82 citations · 190 across the 15 of their papers we have counts for
26 papers
Probabilistic forecasts of wind power generation in regions with complex topography using deep learning methods: An Arctic case
Odin Foldvik Eikeland, Finn Dag Hovem, Tom Eirik Olsen +2
The energy market relies on forecasting capabilities of both demand and power generation that need to be kept in dynamic balance. Today, when it comes to renewable energy generatio…
Detecting and interpreting faults in vulnerable power grids with machine learning
Odin Foldvik Eikeland, Inga Setså Holmstrand, Sigurd Bakkejord +2
Unscheduled power disturbances cause severe consequences both for customers and grid operators. To defend against such events, it is necessary to identify the causes of interruptio…
Pyramidal Reservoir Graph Neural Network
Filippo Maria Bianchi, Claudio Gallicchio, Alessio Micheli
We propose a deep Graph Neural Network (GNN) model that alternates two types of layers. The first type is inspired by Reservoir Computing (RC) and generates new vertex features by…
Intervention fatigue is the primary cause of strong secondary waves in the COVID-19 pandemic
Kristoffer Rypdal, Filippo Maria Bianchi, Martin Rypdal
As of November 2020, the number of COVID-19 cases is increasing rapidly in many countries. In Europe, the virus spread slowed considerably in the late spring due to strict lockdown…
Large-scale detection and categorization of oil spills from SAR images with deep learning
Filippo Maria Bianchi, Martine M. Espeseth, Njål Borch
We propose a deep learning framework to detect and categorize oil spills in synthetic aperture radar (SAR) images at a large scale. By means of a carefully designed neural network…
Code-Aligned Autoencoders for Unsupervised Change Detection in Multimodal Remote Sensing Images
Luigi T. Luppino, Mads A. Hansen, Michael Kampffmeyer +4
Image translation with convolutional autoencoders has recently been used as an approach to multimodal change detection in bitemporal satellite images. A main challenge is the align…