Zobrazeno 1 - 10
of 2 341
pro vyhledávání: '"Paál, Á."'
Autor:
Erdmann, Niklas, Bentsen, Lars Ø., Stenbro, Roy, Riise, Heine N., Warakagoda, Narada, Engelstad, Paal
Solar irradiance forecasts can be dynamic and unreliable due to changing weather conditions. Near the Arctic circle, this also translates into a distinct set of further challenges. This work is forecasting solar irradiance with Norwegian data using v
Externí odkaz:
http://arxiv.org/abs/2410.07806
Autor:
Lewen, Jan, Pargmann, Max, Cherti, Mehdi, Jitsev, Jenia, Pitz-Paal, Robert, Quinto, Daniel Maldonado
Concentrating Solar Power (CSP) plants play a crucial role in the global transition towards sustainable energy. A key factor in ensuring the safe and efficient operation of CSP plants is the distribution of concentrated flux density on the receiver.
Externí odkaz:
http://arxiv.org/abs/2408.10802
The expanding research on manifold-based self-supervised learning (SSL) builds on the manifold hypothesis, which suggests that the inherent complexity of high-dimensional data can be unraveled through lower-dimensional manifold embeddings. Capitalizi
Externí odkaz:
http://arxiv.org/abs/2405.13848
Vision Transformers implement multi-head self-attention via stacking multiple attention blocks. The query, key, and value are often intertwined and generated within those blocks via a single, shared linear transformation. This paper explores the conc
Externí odkaz:
http://arxiv.org/abs/2402.00534
Publikováno v:
ICLR 2024
The manifold hypothesis posits that high-dimensional data often lies on a lower-dimensional manifold and that utilizing this manifold as the target space yields more efficient representations. While numerous traditional manifold-based techniques exis
Externí odkaz:
http://arxiv.org/abs/2305.10267
With an ever-increasing number of sensors in modern society, spatio-temporal time series forecasting has become a de facto tool to make informed decisions about the future. Most spatio-temporal forecasting models typically comprise distinct component
Externí odkaz:
http://arxiv.org/abs/2303.13177
State representation learning aims to capture latent factors of an environment. Contrastive methods have performed better than generative models in previous state representation learning research. Although some researchers realize the connections bet
Externí odkaz:
http://arxiv.org/abs/2303.07437
Publikováno v:
Appied Energy 333 (2023) 120565
This study focuses on multi-step spatio-temporal wind speed forecasting for the Norwegian continental shelf. The study aims to leverage spatial dependencies through the relative physical location of different measurement stations to improve local win
Externí odkaz:
http://arxiv.org/abs/2208.13585
Transformers are neural network models that utilize multiple layers of self-attention heads and have exhibited enormous potential in natural language processing tasks. Meanwhile, there have been efforts to adapt transformers to visual tasks of machin
Externí odkaz:
http://arxiv.org/abs/2206.15269
Publikováno v:
IEEE Journal of Transactions on Games, 2022
Q-learning is one of the most well-known Reinforcement Learning algorithms. There have been tremendous efforts to develop this algorithm using neural networks. Bootstrapped Deep Q-Learning Network is amongst them. It utilizes multiple neural network
Externí odkaz:
http://arxiv.org/abs/2203.01004