I agree that this site is using cookies. You can find further informations
here
.
X
Login
Merkliste (
0
)
Home
About us
Home About us
Our history
Profile
Press & public relations
Friends
The library in figures
Exhibitions
Projects
Training, internships, careers
Films
Services & Information
Home Services & Information
Lending and interlibrary loans
Returns and renewals
Training and library tours
My Account
Library cards
New to the library?
Download Information
Opening hours
Learning spaces
PC, WLAN, copy, scan and print
Catalogs and collections
Home Catalogs and Collections
Rare books and manuscripts
Digital collections
Subject Areas
Our sites
Home Our sites
Central Library
Law Library (Juridicum)
BB Business and Economics (BB11)
BB Physics and Electrical Engineering
TB Engineering and Social Sciences
TB Economics and Nautical Sciences
TB Music
TB Art & Design
TB Bremerhaven
Contact the library
Home Contact the library
Staff Directory
Open access & publishing
Home Open access & publishing
Reference management: Citavi & RefWorks
Publishing documents
Open Access in Bremen
zur Desktop-Version
Toggle navigation
Merkliste
1 Ergebnisse
1
Deep Learning-Based Prediction of Maximum Carrying Capacity..:
, In:
2023 8th International Conference on Power and Renewable Energy (ICPRE)
,
Yujia, Gu
;
Jiecong, Wang
;
Hongqiang, Li
... - p. 1714-1718 , 2023
Link:
https://doi.org/10.1109/ICPRE59655.2023.10353611
RT T1 <cTitle>2023 8th International Conference on Power and Renewable Energy (ICPRE)</cTitle>: T1 <Title>Deep Learning-Based Prediction of Maximum Carrying Capacity of Key Transmission Sections for Photovoltaic Integration</Title> UL https://suche.suub.uni-bremen.de/peid=ieee-10353611&Exemplar=1&LAN=DE A1 Yujia, Gu A1 Jiecong, Wang A1 Hongqiang, Li A1 Guangming, Lu A1 Hanhua, Zhang A1 Lulu, Zhang YR 2023 SN 2768-0525 K1 Photovoltaic systems K1 Industries K1 Renewable energy sources K1 Power transmission lines K1 Power measurement K1 Neural networks K1 Power system stability K1 Photovoltaic power generation K1 button input section K1 GA-BPNN neural network K1 parameter optimization SP 1714 OP 1718 LK http://dx.doi.org/https://doi.org/10.1109/ICPRE59655.2023.10353611 DO https://doi.org/10.1109/ICPRE59655.2023.10353611 SF ELIB - SuUB Bremen
Export
RefWorks (nur Desktop-Version!)
Flow
(Zuerst in
Flow
einloggen, dann importieren)
