A short term wind speed forcasting using SVR and BP-ANN: A comparative analysis

Forecasting wind speed is a very important part in weather forecasting. Because of the nonlinear behaviors of nature and climate changes, wind speed prediction becomes a challenging task, particularly country like Bangladesh where lots of areas are costal and season changes in frequent. This study is done to make an attempt to predict the wind speed using two very potential and wide frames of statistical data mining and machine learning approaches; Support Vector Regression (SVR) and Artificial Neural Network (ANN) with back propagation technique. 7years (2008–2014) historical dataset of wind speed of Chittagong costal area were collected from Bangladesh meteorological division (BMD) for undertaking the experiment. Leaky ReLu function was applied as the rectifier to the input data to control the thresholds and activations of neurons in MLP. The aim of this study was to propose a model that can predict short term wind speed with maximum accuracy. Finally, after considerable amount of experimentations the outcome from this study is; our proposed SVR and ANN models are able to predict wind speed with more than 99% accuracy in short term prediction. Moreover, ANN can outperform SVR in some situations with highest 99.80% accuracy. But SVR models are best suited for overall wind speed forecasting in different horizons with highest 99.60% accuracy. These results outperform the performances of previous recent works that are mentioned in literature review and reference sections of this paper.