Joshua New, Ph.D.
Oak Ridge National Laboratory newjr@ornl.gov
865-241-8783
Building Energy Modeling and Calculations
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• Describe reasons for and challenges involved with creation of an automated calibration methodology
• Explain how evolutionary computation works and how effectively it can create calibrated models
• Provide an overview of the EnergyPlus VRF Heat Pump Computer model
• Demonstrate the VRF computer model verification using manufacturer’s data
• Distinguish between five different existing methods for calculating distribution of absorbed direct and diffuse solar gains in perimeter building zones
• Understand the impact of solar energy distribution on heating and cooling loads as well as on free-floating room air temperatures for various climates and building envelope options
ASHRAE is a Registered Provider with The American Institute of Architects Continuing Education Systems. Credit earned on completion of this program will be reported to ASHRAE Records for AIA members. Certificates of Completion for non-AIA members are available on request.
This program is registered with the AIA/ASHRAE for continuing professional education. As such, it does not include content that may be deemed or construed to be an approval or endorsement by the AIA of any material of construction or any method or manner of handling, using, distributing, or dealing in any material or product. Questions related to specific materials, methods, and services will be addressed at the conclusion of this presentation.
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• Thanks go to:
– Aaron Garrett, Ph.D. – Jacksonville State University
– Theodore Chandler – Jacksonville State University
– Amir Roth – DOE Building Technologies Office
– Oak Ridge Leadership Computing Facility
– Remote Data Analysis and Visualization Center
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• What are two of ASHRAE’s primary sources for calibration, what is their purpose, and what performance metrics do they use?
• What does SAE mean and what is its strength as a performance metric?
• What is one of the acceleration methodologies used to speed up the calibration process and is it justifiable?
• How well does envelope-only automated calibration currently do compared to human calibration?
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• Context and calibration guidelines
• Evolutionary computation (EC) overview
• EC-based Autotune for building calibration
• Acceleration method
• Autotune calibration results
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Context and Calibration Guidelines
• Tool using BEM: retrofit optimization
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Context and Calibration Guidelines
• “All (building energy) models are wrong, but some are useful”
– 22%-97% different from utility data for 3,349 buildings
• More accurate models are more useful
– Error from inputs and algorithms for practical reasons
– Useful for cost-effective energy efficiency (EE) at speed and scale
• Calibration is required to be (legally) useful
– ASHRAE G14
(NMBE<5/10% and CV(RMSE)<15/30% monthly/hourly)
• Manual calibration is risk/cost-prohibitive
– Development costs 10-45% of federal ESPC projects <$1M
– 114 of 119 US buildings are residential, 9% of ESCO market
• Need robust and scalable automated calibration for market
– Adjusts parameters in a physically realistic manner
– Scales to any available data and model (audit)
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E+ Input
Model
Autotune
.
.
.
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EC Overview
• Evolutionary computation simulates natural selection
– Genetic algorithms
– Evolution strategies
– Genetic programs
– Particle swarm optimization
– Ant colony optimization
• EC approach to building calibration
– Individual – a building (list of input parameters)
– Fitness – error between simulation output and sensor data
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EC Autotune
What is an individual?
• Defined by 108 real-valued parameters
– Material
• Thickness
• Conductivity
• Density
• Specific Heat
• Thermal Absorptance
• Solar Absorptance
• Visible Absorptance
– WindowMaterial:SimpleGlazingSystem
• U-Factor
• Solar Heat
– ZoneInfiltration:FlowCoefficient
– Shadow Calculation Frequency
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EC Autotune
What is the fitness?
Individual
Actual Building Data
Error
Model
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EC Autotune
How do they evolve?
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EC Autotune
How are offspring produced?
Mom
Dad
Brother
Sister
Thickness
0.022
0.027
0.0229
0.0262
Conductivity
0.031
0.025
0.029
0.024
Density
29.2
34.3
34.13
26.72
• Average each component
• Add Gaussian noise
Specific Heat
1647.3
1402.5
1494.7
1502.9
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EC Autotune
• Population size 16
• Tournament selection (tournament size 4)
• Generational replacement with weak elitism (1 elite)
• Gaussian mutation (mutation rate 10% of variable range)
• Heuristic crossover
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Acceleration Method
• Pick 1024 sub-atomic particles from the universe
• EnergyPlus is slow
– Full-year schedule
– 2 minutes per simulation
• Use abbreviated 4-day schedule instead
– Jan 1, Apr 1, Aug 1, Nov 1
– 10 – 20 seconds per simulation
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Acceleration Method
• 4 independent random trials
• 1024 simulations per trial
• Samples taken from high to low error r = 0.94
Monthly Electrical Usage r = 0.96
Hourly Electrical Usage
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Acceleration Method
Individual
Actual Building Data
Error
Model
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Acceleration Method
Combining serially…
Evolve Evolve
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Acceleration Method
Combining in parallel…
Island
Hopping
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Autotune Calibration Results
25% reduction in error in 10 generations typical
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Autotune Calibration Results
What are you comparing to?
Model
V7-A2
28July2010
1800
1600
1400
1200
1000
800
600
400
200
0
Monthly SAE
1276.340
1623.364
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𝑆𝐴𝐸 = 𝑀 𝑖
− 𝐴 𝑖 𝑖=1
1 623,4
1 276,3
V7-A2 28July2010
Monthly SAE
9000
8000
7000
6000
5000
4000
3000
2000
1000
0
Hourly SAE (kWh)
6242.036
8113.685
8030
𝑀 𝑖
− 𝐴 𝑖 𝑖=1
8 113,7
6 242,0
V7-A2
Hourly SAE
28July2010
Hourly RMSE
1.20594
1.62455
RMSE =
8030 𝑖=1
𝑀 𝑖
− 𝐴
8030 𝑖
2
1,8
1,6
1,4
1,2
1,0
0,8
0,6
0,4
0,2
0,0
1,2
1,6
V7-A2 28July2010
Hourly RMSE
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Autotune Calibration Results
How well did Autotune do?
• Autotune 108 envelope parameters 60% toward best manual model
• Autotuned best model within $9.46/month (actual use $152/month)
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• ASHRAE. 2013. Evolutionary Tuning of Building Models to Monthly Electrical Consumption. ASHRAE
Transactions 119(1) (pending publication)
• 22 Autotune-related publications:
– 1 PhD dissertation, 9 accepted publications, 6 submitted publications, and 6 internal reports
– Download data, view tuning dashboards, etc.
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Joshua New newjr@ornl.gov
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