Artificial neural network models for wind power short-term forecasting using weather predictions

Authors:
I. J. Ramírez-Rosado;L. A. Fernández-Jiménez;C. Monteiro
Affiliations:
Electrical Engineering Department, University of La Rioja, Spain;Electrical Engineering Department, University of La Rioja, Spain;FEUP, Fac. Eng. Univ. Porto, and INESC - Instituto de Engenharia de Sistemas e Computadores do Porto, Portugal
Venue:
MIC'06 Proceedings of the 25th IASTED international conference on Modeling, indentification, and control
Year:
2006

Citing 3
Cited 1

Neural networks and principal component analysis: learning from examples without local minima

Neural Networks
Neural Networks: A Comprehensive Foundation

Neural Networks: A Comprehensive Foundation
Artificial Neural Networks: Approximation and Learning Theory

Artificial Neural Networks: Approximation and Learning Theory

Short-term wind power forecasting using simple recurrent genetically optimized neural networks

MIC '08 Proceedings of the 27th IASTED International Conference on Modelling, Identification and Control

Quantified Score

Hi-index	0.00

Visualization

Abstract

The use of wind energy has developed significantly worldwide. Wind power is the strongest growing form of renewable energy, ideal for a future with pollution-free electric power. But the integration of wind farms in power networks has become an important problem for the unit commitment and control of power plants in electric power systems. The intermittent nature of wind makes it difficult to forecast wind-produced electric energy in a wind farm even in the next hours. This paper compares the results obtained with a set of selected models for hourly electric power production forecasting in a real-life wind farm. The results show a significant improvement if previous numerical weather forecasts are used as input in hourly power forecasting models.