NI4AI is building bridges between different areas of expertise.
July 31, 2020
Veteran power systems engineers spend decades learning to excel in their jobs. This learning happens in school, professional training, and from real-world experience.
Becoming a skilled data scientist also takes practice. Data scientists often have advanced degrees in statistics or computer science. These degrees often involve a grueling course load and at least a few all-nighters. Given the overhead involved in becoming an in either domain - for most people it simply isn’t practical to become experts in both.
This blog post points you to some of our favorite resources explaining and motivating grid data analytics. We’ve curated two lists: one for data scientists and another for power systems engineers.
Have a favorite paper or textbook we missed? Send us an email: email@example.com.
If you know data but want to build intuition about what the data can tell us about the grid, this list is for you.
Read to start analyzing synchrophasor data? You can get experience working with synchrophasor data by analyzing the “Sunshine” dataset.
Utility analytics is a hot topic these days, and it can be hard to stay up to date. We follow the North American Synchrophasor Initiative (NASPI), a consortium of industry practitioners, vendors, and researchers geared at advancing the use of synchrophasors and other sensor technologies.
Here are a few resources by NASPI and others that we’ve found to be particularly helpful.
For more updates about new blog posts, datasets, tutorials, and events — we encourage you to follow us on LinkedIn.
October 25, 2019
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January 01, 2100
Laurel is a Civil Engineer specializing in data-driven risk assessment and decision analysis for power systems. She has engaged with utilities, regulators, and policymakers about issues like grid modernization and climate resilience. She hopes to help society realize the benefits of scientific advancements by being a facilitator for knowledge transfer among institutions and people.