Real-Time Building Energy Modeling
and Fault Detection and Diagnostics for
a DoD Building
Bing Dong1, Zheng O’Neill2
1
University of Texas, San Antonio, TX, USA
2 University of Alabama, AL, USA
The work was done at the United Technologies Research Center
ME 4343 HVAC Design
Introduction
• Motivation
Source: NBI report 2008 Energy Performance of LEED For New Construction Buildings
Introduction
• HVAC systems consume >20% more energy
than design intent
– Equipment performance degradation, and interact with other systems.
– Existing control and information systems do not make visible system
level energy consumption.
• Need for a scalable building energy
management system that includes whole
building energy diagnostics and visualization
– Better HVAC operational controls and energy diagnostics
– Raises the visibility of energy performance to help decision making
Building Facts
• Each 150K sf2 Barrack
– Compartments, classrooms and
cafeteria/galley
7114
7113
• Cooling
– Two absorption chiller: 450 ton
– Chilled water loop with fixed-speed
primary pump
• Heating
– Steam from the base wide central
heating plant
– steam to water heat exchanger
• 5 AHUs for each building
• More than 200 VAV boxes with
reheat coil
• A distributed Direct Digital Control
System (DDC)
4
Technology Approaches
• Overview of the Integrated Infrastructure
PC Running
EMS
PC Running Integrated infrastructure
Ethernet
BACnet
Gate Way
Energy
Visualization
BIM Based
Database
Core Layer:
BIM-based Database
BIM to BEM
Revit
Core
Real-time Data Acquisition
Application Layer:
Real-time energy simulation, visualization and diagnostics
1000
800
600
400
Simulated
Measured
200
0
00:00
BLDG7114 Water Side Load (kW)
HVAC Lighting Weather
BLDG7114 Water Side Load (kW)
BCVTB
06:00
12:00
18:00
00:00
Building Reference
Model
0.1
0
-0.1
03:00
06:00
09:00
12:00
15:00
18:00
21:00
Energy Diagnostics
Applications
5
Technology Approaches
• Integrated Energy Modeling Approach
Real-Time Data Acquisition
Rwin
Tamb Tosur
Tisur Tzone
1/hoA
1/hiA Qstructure
R
Qsurfi
Qsurfo
C
Architectural Model
Envelope Model
40000
Fan Speed
Measured Power
Poly. (Fan Speed)
Fan Power (w)
35000
30000
Calibration
Electric Energy Deviation
BIM
Database
y = 6.4017x2 - 331.03x + 3355.6
R² = 0.9715
25000
20000
15000
10000
5000
0%
-5%
-10%
-15%
Mechanical Model
10
20
30
40
50
60
Fan Speed (%)
70
80
90
HVAC Equipment
Models
Target ±10% at rated conditions
-20%
-25%
0
0
Model
Integration
and
Validation
C
100
Dev
Total
Pumps
-5%
-4%
Cooling
AHU
Tower Supply
Fans
Fans
-13%
-4%
AHU
Exhaust
Return
Fans
Fans
-3%
-12%
Calibration
Technology Approaches
 BIM to BEM automatic code generation
Traditional Approach
Building 7114 Architectural
Model
Building 7114 Mechanical
Model
Rwin
Tamb Tosur
1/hoA
Tisur Tzone
1/hiA Qstructure
R
Qsurfi
Qsurfo
C
C
BEM (Thermal Network Model)
One Week
7
Technology Approaches
 BIM to BEM automatic code generation
Traditional Approach
Building 7114 Architectural
Model
Our Approach
Building 7114 Architectural
gbXML
Model
IFC
Automatic
data extract
BIM
Database
Automatic
data extract
Building 7114 Mechanical
Model
1/hoA
Rwin
Tamb Tosur
Rwin
Tamb Tosur
Building 7114 Mechanical
Model
Tisur Tzone
1/hiA Qstructure
R
1/hoA
Tisur Tzone
1/hiA Qstructure
R
Qsurfi
Qsurfo
C
C
BEM
Input
files
Qsurfi
Qsurfo
C
C
BEM (Thermal Network Model)
One Week
BEM (Thermal Network Model)
< 5 minutes!!
