View
2
Download
0
Category
Preview:
Citation preview
Panel Discussion
Zheng O’Neill PhD,
PE
The University of Alabama
Yeonsook Heo PhD University of Cambridge
Mark Adams Oak Ridge National Lab
Kaiyu Sun Lawrence Berkeley National
Lab
What’s New in Building
Energy Model Calibration?
Contribution
Number
123456
Presentation Date 2017-08-09
Presentation Time 15:30
Covered topics
• Calibrate building model with sensor data (Zheng O’Neill)
• Bayesian calibration (Yeonsook Heo)
• Automated calibration • Optimization-based (Mark Adams)
• Pattern-based (Kaiyu Sun)
Calibration with sensor data
Integrated Model-based Information System
2
Actual Building
Reference Model Predictions
• Energy Consumption
• CO2 Production
• Water Consumption
• Zonal Comfort
Compare
andVisualize
Integrated
Software Environment
Building Energy
Performance Monitoring
Real-Time
Weather
Measurements
• Power Consumption
• Equipment Status
• Water Consumption
• Zonal T, RH
BCVTB
BCVTB: Building Control Virtual Test Bed
Integrate monitoring and simulation models of
building energy use to identify sources of waste and savings potential
Building Model Calibration
Identify uncertain
parameters, perform sampling
Perform numerous
simulations, pre-process output
Calculate full order
meta-model
Create Energy
Model
Calculate reduced-
order meta-model
Perform UA/SA
Perform
Calibration/Optimization
Verify Calibration
0
2000
4000
6000
8000
10000
12000
14000
16000
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
kWh
Plug Electricity
2010 meter data E+ after calibration E+ before calibration
0
5000
10000
15000
20000
25000
30000
35000
40000
1 2 3 4 5 6 7 8 9 10 11 12
kWh
Total Electricity
2010 meter data E+ after calibration E+ before calibration
Error Plug
Electricity
Total
Electricity
MBE 0.02% -2.31%
CV(RMSE) 0.47% 2.80%
• 2063 parameters were varied ± 25% of nominal value • Varied concurrently using a quasi-random sampling
approach • 6500 model realizations • Derivative based sensitivities
• Two outputs (measured data): monthly electricity and plug electricity
• Perform optimization on
2data) - model(
Building Model Calibration Issue • Measured data is significantly far from the baseline model such that changing the
parameters by +/- 25% does not move the output into the range of the measured data
• To alleviate this concern, optimization was performed with constraints on the output variables. Optimal parameters were defined such that the output did not leave an ellipse (in black color) that encompasses the data
1.6 1.7 1.8 1.9 2 2.1 2.2
x 104
0
0.02
0.04
0.06
0.08
0.1
0.12
Total Elec in kWh for January 2010
Pro
babi
lity
Sampled Data
Measured Data
Nominal Model
1.1 1.2 1.3 1.4 1.5 1.6
x 104
1.6
1.7
1.8
1.9
2
2.1
2.2
2.3
2.4x 10
4 March 2010
Plug Electricity [kWh]
Tota
l E
lectr
icity [
kW
h]
Sampled Output
Sensor Data
Baseline Model
Calibrated Meta
Calibrated E+
The meta-model and actual E+ simulation with optimal values has some difference, the meta-model is least accurate at its edges
Bayesian Calibration
New Calibration Methodology
Bayesian Calibration (Kennedy and O’Hagan, 2001)
Bayesian approach updates prior beliefs on true values of uncertain parameters θ in
a computer model given observations
Prior Density Functions for uncertain parameters
Bayesian Calibration
Building Energy Model
Monitored Data (utility bills)
Posterior Distributions
θ1
θn
Bayes’ theorem:
Kennedy and O’Hagan:
Prior Data Likelihood
Parameter Uncertainty
Model Inadequacy
Observation Errors
What is the Value of Bayesian Calibration?
Standard Calibration vs Bayesian Calibration (BC)
Intercept c Indoor Temperature
Infiltration Rate Discharge Coefficient
-3.8 -3.6 -3.4 -3.2 -3 -2.8 -2.6 -2.4 0
0.4 0.5 0.6 0.7 0.8 0.9 1 1.1 1.2 1.3
20 20.5 21 21.5 22 22.5 23 23.5
0.6 0.62 0.64 0.66 0.68 0.7 0.72 0.74 0.76
Bayesian process can enhance the reliability of the baseline model
How Does Bayesian Calibration Perform Under High
Uncertainty?
