Technical Paper Session 3 - Evolutionary Tuning of

Building Models to Monthly Electrical Consumption

Building Energy Modeling and Calculations

Joshua New, Ph.D.Oak Ridge National Laboratory

newjr@ornl.gov865-241-8783

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Learning Objectives• 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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Acknowledgements

• 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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Objectives Q&A• 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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Outline/Agenda

• 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

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• Tool using BEM: retrofit optimization

• “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)

Context and Calibration Guidelines

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E+ InputModel

.

.

.

Autotune

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• 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

EC Overview

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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

EC Autotune

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Individual Model

Actual Building Data

ErrorFitness

EC Autotune

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What is the fitness?

Mom DadBrotherSister

EC AutotuneHow do they evolve?

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Thickness Conductivity Density Specific Heat

Mom 0.022 0.031 29.2 1647.3

Dad 0.027 0.025 34.3 1402.5

Brother 0.0229 0.029 34.13 1494.7

Sister 0.0262 0.024 26.72 1502.9

• Average each component

• Add Gaussian noise

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EC AutotuneHow are offspring produced?

• 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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EC Autotune

• 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

Monthly Electrical Usage

r = 0.94

Hourly Electrical Usage

r = 0.96

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Acceleration Method

Individual Model

Actual Building Data

ErrorFitness

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Acceleration Method

EvolveEvolve

Acceleration Method

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Combining serially…

Island Hopping

Combining in parallel…Acceleration Method

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Autotune Calibration Results25% reduction in error in 10 generations typical

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Autotune Calibration Results

Model Monthly SAE Hourly SAE (kWh) Hourly RMSE

V7-A2 1276.340 6242.036 1.20594

28July2010 1623.364 8113.685 1.62455

V7-A2 28July20100

200

400

600

800

1000

1200

1400

1600

1800

1,276.3

1,623.4

Monthly SAE

V7-A2 28July20100

1000

2000

3000

4000

5000

6000

7000

8000

9000

6,242.0

8,113.7

Hourly SAE

V7-A2 28July20100.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

1.8

1.2

1.6

Hourly RMSE

What are you comparing to?

𝑆𝐴𝐸=∑𝑖=1

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|𝑀𝑖− 𝐴𝑖| ∑𝑖=1

8030

|𝑀 𝑖−𝐴𝑖| RMSE  =√∑𝑖=18030

(𝑀𝑖−𝐴𝑖 )2

8030

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Autotune Calibration ResultsHow well did Autotune do?

• Autotune 108 envelope parameters 60% toward best manual model• Autotuned best model within $9.46/month (actual use $152/month)

Bibliography• 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. 23

Questions?

Joshua Newnewjr@ornl.gov

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