USING NEURAL NETWORKS TO FORECAST RENEWABLE ENERGY RESOURCES

Rafael Peña, Aurelio Medina

2011

Abstract

This contribution presents the application of feed-forward neural networks to the problem of time series forecasting. This forecast technique is applied to the water flow and wind speed time series. The results obtained from the forecasting of these two renewable resources can be used to determine the power generation capacity of micro or mini-hydraulic plants, and wind parks, respectively. The forecast values obtained with the neural network are compared against the original time series data in order to show the precision of this forecast technique.

Paper Citation

in Harvard Style

Peña R. and Medina A. (2011). USING NEURAL NETWORKS TO FORECAST RENEWABLE ENERGY RESOURCES . In Proceedings of the International Conference on Neural Computation Theory and Applications - Volume 1: NCTA, (IJCCI 2011) ISBN 978-989-8425-84-3, pages 401-404. DOI: 10.5220/0003684204010404

in Bibtex Style

@conference{ncta11,
author={Rafael Peña and Aurelio Medina},
title={USING NEURAL NETWORKS TO FORECAST RENEWABLE ENERGY RESOURCES},
booktitle={Proceedings of the International Conference on Neural Computation Theory and Applications - Volume 1: NCTA, (IJCCI 2011)},
year={2011},
pages={401-404},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003684204010404},
isbn={978-989-8425-84-3},
}

in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Neural Computation Theory and Applications - Volume 1: NCTA, (IJCCI 2011)
TI - USING NEURAL NETWORKS TO FORECAST RENEWABLE ENERGY RESOURCES
SN - 978-989-8425-84-3
AU - Peña R.
AU - Medina A.
PY - 2011
SP - 401
EP - 404
DO - 10.5220/0003684204010404