Leif Erik Andersson
Norwegian University of Science and Technology
19 Papers
13 Citations
Leif Erik Andersson is an academic researcher from Norwegian University of Science and Technology. The author has contributed to research in topics: Iceberg & Computer science. The author has an hindex of 5, co-authored 18 publications.
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Papers
Hydrogen and the decarbonization of the energy system in europe in 2050: A detailed model-based analysis
Gondia Sokhna Seck,Emmanuel Hache,J. Sabathier,Fernanda Alves de Freitas Guedes,Gunhild Allard Reigstad,Julian Straus,Ove Wolfgang,Jabir Ali Ouassou,Magnus Askeland,Ida Hjorth,Hans Ivar Skjelbred,Leif Erik Andersson,Sébastien Douguet,Manuel Villavicencio,Johannes Trüby,Johannes Brauer,C. Cabot +16 more
TL;DR: In this paper , the potential of low-carbon and renewable hydrogen in decarbonizing the European energy system; specifically, reducing emissions by 55% in 2030 compared to 1990, and targeting net-zero emissions by 2050.
Wind farm control - Part I: A review on control system concepts and structures
TL;DR: A comprehensive review of the intense research conducted in this area over the last 10 years is presented and contributes to the existing reviews on the area by providing an elegant classification between model testing and control approaches.
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On Kalman filtering with linear state equality constraints
TL;DR: An alternative derivation of the optimal constrained Kalman filter for time variant systems is proposed and this results in an oblique state projection that gives the smallest error covariance.
28
Real-time optimization of wind farms using modifier adaptation and machine learning
Leif Erik Andersson,Lars Imsland +1 more
- 13 Jul 2020
TL;DR: Gaussian process (GP) regression is considered as a probabilistic, non-parametric modelling technique well known in the machine learning community and it is shown that the approach is able to correct the model and converges to the plant optimal point.
An estimation-forecast set-up for iceberg drift prediction
TL;DR: In this article, a new estimation-forecast scheme is proposed that improves iceberg drift forecasts by using past iceberg drift information to reduce uncertainties, which is guided by simple criteria that are introduced and explained.
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