Analysis of the Training Metrics of ANNs and Linear MCP Models Used for Wind Power Density Estimation at A Candidate Site

Sergio Velázquez
University of Las Palmas de Gran Canaria, Las Palmas, Canary Islands, Spain

José A. Carta
University of Las Palmas de Gran Canaria, Las Palmas, Canary Islands, Spain

José Matías
University of Vigo, Vigo, Spain

Ladda ner artikelhttp://dx.doi.org/10.3384/ecp11057834

Ingår i: World Renewable Energy Congress - Sweden; 8-13 May; 2011; Linköping; Sweden

Linköping Electronic Conference Proceedings 57:12, s. 834-841

Visa mer +

Publicerad: 2011-11-03

ISBN: 978-91-7393-070-3

ISSN: 1650-3686 (tryckt), 1650-3740 (online)


In order to estimate the amount of electricity that can be produced by a potential wind farm it is important to know how the wind resource performs at the site where it is to be installed. Of fundamental importance in an analysis of the wind resource is the wind speed parameter. Understanding how this parameter behaves over periods of time that cover ten or more years (long-term) is vital for an accurate estimation that will span the working life of the wind installation. However; in most cases there is insufficient data available about the candidate site to enable a long-term study.

In this work; the long-term wind power density at a candidate site is estimated through the use of a Measure-Correlate-Predict (MCP) algorithm and an Artificial Neural Network model (ANN). To evaluate the accuracy of the estimations different metrics are used; with a comparison of the results obtained for each of them.

The mean hourly wind speeds and directions obtained from twenty-two weather stations located on different islands in the Canary Archipelago (Spain) are used for this study.

Among the conclusions that are reached is that the use of one or another metric (or combination of metrics) in the wind power density estimation process can lead to differing interpretations and/or conclusions. For this reason; it is important that the most appropriate metric (or set of metrics) is chosen at each moment for the study that is being carried out.


Wind Power Density; Short-Term estimation; Long-Term estimation; Artificial Neural Networks; Measure Correlate Predict


[1] TR. Hiester; WT. Pennell; The siting handbook for large wind energy systems. 1st ed. New York: WindBook; 1981.

[2] GW. Koeppl; Putnam’s power from the wind. Second ed. New York: Van Nostrand Reinhold Company; 1982.

[3] CG. Justus; K. Mani; AS. Mikhail; Interannual and month-to-month variations of wind speed. Journal of Applied Meteorology 18; 1979; 913–932. doi: 10.1175/1520-0450(1979)018<0913:IAMTMV>2.0.CO;2.

[4] CI. Aspliden; DL. Elliott; LL. Wendell; Resources assessment method; siting; and performance evaluation. In: Guzzi R; Justus CG. Physical climatology for solar and wind energy; New Jersey: World Scientific; 1988; pp 321-76.

[5] R. García-Rojo; Algorithm for the estimation of the long-term wind climate at a meteorological mast using a joint probabilistic approach; Wind Engineering 28; 2004; pp 213-236. doi: 10.1260/0309524041211378.

[6] AL. Rogers; JW. Rogers; JF. Manwell; Comparison of the performance of four Measure-Correlate-Predict algorithms. Journal of Wind Engineering and Industrial Aerodynamics 93; 2005; pp 243-264. doi: 10.1016/j.jweia.2004.12.002.

[7] JC. Woods; SJ. Watson; A new matrix method of predicting long-term wind roses with MCP; Journal of Wind Engineering and Industrial Aerodynamics 66; 1997; pp 85-94. doi: 10.1016/S0167-6105(97)00009-3.

[8] JA. Carta; S. Velázquez; JM. Matías; Use of Bayesian Networks classifiers for long-term mean wind turbine energy output estimation at a potential wind energy conversion site; Energy Conversion and Management 52; 2011; pp 1137-1149. doi: 10.1016/j.enconman.2010.09.008.

[9] A. Oztopal; Artificial Neural Network approach to spatial estimation of wind velocity; Energy Conversion and Management 47; 2006; 395-406. doi: 10.1016/j.enconman.2005.05.009.

[10] P. Lopez ; R. Velo; F. Maseda; Effect of direction on wind speed estimation in complex terrain using neural networks; Renewable Energy 33; 2008;33; pp 2266-2272.

[11] S. Velázquez; JA. Carta; JM. Matías; Influence of the input layer signals of ANNs on wind power estimation for a target site: A case study; Renewable and Sustainable Energy Reviews; 15; 2011; pp 1556-1566 doi: 10.1016/j.rser.2010.11.036.

[12] DA. Fadare; The application of artificial neural networks to mapping of wind speed profile for energy application in Nigeria; Applied Energy; 87; 2010; pp 934-942. doi: 10.1016/j.apenergy.2009.09.005.

[13] JC. Principe; NR. Euliano; WC. Lefebvre; Neural and Adaptive Systems. Fundamentals Through Simulations; first ed. New York: John Wiley & Sons; Inc.; 2000.

Citeringar i Crossref