Please use this identifier to cite or link to this item: http://hdl.handle.net/10553/42860
Title: Experiments in short-term wind power prediction using variable selection
Authors: Lorenzo, Javier 
Mendez, Juan 
Hernandez, Daniel 
Castrillón, Modesto 
UNESCO Clasification: 120304 Inteligencia artificial
Keywords: Machine learning
Neural networks
k-NN
Short-term wind farm power prediction.
Issue Date: 2011
Project: Tecnicas de Visión Para la Interacción en Entornos de Interior Con Elaboración Mapas Cognitivos en Sistemas Perceptuales Heterogéneos. 
Conference: 3rd International Conference on Agents and Artificial Intelligence, ICAART 2011 
Abstract: In this paper some experiments have been realized to test how the introduction of variable selection has an effect on the predictor performance in short-term wind farm power prediction. Variable selection based on Kraskov estimation of the mutual information will be used due to its capability to deal with sets of continuous random variables. A Multilayer Percetron and a k-NN estimator will be the predictor based models with different topologies and number of neighbors. Experiments will be carried out with actual data of wind speed and power of an experimental wind farm. We also compute the output of an ideal wind turbine to enrich the dataset and estimate the effect of variable selection on one isolated turbine. This will allow us to define four different settings for the experiments which vary in the nature of the inputs to the model, wind speed, wind farm or isolated wind turbine power, and the predicted variable, wind farm or isolated wind turbine power.
URI: http://hdl.handle.net/10553/42860
ISBN: 978-989-8425-40-9
DOI: 10.5220/0003182703700375
Source: ICAART 2011 - Proceedings of the 3rd International Conference on Agents and Artificial Intelligence, v. 2, p. 370-375
Appears in Collections:Actas de congresos
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