The Application of a Hybrid Artificial Neural Network and Genetic Algorithm (ANN-GA) to Mapping of Wind Speed Profile for Electrical Energy applications: A Case study for South Coasts of Iran

Since wind speed greatly influences the issues such as the scheduling of a power system, and the dynamic control of the wind turbine, wind speed prediction is critical for wind energy conversion systems. In this study, a Hybrid Artificial Neural Network and Genetic Algorithm (MLP-GA) is applied to predict monthly average wind speed, based on meteorological variables. Month of the year, monthly mean air temperature, mean relative humidity, vapor pressure and wind speed data between 1994 and 2005 provided by Iranian Meteorological Organization for south coasts of Iran, were used in this study. In order to consider the effect of each meteorological variable on wind speed prediction, two following combinations of input variables are considered: (i) Month of the year, monthly mean daily air temperature, and relative humidity as inputs, and monthly mean daily wind speed as output; (ii) Month of the year, monthly mean daily air temperature, relative humidity, and vapor pressure as inputs, and monthly mean daily wind speed as output. The measured data between 1994 and 2003 were used as training and the remained data (i.e. 2004 and 2005) used as testing. Obtained results indicate that using vapor pressure along with the month of the year, monthly mean daily air temperature, and relative humidity has better performance than another case with the mean absolute percentage error of 10.86% and correlation of coefficient of 93.95% on testing data sets.

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