
Analysis and Optimisation
Requirement Specification
Integrated Simulation and
Validation
Code
Generation
Wireless/Wired Sensor and Actuator network
Physical Plant
Monitoring &Alarms
Diagnostics
Control &Reconfiguration
G. Provan, June 2010 Hierarchical Diagnostics and Control
Model Generation Process
16
BIM
ACCESSS
24/7 Monitoring
FIRE
ENERGY HVAC
LIFTSSECURITY
LIGHTING
DataWarehouse
Building Meta-Model
System-Level Diagnostics
Monitoring and Anomaly Detection
HIERARCHICAL DIAGNOSTICSSYSTEM
MODELTRANSFORMATION
Parameter Estimation
G. Provan, June 2010 Hierarchical Diagnostics and Control
Model Analysis Process
17
ACCESSS
24/7 Monitoring
FIRE
ENERGY HVAC
LIFTSSECURITY
LIGHTING
DataWarehouse
System-Level Diagnostics&
Control Reconfiguration
Monitoring and Anomaly Detection
HIERARCHICAL DIAGNOSTICSSYSTEM
Real-Time Monitoring
BIM
Continuous Parameter Estimation
G. Provan, June 2010 Hierarchical Diagnostics and Control
Methodology
Define detailed meta-modelUse pre-specified component library
Auto-generate all monitoring/ diagnosis/ control models from meta-modelUse model-transformationCan generate embeddable code for wireless
network retro-fit applications
Estimate model parameters using building dataSupport continuous commissioning through
continuous parameter updating
18
G. Provan, June 2010 Hierarchical Diagnostics and Control
Meta-Model Specification
System topologyHybrid-systems control specification
Dynamical plant modelContinuous and discrete control models
Diagnostics dataFailure modelsComponent failure rates
19
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P(Act=fail-off)=0.005P(Act=fail-on)=0.001
G. Provan, June 2010 Hierarchical Diagnostics and Control
Example: Automated Lighting System
page 20
LX
D4 DimmableLamp
D1 Illumination sensor
D2 Occupancy Sensor
D3 Controller
IFCLocation Data:• Location of
Devices in Rooms
LX
D1 Illumination Sensor
D3 Controller D4 Dimmable Lamp
D2 Occupancy Sensor
L
P
S
C
L Illumination ValueP Occupancy ValueC Plant Control ValueS Illumination Set Point
BAS DesignInteraction Data:• Interaction of Devices
G. Provan, June 2010 Hierarchical Diagnostics and Control
Light Control Loop
page 21
PresenceSensor
LuxSensor Light
ActuatorLampBulb
[(Z alarm)] [(Presence = f) (C=L*)] (L=L* ±3 )]
[(MA fail-low)] (C=L*) (SL= low)]
Continuous-valued alarm monitoring
Discrete-valued fault isolation
G. Provan, June 2010 Hierarchical Diagnostics and Control
Example: Simplified HVAC SystemChiller
Pump
Room
> *
{On,Off}
{Y,N}
/t = f-f
Focus on pump/actuator sub-system
G. Provan, June 2010 Hierarchical Diagnostics and Control
HVAC Monitoring Model
page 23
PresenceSensor
TemperatureSensor
ChillerActuator
[(Z alarm)] [(Presence = f) (C= *) ( =* ±3 )]
[(Z alarm)] [(Presence = t) (C= *) ( <* ±3 )]
[(Z alarm)] [(Presence = t) (C *) (t>t*)]
Continuous-valued alarm monitoring
G. Provan, June 2010 Hierarchical Diagnostics and Control
Conclusion
page 24
Model-based generation of diagnostics and control has many advantagesCheaper method for building commissioning
Enables continuous commissioningMaintains consistency between BMS and BIM given building modifications
Technical feasibilityMonitoring: computationally easy
Uses building schematics and machine learning for parameter estimation
Root-cause diagnostics/reconfiguration: hardComplex model-reduction necessary