Short term wind speed forecasting in La Venta, Oaxaca, México, using artificial neural networks

Erasmo Cadenas and Wilfrido Rivera

Renewable Energy, 2009, vol. 34, issue 1, 274-278

Abstract: In this paper the short term wind speed forecasting in the region of La Venta, Oaxaca, Mexico, applying the technique of artificial neural network (ANN) to the hourly time series representative of the site is presented. The data were collected by the Comisión Federal de Electricidad (CFE) during 7 years through a network of measurement stations located in the place of interest. Diverse configurations of ANN were generated and compared through error measures, guaranteeing the performance and accuracy of the chosen models. First a model with three layers and seven neurons was chosen, according to the recommendations of diverse authors, nevertheless, the results were not sufficiently satisfactory so other three models were developed, consisting of three layers and six neurons, two layers and four neurons and two layers and three neurons. The simplest model of two layers, with two input neurons and one output neuron, was the best for the short term wind speed forecasting, with mean squared error and mean absolute error values of 0.0016 and 0.0399, respectively. The developed model for short term wind speed forecasting showed a very good accuracy to be used by the Electric Utility Control Centre in Oaxaca for the energy supply.

Keywords: Wind speed forecasting; Neural networks (search for similar items in EconPapers)
Date: 2009
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Citations: View citations in EconPapers (61)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:renene:v:34:y:2009:i:1:p:274-278

DOI: 10.1016/j.renene.2008.03.014

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