Recognition of Wind Speed Patterns Using Multi-Scale Subspace Grids with Decision Trees

Abstract

The wind speed patterns are essential and indispensable requirement for the efficient utilization of the wind power generated by wind turbines. For this reason, this paper proposes a new approach in order to recognize the wind speed patterns from the multidimensional meteorological data. The meteorological dataset used in this study includes wind direction, air temperature, atmospheric pressure, relative humidity and wind speed parameters. Firstly, the proposed approach eliminated the dimensionality problem of the total dataset by means of obtaining the lower dimensional subspaces with the principal component analysis and the multiple discriminant analysis. Secondly, the proposed approach alleviated the problem of small sample sizes by means of achieving the coarse scales as generic rules at the lower dimensional subspaces. The total dataset includes 3244 observations for each meteorological parameter. In this study, 3100 data points were used for extracting the rules and 144 data points were utilized for testing the extracted rules. As a result, it is mined that the proposed approach leads to reveal the wind speed patterns in a usable and comprehensive manner.