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Photo: JEAN SCHWEITZER

New associated project on offshore wind energy

Through the ongoing Akademia Avtale fund between Equinor and UiT. The goal is to develop new innovative approaches for combining image data and other sources of information in the context of wind energy.

New associated project on offshore wind energy

Through the ongoing Akademia Avtale fund between Equinor and UiT, a project is established to foster innovation and connections with VI partners, especially Equinor.

The goal is to develop new innovative approaches for combining image data and other sources of information in the context of anomaly detection for energy applications, including wind energy.

The challenge

In energy applications, the amount of data collected is exploding as well as its diversity. Numerical weather prediction models combine inputs from a well-established global network of meteorological measurement systems with a set of physical equations to describe the atmospheric conditions either globally, regionally or locally. With increased resolution, both in spatial dimension and time, these models can provide important information for resource mapping as well as forecasting of renewable energy. Extreme events, as e.g. polar lows and atmospheric icing, are difficult to predict, and represent a great risk in Arctic wind power operations.

There is an increasing need for computational tools able to combine data of different nature, be it images 2D or 3D, time-series from sensors, table of values or unstructured text or networks. In addition, the incorporation of expert knowledge expressed in the form of rules, constraints, physical models or invariant properties can greatly facilitate the extraction of information.

We are seeking to develop new innovative approaches for combining image data and other sources of information in the context of anomaly detection for energy applications, including wind energy applications. These anomalies or rare events are especially difficult to detect in a supervised learning framework due to their rarity.

Two postdoc positions will be funded, one in the machine learning group and one with the Arctic Center for Renewable Energy at UiT, more focused on weather data and models. Both researchers will collaborate to make new innovative solutions for monitoring energy plants and offshore windmills, based on anomaly detection and combining diverse data (images, sensors, weather data from measurements and models). Apply for the positions here.

For more info: Contact: Benjamin Ricaud benjamin.ricaud@uit.no

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