Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Statistical learning for wind power : a modeling and stability study
  towards forecasting   We focus on wind power modeling using machine learning techniques. We show on
real data provided by the wind energy company Ma{\"i}a Eolis, that parametric
models, even following closely the physical equation relating wind production
to wind speed are outperformed by intelligent learning algorithms. In
particular, the CART-Bagging algorithm gives very stable and promising results.
Besides, as a step towards forecast, we quantify the impact of using
deteriorated wind measures on the performances. We show also on this
application that the default methodology to select a subset of predictors
provided in the standard random forest package can be refined, especially when
there exists among the predictors one variable which has a major impact.

29
The categories of this paper are: funct-an
8
The categories of this paper are: hep-ex
3
The categories of this paper are: physics
19
The categories of this paper are: eess
11
The categories of this paper are: hep-lat
33
The categories of this paper are: plasm-ph
2
The categories of this paper are: math
35
The categories of this paper are: mtrl-th
7
The categories of this paper are: hep-th
21
The categories of this paper are: adap-org
15
The categories of this paper are: nucl-ex
25
The categories of this paper are: patt-sol
34
The categories of this paper are: chem-ph
6
The categories of this paper are: astro-ph
10
The categories of this paper are: q-bio
24
The categories of this paper are: dg-ga