Trending
All posts
-
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
-
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
-
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
-
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
-
How we validate input data using pydantic
We use the Python package pydantic for fast and easy validation of input data. Here’s how. Read more
-
Retrofitting the Transmission Grid with Low-cost Sensors
In Statnett, we collect large amounts of sensing data from our transmission grid. This includes both electric parameters such as power and current, and parameters more directly related to the individual components, such as temperatures, gas concentrations and so on. Nevertheless, the state and behaviour of many of our assets are to a large extent Read more