Julia for Energy

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Julia is the fastest, easiest and most secure language for energy. Julia is being used today by utility companies, energy traders, energy research laboratories and others.

Energy firms use Julia to run models up to 100x faster with 90% fewer lines of code

Who Is Using Julia for Energy?

ARPA-E (Advanced Research Projects Agency - Energy) is a project of the US Department of Energy using Julia for a number of projects including collaborations with Mitsubishi Electric Research Laboratories, Carnegie Mellon University, MIT, Los Alamos National Laboratory, Quantumscape and others.

Électricité de France’s Jérôme Collet uses Julia to forecast electricity demand.

Fugro Roames is a large utility using Julia machine learning tools to identify network failures and potential failures 100x faster.

AOT Energy is an energy trading firm using Julia for options pricing and market simulations in the energy sector.

PSR uses Julia for energy market simulation, analytics and planning.

LAMPS PUC-Rio uses Julia for applied energy optimization research.

Los Alamos National Laboratory uses Julia for critical infrastructure optimization.

Invenia uses Julia to optimize the North American electrical grid.

Tangent Works uses Julia for real-time energy forecasting.

Other Julia users in the energy industry include Chevron, ExxonMobil and all of the US Department of Energy National Laboratories: Ames, Argonne, Brookhaven, Fermi, Frederick, Idaho, Lawrence Berkeley, Lawrence Livermore, Los Alamos, National Energy Technology Laboratory, National Renewable Energy Laboratory, Oak Ridge, Pacific Northwest, Princeton Plasma Physics, Sandia, Savannah River, SLAC National Accelerator and Thomas Jefferson National Accelerator.

Interested in Using Large Datasets in JuliaHub?

Sign up for our webinar on December 1 to learn the awkwardness of handling and lengthy processing time of large datasets.

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6
REASONS
TO GET ON
BOARD

Optimization

Julia provides faster, more efficient optimization using JuMP and other optimization packages.

Speed

Julia is the fastest language for machine learning, artificial intelligence, risk analysis, Monte Carlo simulations and other energy uses.

Ease of Use

Julia is easy to learn and easy to code. Spend your time improving energy delivery and efficiency - not writing code.

Solve the Two Language Problem

Stop prototyping in one language and deployment in a second language. Julia delivers the speed of C with the ease of use of Python.

High Performance Computing

Leverage GPUs, TPUs, supercomputers and other advanced hardware easily and seamlessly with Julia.

Robust package Environment

There are more than 6,000 registered Julia packages, including the best optimization packages available in any language.

What our products can do for the energy sector

More efficient modeling and faster risk analysis

JuliaTeam allows you to manage private and public packages for your entire team

Run simulations faster and cheaper in the cloud with JuliaHub

Our Customer Journey

  • 01

    Contact us to learn more about how Julia can help your business

  • 02

    Identify specific projects to test Julia’s capabilities for your work

  • 03

    Conduct a test of Julia’s performance for your use case

  • 04

    Analyze results and determine where and how Julia can improve your KPIs

  • 05

    We work with you to develop a custom solution. Execute and deploy.

Energy Trading

AOT Energy uses Julia for options pricing, linear programming and market simulations

Case Study

Optimizing the Electrical Grid

Invenia Technical Computing is scaling up its energy intelligence system using Julia

Case Study

Protecting the Electrical Grid

Fugro Roames engineers use machine learning in Julia to identify network failures and potential failures 100x faster

Case Study

Meet JuliaHub

Watch our webinar “JuliaHub 101”

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LEARN MORE ABOUT PHARMA AND HOW IT IS BEING USED
Feel the Power - Julia for Energy
DR. MATT BAUMAN

WEBINAR

Learn how JuliaHub can help address your energy organization. Speak with one of our team members.

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