Accelerating Coupled HVAC/Building Simulation with a Neural Component Architecture

Default ARPA-E Project Image

Program:
DIFFERENTIATE
Award:
$2,697,922
Location:
Newton, Massachusetts
Status:
ALUMNI
Project Term:
05/12/2020 - 07/31/2022
Website:

Critical Need:

The DIFFERENTIATE program seeks to leverage the emerging artificial intelligence (AI) revolution to help resolve the energy and environmental challenges of our time. The program aims to speed energy innovation by incorporating machine learning (ML) into the energy technology development process. A core part of AI, ML is the study of computer algorithms that improve automatically through experience. This approach is expected to facilitate a rapid transition to lower-carbon-footprint energy sources and systems. To organize the proposed efforts, the program uses a simplified engineering design process framework to conceptualize several ML tools that could help engineers execute and solve these problems in a manner that dramatically accelerates the pace of energy innovation.

Project Innovation + Advantages:

Julia Computing, Inc. will develop a neural component machine learning tool to reduce the total energy consumption of heating, ventilation, and air conditioning (HVAC) systems in buildings. As of 2012, buildings consume 40 percent of the nation’s primary energy, with HVAC systems comprising a significant portion of this consumption. It has been demonstrated that the use of modeling and simulation tools in the design of a building can yield significant energy savings—up to 27 percent of total energy consumption. However, these simulation tools are still too slow to be practically useful. Julia Computing seeks to improve upon these tools using the latest computing and mathematical technologies in differentiable programming and composable software to enhance the ability of engineers to design more energy efficient buildings.

Potential Impact:

DIFFERENTIATE aims to enhance the productivity of energy engineers in helping them to develop next-generation energy technologies. If successful, DIFFERENTIATE will yield the following benefits in ARPA-E mission areas:

Security:

Seek U.S. technological competitive advantage by leading the development of machine-learning enhanced engineering design tools.

Environment:

Use these tools to solve our most challenging energy and environmental problems by facilitating an economically-attractive transition to lower carbon-footprint energy sources and systems.

Economy:

Reap the economic productivity benefits associated with the commercial adoption of the resulting higher-value energy technologies and associated products.

Contact

ARPA-E Program Director:
Dr. Rakesh Radhakrishnan
Project Contact:
Dr. Viral Shah
Press and General Inquiries Email:
ARPA-E-Comms@hq.doe.gov
Project Contact Email:
viral@juliacomputing.com

Related Projects

• Carnegie Mellon University (CMU) - Predicting Catalyst Surface Stability Under Reaction Conditions Using Deep Reinforcement Learning and Machine Learning Potentials
• Carnegie Mellon University (CMU) - High-fidelity Accelerated Design of High-performance Electrochemical Systems
• General Electric (GE) Global Research - Probabalistic Machine Learning for Inverse Design of Aerodynamic Systems (Pro-ML IDeAS)
• General Electric (GE) Global Research - IMPACT: Design of Integrated Multi-physics, Producible Additive Components for Turbomachinery
• IBM T.J. Watson Research Center - Model-based Reinforcement Learning with Active Learning for Efficient Electrical Power Converter Design
• Iowa State University (ISU) - Context-Aware Learning for Inverse Design in Photovoltaics
• Julia Computing - Accelerating Coupled HVAC/Building Simulation with a Neural Component Architecture
• Lawrence Berkeley National Laboratory (LBNL) - Deep Learning and Natural Language Processing for Accelerated Inverse Design of Optical Metamaterials
• Los Alamos National Laboratory (LANL) - Machine Learning-Based Well Design to Enhance Unconventional Energy Production
• Massachusetts Institute of Technology (MIT) - Machine Learning Assisted Models for Understanding and Optimizing Boiling Heat Transfer on Scalable Random Surfaces
• Massachusetts Institute of Technology (MIT) - Global Optimization of Multicomponent Oxide Catalysts for OER/ORR
• National Renewable Energy Laboratory (NREL) - End-to-End Optimization for Battery Materials and Molecules by Combining Graph Neural Networks and Reinforcement Learning
• National Renewable Energy Laboratory (NREL) - INTEGRATE - Inverse Network Transformations for Efficient Generation of Robust Airfoil and Turbine Enhancements
• Northwestern University - Adaptive Discovery and Mixed-Variable Optimization of Next Generation Synthesizable Microelectronic Materials
• Pacific Northwest National Laboratory (PNNL) - Machine Learning for Natural Gas to Electric Power System Design
• Princeton University - MLSPICE: Machine Learning based SPICE Modeling Platform for Power Magnetics
• Stanford University - Energy Efficient Integrated Photonic Systems based on Inverse Design
• United Technologies Research Center (UTRC) - Learning Enabled Network Synthesis (LENS)
• United Technologies Research Center (UTRC) - MULTI-source Learning-Accelerated Design of high-Efficiency multi-stage compRessor (MULTI-LEADER)
• University of Maryland (UMD) - Invertible Design Manifolds for Heat Transfer Surfaces (INVERT)
• University of Michigan, Dearborn - ML-ACCEPT: Machine-Learning-enhanced Automated Circuit Configuration and Evaluation of Power Converters
• University of Missouri - Deep Learning Prediction of Protein Complex Structures
• University of Texas at Austin (UT Austin) - Learning Optimal Aerodynamic Designs

Release Date:
04/05/2019