Authors:
Javier Lorenzo
;
Juan Méndez
;
Daniel Hernández
and
Modesto Castrillón
Affiliation:
Univ. de Las Palmas de Gran Canaria, Spain
Keyword(s):
Machine learning, Neural networks, k-NN, Short-term wind farm power prediction.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Computational Intelligence
;
Evolutionary Computing
;
Health Engineering and Technology Applications
;
Human-Computer Interaction
;
Industrial Applications of AI
;
Knowledge Discovery and Information Retrieval
;
Knowledge-Based Systems
;
Machine Learning
;
Methodologies and Methods
;
Neural Networks
;
Neurocomputing
;
Neurotechnology, Electronics and Informatics
;
Pattern Recognition
;
Physiological Computing Systems
;
Sensor Networks
;
Signal Processing
;
Soft Computing
;
Symbolic Systems
;
Theory and Methods
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.