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


YEŞİLBUDAK M., SAĞIROĞLU Ş., Colak I.

ENERGY CONVERSION AND MANAGEMENT, vol.69, pp.77-86, 2013 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 69
  • Publication Date: 2013
  • Doi Number: 10.1016/j.enconman.2013.01.033
  • Journal Name: ENERGY CONVERSION AND MANAGEMENT
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.77-86
  • Keywords: Wind speed, Very short term prediction, Input space, k-NN classification, NEURAL-NETWORK, MODEL, GENERATION, FORECAST
  • Gazi University Affiliated: Yes

Abstract

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. (C) 2013 Elsevier Ltd. All rights reserved.