Zobrazeno 1 - 10
of 22
pro vyhledávání: '"Dimitri Thomopulos"'
Publikováno v:
Energies, Vol 17, Iss 10, p 2346 (2024)
Predicting electricity production from renewable energy sources, such as solar photovoltaic installations, is crucial for effective grid management and energy planning in the transition towards a sustainable future. This study proposes machine learni
Externí odkaz:
https://doaj.org/article/4877ba09f353433780301746568b5d07
Autor:
Emanuele Guerrazzi, Dimitri Thomopulos, Davide Fioriti, Ivan Mariuzzo, Eva Schito, Davide Poli, Marco Raugi
Publikováno v:
Energies, Vol 16, Iss 17, p 6268 (2023)
Governments are promoting energy community (EC) policies to encourage joint investment and the operation of shared energy assets by citizens, industries, and public authorities, with the aim of promoting economic, social, and environmental benefits.
Externí odkaz:
https://doaj.org/article/6ebd5976274644ef965749b9c96544fb
Autor:
Giuseppe Anastasi, Carlo Bartoli, Paolo Conti, Emanuele Crisostomi, Alessandro Franco, Sergio Saponara, Daniele Testi, Dimitri Thomopulos, Carlo Vallati
Publikováno v:
Energies, Vol 14, Iss 8, p 2124 (2021)
Worldwide increasing awareness of energy sustainability issues has been the main driver in developing the concepts of (Nearly) Zero Energy Buildings, where the reduced energy consumptions are (nearly) fully covered by power locally generated by renew
Externí odkaz:
https://doaj.org/article/005d3dcb673e462d8a55df72964b37c9
Autor:
Alessandro Betti, Mauro Tucci, Emanuele Crisostomi, Antonio Piazzi, Sami Barmada, Dimitri Thomopulos
Publikováno v:
Sensors, Vol 21, Iss 5, p 1687 (2021)
In this paper, a novel and flexible solution for fault prediction based on data collected from Supervisory Control and Data Acquisition (SCADA) system is presented. Generic fault/status prediction is offered by means of a data driven approach based o
Externí odkaz:
https://doaj.org/article/6afb082576d3425c91e3dc21b64d5ca3
Autor:
Alexis Tantet, Marc Stéfanon, Philippe Drobinski, Jordi Badosa, Silvia Concettini, Anna Cretì, Claudia D’Ambrosio, Dimitri Thomopulos, Peter Tankov
Publikováno v:
Energies, Vol 12, Iss 22, p 4299 (2019)
We develop an open-source Python software integrating flexibility needs from Variable Renewable Energies (VREs) in the development of regional energy mixes. It provides a flexible and extensible tool to researchers/engineers, and for education/outrea
Externí odkaz:
https://doaj.org/article/1b35de5ac43c473aaaa301a30551a354
Publikováno v:
COMPEL - The international journal for computation and mathematics in electrical and electronic engineering. 41:2120-2133
Purpose This study aims to investigate the possible use of a deep neural network (DNN) as an inverse solver. Design/methodology/approach Different models based on DNNs are designed and proposed for the resolution of inverse electromagnetic problems e
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::a721cf24bd41a001589361bff5aa82f9
https://doi.org/10.1108/compel-10-2021-0383
https://doi.org/10.1108/compel-10-2021-0383
Publikováno v:
European Journal of Operational Research
European Journal of Operational Research, Elsevier, 2020, ⟨10.1016/j.ejor.2020.12.029⟩
European Journal of Operational Research, Elsevier, 2020, ⟨10.1016/j.ejor.2020.12.029⟩
In this paper, we tackle the hydro unit commitment problem and scheduling in a hydro valley. We first decompose the problem into several simpler subproblems, one for each reservoir/plant. Then, we model each of them as an optimization problem on grap
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a5b02ccd07837826b7ef2ee33f8fefdb
https://doi.org/10.1016/j.ejor.2020.12.029
https://doi.org/10.1016/j.ejor.2020.12.029
Publikováno v:
IEEE Transactions on Magnetics. 56:1-4
The computational cost of topology optimization based on the binary particle swarm optimization is greatly reduced by the use of deep neural networks (DNNs). A first convolutional neural network (CNN) is trained with data coming from finite-element a
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::be04aaf74ee14c3efc1f13e453ab2543
https://doi.org/10.1109/tmag.2019.2957197
https://doi.org/10.1109/tmag.2019.2957197
open 4 si We present a Branch-and-Cut algorithm for a class of nonlinear chance- constrained mathematical optimization problems with a finite number of scenarios. Unsatisfied scenarios can enter a recovery mode. This class corresponds to problems tha
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::04f27b85a1292bd5059649125acd67c5
https://hdl.handle.net/11585/763501
https://hdl.handle.net/11585/763501
Publikováno v:
Electronics
Volume 10
Issue 18
Electronics, Vol 10, Iss 2185, p 2185 (2021)
Volume 10
Issue 18
Electronics, Vol 10, Iss 2185, p 2185 (2021)
The use of behavioral models based on deep learning (DL) to accelerate electromagnetic field computations has recently been proposed to solve complex electromagnetic problems. Such problems usually require time-consuming numerical analysis, while DL
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c9c539f6b978d597186332174748b0e7