All posts
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Is bid filtering effective against network congestion?
Earlier this year, I wrote an introduction to the bid filtering problem, and explained how my team at Statnett are trying to solve it. The system we’ve built at Statnett combines data from various sources in its attempt to make the right call. But how well is it doing its job? Or, more precisely, what Read more
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Smarter Transmission Grid Capacities with Weather Data
We are looking into sensor and weather based approaches to operating the grid more efficiently using dynamic line rating Read more
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Being a trainee on the forecasting team, including some secret tips
I’ve been a trainee at Statnett’s Data Science unit over the past months and learned a lot. In this post I will give you a quick look at what I have been working on, and some helpful advice for how to survive and thrive as a data science trainee. I was put on a team Read more
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Automatic data quality validations with Great Expectations: An Introduction to DQVT
Monitor your data assets History has showed us that cascading blackouts of the power grid can result from a single failure, often caused by extreme weather conditions or a defective component. Statnett and other transmission system operators (TSOs) learn continuously from these failures, adapt to them and prepare against them in case these physical assets Read more
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Using data to handle intra-zonal constraints in the upcoming balancing market
Together with almost half of the Data science group at Statnett, I spend my time building automatic systems for congestion management. This job is fascinating and challenging at the same time, and I would love to share some of what our cross-functional team has done so far. But before diving in, let me first provide Read more
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How we share data requirements between ML applications
We use pydantic models to share data requirements and metadata between ML applications. Here’s how. Read more