Zobrazeno 1 - 10
of 1 657
pro vyhledávání: '"P. Bolland"'
The Unit Commitment (UC) problem is a key optimization task in power systems to forecast the generation schedules of power units over a finite time period by minimizing costs while meeting demand and technical constraints. However, many parameters re
Externí odkaz:
http://arxiv.org/abs/2409.03588
Publikováno v:
AI for Science workshop at ICML 2024
The ongoing energy transition drives the development of decentralised renewable energy sources, which are heterogeneous and weather-dependent, complicating their integration into energy systems. This study tackles this issue by introducing a novel re
Externí odkaz:
http://arxiv.org/abs/2406.19825
Policy-gradient algorithms are effective reinforcement learning methods for solving control problems with continuous state and action spaces. To compute near-optimal policies, it is essential in practice to include exploration terms in the learning o
Externí odkaz:
http://arxiv.org/abs/2402.00162
We propose in this paper an optimal control framework for renewable energy communities (RECs) equipped with controllable assets. Such RECs allow its members to exchange production surplus through an internal market. The objective is to control their
Externí odkaz:
http://arxiv.org/abs/2401.16321
Autor:
Cauz, Marine, Bolland, Adrien, Miftari, Bardhyl, Perret, Lionel, Ballif, Christophe, Wyrsch, Nicolas
Publikováno v:
PROCEEDINGS OF ECOS 2023
The decentralisation and unpredictability of new renewable energy sources require rethinking our energy system. Data-driven approaches, such as reinforcement learning (RL), have emerged as new control strategies for operating these systems, but they
Externí odkaz:
http://arxiv.org/abs/2307.04244
In this work, we generalize the problem of learning through interaction in a POMDP by accounting for eventual additional information available at training time. First, we introduce the informed POMDP, a new learning paradigm offering a clear distinct
Externí odkaz:
http://arxiv.org/abs/2306.11488
Direct policy optimization in reinforcement learning is usually solved with policy-gradient algorithms, which optimize policy parameters via stochastic gradient ascent. This paper provides a new theoretical interpretation and justification of these a
Externí odkaz:
http://arxiv.org/abs/2305.06851
Autor:
Oliver J. Evans, Josh Norman, Liam J. Carter, Thomas Hutchinson, Andrew Don, Rosalind M. Wright, Jeffrey A. Tuhtan, Gert Toming, Jonathan D. Bolland
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-12 (2024)
Abstract Globally, catadromous freshwater eels of the genus Anguilla are of conservation concern, including critically endangered European eel (Anguilla anguilla). Pumping stations that move river water to a higher elevation severely impact eels duri
Externí odkaz:
https://doaj.org/article/505874b083df424690108b2b2f88f4d3
Autor:
Delphine Planas, Isabelle Staropoli, Vincent Michel, Frederic Lemoine, Flora Donati, Matthieu Prot, Francoise Porrot, Florence Guivel-Benhassine, Banujaa Jeyarajah, Angela Brisebarre, Océane Dehan, Léa Avon, William Henry Bolland, Mathieu Hubert, Julian Buchrieser, Thibault Vanhoucke, Pierre Rosenbaum, David Veyer, Hélène Péré, Bruno Lina, Sophie Trouillet-Assant, Laurent Hocqueloux, Thierry Prazuck, Etienne Simon-Loriere, Olivier Schwartz
Publikováno v:
Nature Communications, Vol 15, Iss 1, Pp 1-17 (2024)
Abstract The unceasing circulation of SARS-CoV-2 leads to the continuous emergence of novel viral sublineages. Here, we isolate and characterize XBB.1, XBB.1.5, XBB.1.9.1, XBB.1.16.1, EG.5.1.1, EG.5.1.3, XBF, BA.2.86.1 and JN.1 variants, representing
Externí odkaz:
https://doaj.org/article/8234f50bbc5043a1a52a4adceb8cfad0
Publikováno v:
Transactions on Machine Learning Research, 2022
Reinforcement learning aims to learn optimal policies from interaction with environments whose dynamics are unknown. Many methods rely on the approximation of a value function to derive near-optimal policies. In partially observable environments, the
Externí odkaz:
http://arxiv.org/abs/2208.03520