All posts
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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
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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
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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
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How we created our own data science academy
If your goal is a more data driven organization, a group of six people in the Data Science department cannot do the task alone. In this post we describe how we developed a data science academy where a group of colleagues attended three months of training in data science. Read more
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How to recruit data scientists and build a data science department from scratch
Our strategy was to build on talent from the business and hire fast learners when we started building a data science department at Statnett two years ago. Now, we have operated for a year with great achievements as well as setbacks and learning points. Read about our story in this post. Read more
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Developing and deploying machine learning models for online load forecasts
Load forecasts are important input to operating the power system, especially as renewable energy sources become a bigger part of our power mix and new electrical interconnectors link the Norwegian power system to neighbouring systems. That is why we have been developing machine learning models to make predictions for power consumption. This post describes the… Read more