In this article we have used the ARMA (autoregressive moving average
process) and persistence models to predict the hourly average wind
speed up to 10 h in advance. In order to adjust the time series to
the ARMA models, it has been necessary to carry out their transformation
and standardization, given the non-Gaussian nature of the hourly
wind speed distribution and the non-stationary nature of its daily
evolution. In order to avoid seasonality problems we have adjusted
a different model to each calendar month. The study expands to five
locations with different topographic characteristics and to nine
years. It has been proven that the transformation and standardization
of the original series allow the use of ARMA models and these behave
significantly better in the forecast than the persistence model,
especially in the longer-term forecasts. When the acceptable RMSE
(root mean square error) in the forecast is limited to 1.5 m/s, the
models are only valid in the short term.
%0 Journal Article
%1 Torres.Garcia.ea2005
%A Torres, J. L.
%A Garcıa, A.
%A Blas, M. De
%A Francisco, A. De
%D 2005
%J Solar Energy
%K ARIMA Wind forecasting, models, series, speed time
%P 65--77
%T Forecast of hourly average wind speed with ARMA models in Navarre
(Spain)
%V 79
%X In this article we have used the ARMA (autoregressive moving average
process) and persistence models to predict the hourly average wind
speed up to 10 h in advance. In order to adjust the time series to
the ARMA models, it has been necessary to carry out their transformation
and standardization, given the non-Gaussian nature of the hourly
wind speed distribution and the non-stationary nature of its daily
evolution. In order to avoid seasonality problems we have adjusted
a different model to each calendar month. The study expands to five
locations with different topographic characteristics and to nine
years. It has been proven that the transformation and standardization
of the original series allow the use of ARMA models and these behave
significantly better in the forecast than the persistence model,
especially in the longer-term forecasts. When the acceptable RMSE
(root mean square error) in the forecast is limited to 1.5 m/s, the
models are only valid in the short term.
@article{Torres.Garcia.ea2005,
abstract = {In this article we have used the ARMA (autoregressive moving average
process) and persistence models to predict the hourly average wind
speed up to 10 h in advance. In order to adjust the time series to
the ARMA models, it has been necessary to carry out their transformation
and standardization, given the non-Gaussian nature of the hourly
wind speed distribution and the non-stationary nature of its daily
evolution. In order to avoid seasonality problems we have adjusted
a different model to each calendar month. The study expands to five
locations with different topographic characteristics and to nine
years. It has been proven that the transformation and standardization
of the original series allow the use of ARMA models and these behave
significantly better in the forecast than the persistence model,
especially in the longer-term forecasts. When the acceptable RMSE
(root mean square error) in the forecast is limited to 1.5 m/s, the
models are only valid in the short term.},
added-at = {2011-09-01T13:26:03.000+0200},
author = {Torres, J. L. and Garc\ia, A. and Blas, M. De and Francisco, A. De},
biburl = {https://www.bibsonomy.org/bibtex/2f50e8db2aa87a10a71ef1a1dbbccc596/procomun},
file = {Torres.Garcia.ea2005.pdf:Torres.Garcia.ea2005.pdf:PDF},
interhash = {9a15dc57b467b7a210e3bafcb57a0f0c},
intrahash = {f50e8db2aa87a10a71ef1a1dbbccc596},
journal = {Solar Energy},
keywords = {ARIMA Wind forecasting, models, series, speed time},
owner = {oscar},
pages = {65--77},
refid = {Torres.Garc.ea2005},
timestamp = {2011-09-02T08:25:25.000+0200},
title = {Forecast of hourly average wind speed with ARMA models in Navarre
(Spain)},
volume = 79,
year = 2005
}