Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Performance Comparison of Different Machine Learning Algorithms on the
  Prediction of Wind Turbine Power Generation   Over the past decade, wind energy has gained more attention in the world.
However, owing to its indirectness and volatility properties, wind power
penetration has increased the difficulty and complexity in dispatching and
planning of electric power systems. Therefore, it is needed to make the
high-precision wind power prediction in order to balance the electrical power.
For this purpose, in this study, the prediction performance of linear
regression, k-nearest neighbor regression and decision tree regression
algorithms is compared in detail. k-nearest neighbor regression algorithm
provides lower coefficient of determination values, while decision tree
regression algorithm produces lower mean absolute error values. In addition,
the meteorological parameters of wind speed, wind direction, barometric
pressure and air temperature are evaluated in terms of their importance on the
wind power parameter. The biggest importance factor is achieved by wind speed
parameter. In consequence, many useful assessments are made for wind power
predictions.

30
The categories of this paper are: q-alg
4
The categories of this paper are: cond-mat
6
The categories of this paper are: astro-ph
23
The categories of this paper are: solv-int
34
The categories of this paper are: chem-ph
28
The categories of this paper are: alg-geom
33
The categories of this paper are: plasm-ph
19
The categories of this paper are: eess
14
The categories of this paper are: math-ph
37
The categories of this paper are: supr-con
9
The categories of this paper are: nlin
12
The categories of this paper are: nucl-th
16
The categories of this paper are: stat
15
The categories of this paper are: nucl-ex
10
The categories of this paper are: q-bio
32
The categories of this paper are: atom-ph