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Open source – What’s in it for utilities?

March 25, 2020 / Jill Feblowitz / Artificial Intelligence, Data and Analytics, Deep Learning, Machine learning, Open Source, Power Generation, Training, Transmission Grid

With an uptick in literacy in Python and R, utilities are hearing the term “open source” more often. But, open source has already become ubiquitous in many industries. The  2019 Open Source Security and Risk Analysis Report found that 100% of the software operating at in the energy and cleantech has open-source code. It stands to reason, then, that open-source is a hot topic. Specifically, when and how to leverage open source for utility-specific analytics and data science.

For more information, read More to Open Source than You Think, UAI,  August, 2019.

While “open-source” is being used to describe developer tools, data, models, standards, frameworks, and platforms, in this blog, we’ll look at open-source analytics most applicable to utilities. There are plenty of places where models built using open source tools can be found; utility analytics teams are taking advantage of these. There is a difference, though, between using non-utility specific open-source tools to build models for utility use and creating open-source utility-specific computational models.

For more information, read Community – A Major Caveat to Successful Open Source Model Adoption, UAI,  August, 2019


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