8
Technology Approaches
 Real-time Data Acquisition
Outside view
cafeteria
sleeping area
classroom
Building Control Virtual Test Bed (BCVTB)
Extend BCVTB BACnet actors:
1) BACnet reader utility:
Automatically generate a.xml configuration file
and a .csv point description file based on the file
created by Simens EMS
Naval Station Great Lakes (Bldg 7114)
2)
Simens EMS
Our DAQ
3)
StoreBACnetDatatoBIMDatabase:
Based on the .csv file, automatically create SQL
statements based on the raw data received from
EMS
DatabaseManager
Establish the connection between BCVTB and BIMbased database
9
Results
 Real-time Energy Performance Visualization
Energy Statistics Pie Chart Interface
Building
Hierarchy
Interface
Time-Series Energy Flows Interface
10
Instant Error
BLDG7114 Water Side Load (kW)
Results
 Real-time Energy Simulation
1000
Simulated
Measured
500
0
07/06
07/07
07/08
07/09
07/10
07/11
0.1
0.05
Q fan
 a_r
m
 a _ ex
m
3
2
 a _ rm
m
0
-0.05
3
8
5
-0.1
QCC
QHC
07/07
07/08
07/09
07/10
07/11 T , m
a _ amb
Building 7114 Real-Time Simulation Results from
07/06/2011 to 07/11/2011.
4
3
Qz 2 , Ta _ r , 2
Q fan
 a _ mix
T,m
 w_ s
T, m
5
2
7
 w_ r
T, m
 a_s
T,m
Qz 3 , Ta _ r ,3
Qz , Ta _ r
6
Qz 4 , Ta _ r , 4
12
1
9
13
10
2
3
4
14
Qz1 , Ta _ r ,1
1
11
5
6
7
8
Zone mode
Supply/Return fan
Mixed air
Economizer
Heating coil
Cooling coil
DAT set-point
Zone reheat coil
9
10
11
12
13
14
15
Secondary loop pumps
Bypasss loop
Primary loop pumps
Hotwater loop pumps
Chiller S/R water temp
Condenser pumps
Cooling tower
15
Building 7114 AHU3 secondary and primary system diagram
11
Results
Building 7114 Energy Diagnostics: Economizer fault identified and corrected
OA Damper Position
Reference ROM
3
0.8
Q fan
 a_r
m
 a _ ex
m
8
5
3
 a _ mix
T,m
7
 w_ s
T, m
5
 w_ r
T, m
Qz1 , Ta _ r ,1
1
QCC
QHC
4
Actual
Expected OA Damper Position
AHU network
2
 a _ rm
m
3
 a _ amb
T,m
1
2
Qz 2 , Ta _ r , 2
Q fan
 a_s
T,m
Qz 3 , Ta _ r ,3 Qz , Ta _ r
1
OAT
Train
OAD
0.6
0.8
6
Qz 4 , Ta _ r , 4
1
2
3
4
5
6
7
8
Airflow
0.4
Zone mode
Supply/Return fan
Mixed air
Economizer
Heating coil
Cooling coil
Discharge air temp set-point
Zone reheat coil
07/17
0.6
07/24
Building Operation data
07/31
0.4
07/17
AHU
energy
07/24
Anomaly Score
Damper
1000
Inference
1000
500
Valve 500
0
0
07/31
Anomaly Score
07/17
07/24
07/17
07/24
07/31
07/31
Building 7114
Energy Impact
Operation data
MAT
OA Damper
DAT
DATS
OAT
95
90
85
OA damper 100%
80
75
70
65
60
55
07/21
07/26
Times
07/31
DAT setpoint
cannot be
maintained
Economizer faults:
Enthalpy calculation in control sequences is wrong
B7114 CHW Average BTU/hr
Temperature (F) / Damper Position (%)
100
3,000,000
2,500,000
2,000,000
1,500,000
With Faults
1,000,000
Faults Corrected
500,000
0
OAT BINS (F)
Faults was corrected on Aug 3rd , 2011.
Measured chilled water energy consumption shows 18%
savings were achieved
Conclusion
• This study has demonstrated an integrated infrastructure
which integrates design information, database and realtime data acquisition in a real building to support energy
modeling, visualization and FDD.
Observations and Lessons learned:
• Manually mapping BMS points of each HVAC component.
• The designed control logic in the HVAC control system is
usually different from what is actually implemented locally.
Communication with field people is necessary to get an
accurate baseline model.
13
Thank you!
• Acknowledgements:
– DoD ESTCP program manager: Dr. Jim Galvin
– UTRC: Dong Luo, Madhusudana, Shashanka ,Sunil Ahuja,
Trevor Bailey
– Naval Station Great Lakes
• Energy manager: Peter Behrens
• Mechanical Engineer: Kirk Brandys
• Facility team
• Questions?
14