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Sustainability | Free Full-Text | Machine Learning Classification and Prediction of Wind Estimation Using Artificial Intelligence Techniques and Normal PDF

Estimating wind energy at a specific wind site depends on how well the real wind data in that area can be represented using an appropriate distribution function. In fact, wind sites differ in the extent to which their wind data can be represented from one region to another, despite the widespread use of the Weibull function in representing the wind speed in various wind locations in the world. In this study, a new probability distribution model (normal PDF) was tested to implement wind speed at several wind locations in Jordan. The results show high compatibility between this model and the wind resources in Jordan. Therefore, this model was used to estimate the values of the wind energy and the extracted energy of wind turbines compared to those obtained by the Weibull PDF. Several artificial intelligence techniques were used (GA, BFOA, SA, and a neuro-fuzzy method) to estimate and predict the parameters of both the normal and Weibull PDFs that were reflected in conjunction with the ac ....

Jordan General , Souss Massa Drâmo , Federation Of Bosnia And Herzegovina , Bosnia Herzegovina , Rayleigh Pdfs , Weibull Pdfs , Artificial Intelligence Techniques , Neural Fuzzy , Escherichia Coli , Neural Fuzzy Inference System , Most Accurate Approach Based , Absolute Error , Learning Classification , Prediction Based ,