Zobrazeno 1 - 4
of 4
pro vyhledávání: '"Théophile Simo"'
Publikováno v:
Energy Reports, Vol 10, Iss , Pp 2793-2803 (2023)
Due to its cheapest cost of production, flexibility, and very low indirect emissions, hydroelectricity is of utmost importance to the economy. Its production is challenging due to the stochastic nature of water and other environmental factors. Hence,
Externí odkaz:
https://doaj.org/article/bf3042387d204342b979bc5af255887a
Publikováno v:
Heliyon, Vol 9, Iss 6, Pp e16456- (2023)
Accurate reservoir inflow forecasting is crucial for efficient water management. In this study, different deep learning models, including Dense, Long short-term memory (LSTM), and one-dimensional convolutional neural networks (Conv1D), were used to b
Externí odkaz:
https://doaj.org/article/af765027df944787a037a21bfafb99bd
Autor:
Njogho Kenneth Tebong, Théophile Simo, Armand Nzeukou Takougang, Alain Tchakoutio Sandjon, Ntanguen Patrick Herve
Publikováno v:
Journal of Hydrology: Regional Studies, Vol 46, Iss , Pp 101357- (2023)
Study region: Song bengue confluent in Cameroon regulates the river flow rate for hydro energy production with input from four upstream reservoirs. Study focus: Deep learning models forecast a day flow rate of the Song bengue confluent. Decomposed ti
Externí odkaz:
https://doaj.org/article/37d62a2e06d24b5fb0131a7f88c9d18c
Contribution to the long-term generation scheduling of the Cameroonian electricity production system
Publikováno v:
Electric Power Systems Research. 77:1265-1273
This paper presents the new long-term generation scheduling model that has been developed to improve the generation scheduling of the electric system of Cameroon. Based on the Dantzig-Wolfe Decomposition Technique (DWDT), the proposed solution approa
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::0bb7486bbf1b6e6629b565458df9e531
https://doi.org/10.1016/j.epsr.2006.09.017
https://doi.org/10.1016/j.epsr.2006.09.017