<p>Wind power forecasting is of greater importance to increase the wind power penetration to the grid as well as to maintain the grid stability. Wind power varies with cubic times the wind speed. Thus, accurate forecasting of wind speed is a preliminary process. This paper projects the analysis of wind speed forecasting using various statistical approaches and describes Auto Regressive Integrated Moving Average (ARIMA) method based wind speed forecasting in detail. Historical time series wind speed data, with a time interval of 3 hours average, collected from Amrita Wind Energy Centre is considered for the analysis. Results show that ARIMA model forecast the wind speed with better accuracy. Consideration of multivariate data and seasonal factors are also suggested to improve the wind speed forecasting accuracy. © International Science Press.</p>
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Aa Vishnupriyadharshini, Vanitha, Va, and Dr. Palanisamy T., “Wind speed forecasting based on statistical Auto Regressive Integrated Moving Average (ARIMA) method”, International Journal of Control Theory and Applications, vol. 9, pp. 7681-7690, 2016.