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Abstract: In this paper the analysis and forecasting of wind velocities in Chetumal, Quintana Roo, Mexico is presented. Measurements were made by the Instituto de Investigaciones Eléctricas (IIE) during two years, from 2004 to 2005. This location exemplifies the wind energy generation potential in the Caribbean coast of Mexico that could be employed in the hotel industry in the next decade. The wind speed and wind direction were measured at 10m above ground level. Sensors with high accuracy and a low starting threshold were used. The wind velocity was recorded using a data acquisition system supplied by a 10W photovoltaic panel. The wind speed values were measured with a frequency of 1Hz and the average wind speed was recorded considering regular intervals of 10min. First a statistical analysis of the time series was made in the first part of the paper through conventional and robust measures. Also the forecasting of the last day of measurements was made utilizing the single exponential smoothing method (SES). The results showed a very good accuracy of the data with this technique for an α value of 0.9. Finally the SES method was compared with the artificial neural network (ANN) method showing the former better results. [Copyright &y& Elsevier]

Abstract: In this paper the wind speed forecasting in the Isla de Cedros in Baja California, in the Cerro de la Virgen in Zacatecas and in Holbox in Quintana Roo is presented. The time series utilized are average hourly wind speed data obtained directly from the measurements realized in the different sites during about one month. In order to do wind speed forecasting Hybrid models consisting of Autoregressive Integrated Moving Average (ARIMA) models and Artificial Neural Network (ANN) models were developed. The ARIMA models were first used to do the wind speed forecasting of the time series and then with the obtained errors ANN were built taking into account the nonlinear tendencies that the ARIMA technique could not identify, reducing with this the final errors. Once the Hybrid models were developed 48 data out of sample for each one of the sites were used to do the wind speed forecasting and the results were compared with the ARIMA and the ANN models working separately. Statistical error measures such as the mean error (ME), the mean square error (MSE) and the mean absolute error (MAE) were calculated to compare the three methods. The results showed that the Hybrid models predict the wind velocities with a higher accuracy than the ARIMA and ANN models in the three examined sites. [Copyright &y& Elsevier]