Stochastic simulation and forecast models of hourly average wind
speeds are presented. Time series models take into account several
basic features of wind speed data including autocorrelation, non-
Gaussian distribution and diurnal nonstationarity. The positive correlation
between consecutive wind speed observations is taken into account
by fitting an ARMA (p,q) process to wind speed data transformed to
make their distribution approximately Gaussian and standardized to
remove scattering of transformed data. Diurnal variations have been
taken into account to observe forecasts and its dependence on lead
times. We find the ARMA (p,q) model suitable for prediction intervals
and probability forecasts.
%0 Journal Article
%1 Kamal.Jafri1997
%A Kamal, L.
%A Jafri, Y. Z.
%D 1997
%J Solar Energy
%K ARIMA Wind analysis, forecasting models, series simulation, time
%N 1
%P 23--32
%T Time Series Models to Simulate and Forecast Hourly Averaged Wind
Speed in Quetta, Pakistan
%V 61
%X Stochastic simulation and forecast models of hourly average wind
speeds are presented. Time series models take into account several
basic features of wind speed data including autocorrelation, non-
Gaussian distribution and diurnal nonstationarity. The positive correlation
between consecutive wind speed observations is taken into account
by fitting an ARMA (p,q) process to wind speed data transformed to
make their distribution approximately Gaussian and standardized to
remove scattering of transformed data. Diurnal variations have been
taken into account to observe forecasts and its dependence on lead
times. We find the ARMA (p,q) model suitable for prediction intervals
and probability forecasts.
@article{Kamal.Jafri1997,
abstract = {Stochastic simulation and forecast models of hourly average wind
speeds are presented. Time series models take into account several
basic features of wind speed data including autocorrelation, non-
Gaussian distribution and diurnal nonstationarity. The positive correlation
between consecutive wind speed observations is taken into account
by fitting an ARMA (p,q) process to wind speed data transformed to
make their distribution approximately Gaussian and standardized to
remove scattering of transformed data. Diurnal variations have been
taken into account to observe forecasts and its dependence on lead
times. We find the ARMA (p,q) model suitable for prediction intervals
and probability forecasts.},
added-at = {2011-09-01T13:26:03.000+0200},
author = {Kamal, L. and Jafri, Y. Z.},
biburl = {https://www.bibsonomy.org/bibtex/2a15dcb7ca2cb06991470f76d84e8ebb7/procomun},
file = {Kamal.Jafri1997.pdf:Kamal.Jafri1997.pdf:PDF},
interhash = {c0628604394f14dc805ad8fc2ce5546f},
intrahash = {a15dcb7ca2cb06991470f76d84e8ebb7},
journal = {Solar Energy},
keywords = {ARIMA Wind analysis, forecasting models, series simulation, time},
number = 1,
owner = {oscar},
pages = {23--32},
refid = {Kamal.Jafri1997},
timestamp = {2011-09-02T08:25:25.000+0200},
title = {Time Series Models to Simulate and Forecast Hourly Averaged Wind
Speed in Quetta, Pakistan},
volume = 61,
year = 1997
}