Invenia Technical Computing optimizes the electrical grid across North
America, using an Energy Intelligence System (EIS) that uses various
signals to directly improve the day ahead planning process. They employ
the latest research in machine learning, complex systems, risk analysis,
and energy systems.
Invenia’s current codebase is mostly written in MATLAB, Python and C.
But now Invenia is looking to scale up its operations, and their
language of choice for this experiment is Julia. This would allow them
to improve their systems to use much more data, optimize more
electricity grids, and run simulations in less time.
Additionally, Julia provides Invenia with some much needed versatility
in terms of programming style, parallelization and language
“We were constrained by MATLAB in terms of programming style
(vectorization was the only way), available libraries (no hash table,
for instance), cost (new release and toolboxes), parallelization (no way
to do generic parallel processing or threading), speed (no way to
hyper-optimize critical MATLAB code without dropping to C/C++/Fortran),
and compatibility (calling C/C++ is cumbersome and calling Python from
MATLAB caused a bunch of hassles.”
Invenia actively contributes to Base Julia, and writes a number of Julia
packages for its work, many of which are open source. Some examples are:
Invenia is using Julia to make electrical grids more efficient, reliable
our enterprise products
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Julia Computing's mission is to create and deliver products that make Julia easy to use, easy to deploy and easy to scale. We operate out of Boston, London and Bangalore, and we serve customers worldwide.
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