@Article{LimaGueFrePanMat:2017:MeMoSh,
author = "Lima, Jo{\~a}o Marcos and Guetter, Alexandre K. and Freitas,
Saulo R. and Panetta, Jairo and Mattos, Jo{\~a}o Gerd Zell de",
affiliation = "{Copel Gera{\c{c}}{\~a}o e Transmiss{\~a}o S.A} and
{Universidade Federal do Paran{\'a} (UFPR)} and {USRA/GESTAR at
NASA/GFSC} and {Instituto Tecnol{\'o}gico de Aeron{\'a}utica
(ITA)} and {Instituto Nacional de Pesquisas Espaciais (INPE)}",
title = "A meteorological–statistic model for short-term wind power
forecasting",
journal = "Journal of Control, Automation and Electrical Systems",
year = "2017",
volume = "28",
number = "5",
pages = "679--691",
month = "Oct.",
keywords = "Wind power forecast · Numerical weather prediction models · Kalman
filtering · Power curve.",
abstract = "The problem of wind power forecasting is addressed in this work,
considering a combination of a numerical weather prediction model
(NWP) and statistical models. Brazilian developments on the
Regional Atmospheric Modeling System is employed in two different
areas in Brazil to simulate forecasts of 72 h ahead of the wind
speed, at each 10 min. In one of the areas studied, the wind speed
is converted into wind power. Different conversion methods are
employed and discussed. Kalman filtering techniques are employed
to reduce systematic error of the forecasts, both wind and
generation. Each 72-h period of the NWP simulations had a
computational time of approximately 6070 min using indicating that
the proposed method can be applied in real time for power system
operation. The results obtained are very encouraging for further
investigation to achieve more accurate wind power researches.",
doi = "10.1007/s40313-017-0329-8",
url = "http://dx.doi.org/10.1007/s40313-017-0329-8",
issn = "21953880 and 21953899",
language = "en",
targetfile = "lima_meteorological.pdf",
urlaccessdate = "27 abr. 2024"
}