@conference{10902/9865,
year = {2014},
month = {4},
url = {http://hdl.handle.net/10902/9865},
abstract = {Ampacity techniques have been used by Distributor System Operators (DSO) and Transport System  Operators (TSO) in order to increase the static rate of transport and distribution infrastructures, especially those who are used for the grid integration of renewable energy. One of the main drawbacks of this technique is related with the fact that DSO and TSO need to do some planning tasks in advance. In order to perform a
previous planning it is compulsory to forecast the weather conditions in the short-time. This paper analyses the application of the neural network to the estimation of the ampacity in order to increase the amount of power produced by wind farms that can be integrated into the grid.},
organization = {This work was supported by the Spanish Government under the R+D initiative INNPACTO with reference IPT-2011-1447-920000.},
publisher = {The European Association for the Development of Renewable Energies, Environment and Power Quality (EA4EPQ)},
publisher = {Renewable Energy & Power Quality Journal, 2014, Vol.1 (12), 120-123},
publisher = {International Conference on Renewable Energies and Power Quality (ICREPQ’14), Córdoba},
title = {Ampacity forecasting using neural networks},
author = {Martínez Torre, Raquel and González Diego, Antonio and Madrazo Maza, Alfredo and Mañana Canteli, Mario and Arroyo Gutiérrez, Alberto and Cavia Soto, María de los Ángeles and Domingo Fernández, Rodrigo and Sierra Molleda, Alberto and Laso Pérez, Alberto},
}