A new approach to very short term wind speed prediction using k-nearest neighbor classification

作者：Yesilbudak Mehmet; Sagiroglu Seref; Colak Ilhami^*

来源：Energy Conversion and Management, 2013, 69: 77-86.

DOI：10.1016/j.enconman.2013.01.033

• Wind speed
• Very short term prediction
• Input space
• k-NN classification

Wind energy is an inexhaustible energy source and wind power production has been growing rapidly in recent years. However, wind power has a non-schedulable nature due to wind speed variations. Hence, wind speed prediction is an indispensable requirement for power system operators. This paper predicts wind speed parameter in an n-tupled inputs using k-nearest neighbor (k-NN) classification and analyzes the effects of input parameters, nearest neighbors and distance metrics on wind speed prediction. The k-NN classification model was developed using the object oriented programming techniques and includes Manhattan and Minkowski distance metrics except from Euclidean distance metric on the contrary of literature. The k-NN classification model which uses wind direction, air temperature, atmospheric pressure and relative humidity parameters in a 4-tupled space achieved the best wind speed prediction for k = 5 in the Manhattan distance metric. Differently, the k-NN classification model which uses wind direction, air temperature and atmospheric pressure parameters in a 3-tupled inputs gave the worst wind speed prediction for k = 1 in the Minkowski distance metric.

• 出版日期2013-5