A wind speed forecasting approach based on 2-dimensional input space

Wind speed forecasting is required for ensuring an efficient utilization of the wind power generated by wind turbines. This paper purposes the short-term wind speed forecasting in a 2-dimesional input space using the developed k-nearest neighbor (k-NN) classifier. As well, the effects of the nearest neighbor number and the selected distance metric on the wind speed forecasting were analyzed and many useful inferences were mined in order to minimize the forecasting error. The results have shown that the k-NN classifier which uses wind direction and relative humidity parameters achieved the best forecasting results for k=10 in the Minkowski distance metric. On the other hand, the k-NN classifier which uses wind direction and atmosphere pressure parameters gave the worst forecasting results for k=1 in the Euclidean distance metric.