Uncalibrated Model
Calibrated Model
Level 3 0.29 0.04
Level 2 0.32 0.12
Level 1 0.38 0.13
CVRMSE values of model predictions
Is It Necessary to Calibrate All Uncertain Parameters?
Both cases calibrate the top four uncertain parameters:
o Case B: the remaining uncertain parameters are set at base values (mode of uncertainty distribution)
o Case I: the remaining uncertain parameters are fixed at true values (95-quantile of uncertainty distribution)
Optimization-based Automated Calibration
Case Study #1
• Model manually “de-tuned”
• 24 faults
• 63 input changes, +/- 40%
• Goal: Can Autotune recover data it did not use for calibration?
Case Study #1 Results
• Recovery 63 inputs (with +/- 40% ranges)
• 32 of 63 (50%) within 10% of their original value
• 22 (35%) between 10% and 30%
• 9 (14%) above 30%
• Variables important for energy were better
• COP of Chiller:Electric:EIR was off by 0.27%
Case Study #2
• Typical calibration workflow
• Residential Home
• 166 variable changes (35 types), +/- 30%
• Goal: Can Autotune do as well as a human expert calibrator?
Case Study #2 Results
For more information
http://bit.ly/autotune_science
http://bit.ly/autotune_code
Chaudhary, Gaurav, New, Joshua R., Sanyal, Jibonananda, Im, Piljae, O'Neill, Zheng, and Garg, Vishal (2016). "Evaluation of 'Autotune' Calibration Against Manual Calibration of Building Energy Models." In Journal of Applied Energy, volume 182, pp. 115-134, August 2016. [PDF]
Pattern-based Automated Calibration
Pattern-based automated approach • Basic idea => Tune the model according to the patterns of the measured and
simulated data, using experience in modeling and engineering
1 2 3 4 5 6 7 8 9 10 11 12
Months
0
2
4
6
8
10
12
14
Natu
ral G
as U
se (
MW
h)
Simulated Result
Natural Gas Bill
(b)Universal Bias
1 2 3 4 5 6 7 8 9 10 11 12
Months
0
2
4
6
8
10
12
14
Ele
ctr
icity U
se (
MW
h)
Simulated Result
Electricity Bill
(a)Universal Bias
Simulated resultsconsistently lower than electricity use.
Simulated resultsconsistently higher than NG use.
Universal Bias
Seasonal Bias
20
Category Parameters to tune
Internal loads
Occupant density
Lighting power density
Electric equipment power density
Outdoor air flow rate
Infiltration rate
HVAC system
Cooling equipment efficiency
Heating equipment efficiency
Fan efficiency
Cooling set point (schedule)
Heating set point (schedule)
Economizer status
Construction Window U-value
Window SHGC
Schedules
HVAC operation schedule
Lighting schedule
Electric equipment schedule
Case Study
• Single-story office building
• 10,000 ft2
• San Francisco
• Built in 1977
21
22
Tuning Calibration actions Before
tuning
After
tuning
Baseline prior to calibration
Step 1 Decrease lighting power density (W/m2) 21.4 14.9
Step 2 Increase occupant density (person/m2) 0.11 0.14
Step 3 Increase average outdoor air flow per
person (m3/s·person) 0.007 0.008
Step 4 Increase cooling COP 3.1 3.7
…
NMBE NMBE CVRMSE CVRMSE
Electricity Gas Electricity Gas
(%) (%) (%) (%)
… 20.8 -41.2 23.4 50.7
… 6.8 -32.7 10.6 38.9
… 7.6 -9.9 11.3 13.8
… 7.1 -0.2 10.9 14.1
… 4.7 -0.2 8.5 14.1
Baseline
Step1
Step2
Step4
Step3
Electricity Natural Gas
Open Discussion
Questions for panel discussion
• Key impact factors on the calibration accuracy?
• Updates on district/community-level calibration? What are the keys?
• Application of different calibration methods on large-scale data set?
• The application of widely-used sensors (e.g. temperature) data to model calibration?
• Matching monthly or hourly utility bills to Guideline 14 is great, but how do we know the calibrated model matches the real building?
• Is ASHRAE Guideline 14 good enough?
• What metrics define the “best” calibration methodology?
Questions and Discussion
Zheng O’Neill ZONeill@eng.ua.edu
Yeonsook Heo yh305@cam.ac.uk
Mark Adams adamsmb@ornl.gov
Kaiyu Sun ksun@lbl.gov
Recommended