Zobrazeno 1 - 10
of 10
pro vyhledávání: '"Janne Huotari"'
Publikováno v:
IEEE Access, Vol 7, Pp 152879-152890 (2019)
We present a reinforcement learning (RL) model that is based on Q-learning for the autonomous control of ship auxiliary power networks. The development and application of the proposed model is demonstrated using a case-study ship as the platform. The
Externí odkaz:
https://doaj.org/article/fa29f148835846f9ad6f74b10a8b4a20
Publikováno v:
Journal of Marine Science and Engineering, Vol 9, Iss 9, p 944 (2021)
This paper evaluates the effect of a large-capacity electrical energy storage, e.g., Li-ion battery, on optimal sailing routes, speeds, fuel choice, and emission abatement technology selection. Despite rapid cost reduction and performance improvement
Externí odkaz:
https://doaj.org/article/ba99c17b87a642758a37e957879b0d31
Publikováno v:
Journal of Marine Science and Engineering, Vol 9, Iss 7, p 730 (2021)
We present a novel convex optimisation model for ship speed profile optimisation under varying environmental conditions, with a fixed schedule for the journey. To demonstrate the efficacy of the proposed method, a combined speed profile optimisation
Externí odkaz:
https://doaj.org/article/4ca4bdecb5ba47a8b9fd4d65643aefec
Publikováno v:
Energies, Vol 13, Iss 18, p 4748 (2020)
We present a novel methodology for the control of power unit commitment in complex ship energy systems. The usage of this method is demonstrated with a case study, where measured data was used from a cruise ship operating in the Caribbean and the Med
Externí odkaz:
https://doaj.org/article/82c8beca3a224e79a8136a92c28cbb9e
Over the coming decades, maritime transportation will transition from fossil hydrocarbon fuels to hydrogen, ammonia, and synthetic hydrocarbon fuels produced using renewable electricity as the primary energy source. In this context, a shipowner needs
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a617af487e2a26831cde6c3977b0f5fd
https://aaltodoc.aalto.fi/handle/123456789/119545
https://aaltodoc.aalto.fi/handle/123456789/119545
Autor:
Andrea Coraddu, Antoni Gil, Bakytzhan Akhmetov, Lizhong Yang, Alessandro Romagnoli, Antti Ritari, Janne Huotari, Kari Tammi
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::c2163bc118466200a6758718b5b21bf6
https://doi.org/10.1016/b978-0-12-824471-5.00012-8
https://doi.org/10.1016/b978-0-12-824471-5.00012-8
Autor:
Bakytzhan Akhmetov, Karin Andersson, Francesco Baldi, Volker Bertram, Selma Brynolf, Tao Cao, Stefano Clemente, Andrea Coraddu, Mia Elg, Konstantinos Fakiolas, Antoni Gil, Maria Grahn, Julia Hansson, Janne Huotari, Davide Ilardi, Miltiadis Kalikatzarakis, Andrei David Korberg, Elin Malmgren, Diego Micheli, Luca Oneto, Antoni Gil Pujol, Antti Ritari, Alessandro Romagnoli, Orestis Schinas, Santiago Suárez de la Fuente, Rodolfo Taccani, Kari Tammi, Fabian Thies, Lindert van Biert, Klaas Visser, Jake Walker, Lizhong Yang
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::4e8b839679c97cdb69ca3beaff1e4377
https://doi.org/10.1016/b978-0-12-824471-5.00005-0
https://doi.org/10.1016/b978-0-12-824471-5.00005-0
Publikováno v:
Energies; Volume 13; Issue 18; Pages: 4748
We present a novel methodology for the control of power unit commitment in complex ship energy systems. The usage of this method is demonstrated with a case study, where measured data was used from a cruise ship operating in the Caribbean and the Med
The inclusion of a battery system for a diesel mechanical short sea ship was investigated. The main benefits of the battery were assumed to emerge from shaving thruster generated power peaks, rather than starting additional generating sets to accommo
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::7bd711b3a6719f37c2b3c9cffe670935
https://aaltodoc.aalto.fi/handle/123456789/41195
https://aaltodoc.aalto.fi/handle/123456789/41195
Publikováno v:
IEEE Access, Vol 7, Pp 152879-152890 (2019)
We present a reinforcement learning (RL) model that is based on Q-learning for the autonomous control of ship auxiliary power networks. The development and application of the proposed model is demonstrated using a case-study ship as the platform. The
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c7da27c9f6cf0f58fb8755c67e2afa40
https://aaltodoc.aalto.fi/handle/123456789/41171
https://aaltodoc.aalto.fi/handle/123456789/41171