Zobrazeno 1 - 10
of 24
pro vyhledávání: '"Frank Sehnke"'
Autor:
Sarah Barber, Unai Izagirre, Oscar Serradilla, Jon Olaizola, Ekhi Zugasti, Jose Ignacio Aizpurua, Ali Eftekhari Milani, Frank Sehnke, Yoshiaki Sakagami, Charles Henderson
Publikováno v:
Energies, Vol 16, Iss 8, p 3567 (2023)
In this paper, a set of best practice data sharing guidelines for wind turbine fault detection model evaluation is developed, which can help practitioners overcome the main challenges of digitalisation. Digitalisation is one of the key drivers for re
Externí odkaz:
https://doaj.org/article/d6d191a0e3674cac9897ab3b94aeb524
Autor:
Frank Sehnke, Anton Kaifel
Publikováno v:
BWK ENERGIE.. 74:30-32
Künstliche Intelligenz (KI) spielt bei der Energiewende eine bedeutende Rolle. Selbstlernende Verfahren helfen dabei, die Wind- und Solareinspeisung besser vorherzusagen oder Entwicklung und Produktion von Brennstoffzellen, Batterien und e-Fuels zu
Publikováno v:
SSRN Electronic Journal.
Autor:
Bartolomé Manobel, Frank Sehnke, Martin Felder, Sonia Montecinos, Juan A. Lazzús, Ignacio Salfate
Publikováno v:
Renewable Energy. 125:1015-1020
An accurate estimation of the wind turbine power curve is a key issue to the provision of the electricity that the wind farm will transfer to the grid and for a correct evaluation of the performance of each turbine. Artificial Neural Networks (ANN) h
Publikováno v:
Advances in Geosciences, Vol 45, Pp 13-17 (2018)
Usually, neural networks trained on historical feed-in time series of wind turbines deterministically predict power output over the next hours to days. Here, the training goal is to minimise a scalar cost function, often the root mean square error (R
Publikováno v:
Energy Procedia. 73:190-199
The storage of fluctuating energy production is a major challenge on the pathway to a fully renewable electricity supply. This paper investigates the role of Power-to-Gas (PtG) as a key storage technology in the fulfilment of the Energiewende. This s
Publikováno v:
Atmospheric Measurement Techniques, Vol 10, Pp 3547-3573 (2017)
Cirrus clouds play an important role in climate as they tend to warm the Earth–atmosphere system. Nevertheless their physical properties remain one of the largest sources of uncertainty in atmospheric research. To better understand the physical pro
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::1e99e666f9f4442fd6af9545523aeb06
http://elib.dlr.de/114488/
http://elib.dlr.de/114488/
Autor:
Vera Stoyanova, Gideon Dror, Xiaolin Xu, Wayne Burleson, Jürgen Schmidhuber, Ahmed Mahmoud, Ulrich Rührmair, Frank Sehnke, Srinivas Devadas, Jan Sölter
Publikováno v:
MIT web domain
We discuss numerical modeling attacks on several proposed strong physical unclonable functions (PUFs). Given a set of challenge-response pairs (CRPs) of a Strong PUF, the goal of our attacks is to construct a computer algorithm which behaves indistin
The storage of fluctuating energy production is a major challenge on the pathway to a fully renewable electricity supply. This paper investigates the impact of the storage technology Power-to-Gas (PtG) in the implementation of the Energiewende. A det
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ec21185e8ca141da75ac8397b5fef787
https://publica.fraunhofer.de/handle/publica/246862
https://publica.fraunhofer.de/handle/publica/246862
Publikováno v:
Paladyn: Journal of Behavioral Robotics, Vol 1, Iss 1, Pp 14-24 (2010)
This paper discusses parameter-based exploration methods for reinforcement learning. Parameter-based methods perturb parameters of a general function approximator directly, rather than adding noise to the resulting actions. Parameter-based exploratio