Embed Size (px)
Citation preview
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
.
.
.
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
10 Managed by UT-Battelle for the U.S. Department of Energy
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?
12 Managed by UT-Battelle for the U.S. Department of Energy
Using metrics such as CVRMSE and MAPE
Below 0.5% error
ASHRAE requires only within 30%
13 Managed by UT-Battelle for the U.S. Department of Energy
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
