RISE DL Research Group

RISE DL Research Group

Foundational and applied machine learning for the Earth

Paper accepted at EUSIPCO 2024

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The paper From Weak to Strong Sound Event Labels using Adaptive Change-Point Detection and Active Learning by Martinsson et al. was accepted at EUSIPCO 2024.

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ML models in space

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ML models for cloud optical thickness estimation developed my members of the DL group (available here) have been running on-device in satellites orbiting in space. Read more about it here.

Funding of project on ML-based wind farm planning obtained from the Swedish Energy Agency

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We are happy to announce that we have received funding from the Swedish Energy Agency for the project AI-based Power Production Models for Increased Wind Farm Efficiency. Project summary: Wind energy is a promising source of power but is not easy to utilize effectively. Wind farms consist of many turbines that have complex interactions with each other and their surroundings. Factors such as terrain, wind trail effect (wake) between turbines, and ice accumulation on the blades influence the amount of power generated. Predicting the power output of wind farms typically relies on time-consuming simulations, but an emerging paradigm based on AI can drastically speed up prediction methods while maintaining their reliability. In this project we will develop new methods that use AI trained on real-world data to get accurate prediction of wind farm power output at a low computational cost. As the turbines and their relationships can be seen as a graph, we will use Graph Neural Networks (GNNs) to model them. Our method can have a big impact as the number of wind farms keeps growing, improving their efficiency and planning, and enabling more sustainable and affordable energy.