Supercomputer Assisted Generation of Machine Learning...

Supercomputer Assisted Generation of Machine Learning Agents for the Calibration of Building Energy Models

Jibonananda Sanyal Joshua New Richard Edwards

Oak Ridge National Laboratory

University of Tennessee, Knoxville

2 Managed by UT-Battelle for the U.S. Department of Energy

Sustainability is the Defining Challenge of Our Time

• Buildings in China

– 60% of urban building floor space in 2030 has yet to be built

• Buildings in India

– 67% of all building floor space in 2030 has yet to be built

• Buildings in the U.S. consume:

• 73% of all electricity

• 55% of all natural gas

Buildings, 41% Industry 31%

Transportation, 28%

U.S. Primary Energy Consumption in 2010

3 Managed by UT-Battelle for the U.S. Department of Energy

Building Energy Modeling

4 Managed by UT-Battelle for the U.S. Department of Energy

The biggest hurdle is the

cost-effective calibration of

Building Energy Models

5 Managed by UT-Battelle for the U.S. Department of Energy

EnergyPlus takes about 3 minutes for a simulation… already too long for industry

6 Managed by UT-Battelle for the U.S. Department of Energy

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.

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E+ Input

Model

Autotune

7 Managed by UT-Battelle for the U.S. Department of Energy

Each building is unique

• Buildings must conform to code

• DOE has standard reference buildings

– Representative of U.S. building stock

– Starting point of BEM experts

Surrogate

Modeling

EnergyPlus

Modeling

Time Accuracy

8 Managed by UT-Battelle for the U.S. Department of Energy

Types of buildings

• Residential

– 5 million simulations

• Medium Office

– 1 million

• Stand-alone retail

– 1 million

• Warehouse

– 1 million

• 8 million = 270+TBs of data

• 16 ASHRAE climate zones

• Vintage: Old, Recent, New construction

9 Managed by UT-Battelle for the U.S. Department of Energy

High Performance Computing Resources

One of the largest users of NICS Nautilus, over 300,000 SUs SDSC Gordon

NICS Kraken

ORNL Titan

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MLSuite: Machine Learning on

Supercomputers

• Support Vector Machines

• Genetic Algorithms

• FF and Recurrent Neural Networks

• (Non-)Linear Regression

• Self-Organizing Maps

• C/K-Means

• Ensemble Learning Shared memory Nautilus and

other distributed memory

supercomputers

11 Managed by UT-Battelle for the U.S. Department of Energy

So, how well can we calibrate?

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Using metrics such as CVRMSE and MAPE

Below 0.5% error

ASHRAE requires only within 30%

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Key technical merits

• EnergyPlus is desktop software

– Parametric simulations traditionally scale poorly

– Scalability on Leadership Class Infrastructure: up to 131,072 cores

• Surrogate modeling, Machine learning agents

– Runtime: from 3 minutes down to 3 seconds

• Big-data

– 45 TB in 68 minutes for ½ million E+ runs

– Total: 270+ TB raw

• Data analysis

– Move computation to data

– Parallel Big-Data R

14 Managed by UT-Battelle for the U.S. Department of Energy

Autotune is

simulation engine agnostic

Jibonananda Sanyal Joshua New

Richard Edwards

Oak Ridge National Laboratory

University of Tennessee, Knoxville

Supercomputer Assisted Generation of Machine Learning Agents for the Calibration of Building Energy Models