Gregory Provan Cork Complex Systems Lab Computer Science Department, University College Cork, Cork,...

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


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)


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


As-Built vs. As-Designed Energy Performance

Source: Turner and Frankel, ENERGY PERFORMANCE OF LEED FOR NEW CONSTRUCTION BUILDINGS, 2008


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


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


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


Multiple Modelling Formalisms

Lighting/securityDiscrete and continuous signals

HVACContinuous signals

Rotating machinery: signal processing


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


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


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


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


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


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


Meta-Model Specification

System topologyHybrid-systems control specification

Dynamical plant modelContinuous and discrete control models

Diagnostics dataFailure modelsComponent failure rates

19

u

)(

)(

xFx

uIx

u

u1

0Xx

)(

)(

1

1

xFx

uIx

u

)(: 11 eGxe

),( 1 xeRx

),( 4 xeRx )(: 44 eGxe

)(: 22 eGxe ),( 2 xeRx

u2

)(

)(

2

2

xFx

uIx

u

)(: 33 eGxe

),( 3 xeRx

(Act=fail-off) [C=Cext](Act=fail-on) [C=C*]

P(Act=fail-off)=0.005P(Act=fail-on)=0.001


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


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


Example: Simplified HVAC SystemChiller

Pump

Room

> *

{On,Off}

{Y,N}

/t = f-f

Focus on pump/actuator sub-system


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


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

Gregory Provan Cork Complex Systems Lab Computer Science Department, University College Cork, Cork,...

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