Wind power is an increasingly used form of renewable energy. The
uncertainty in wind generation is very largely due to the inherent
variability in wind speed, and this needs to be understood by operators
of power systems and wind farms. To assist with the management of
this risk, this paper investigates methods for predicting the probability
density function of generated wind power from one to 10 days ahead
at five UK wind farm locations. These density forecasts provide a
description of the expected future value and the associated uncertainty.
We construct density forecasts from weather ensemble predictions,
which are a relatively new type of weather forecast generated from
atmospheric models. We also consider density forecasting from statistical
time series models. The best results for wind power density prediction
and point forecasting were produced by an approach that involves
calibration and smoothing of the ensemble-based wind power density.
%0 Generic
%1 Taylor.McSharry2007
%A Taylor, J. W.
%A McSharry, P. E.
%D 2007
%K Density GARCH Power, Wind ensemble forecasting, models, predictions, speed weather
%T Wind Power Density Forecasting Using Ensemble Predictions and Time
Series Models
%X Wind power is an increasingly used form of renewable energy. The
uncertainty in wind generation is very largely due to the inherent
variability in wind speed, and this needs to be understood by operators
of power systems and wind farms. To assist with the management of
this risk, this paper investigates methods for predicting the probability
density function of generated wind power from one to 10 days ahead
at five UK wind farm locations. These density forecasts provide a
description of the expected future value and the associated uncertainty.
We construct density forecasts from weather ensemble predictions,
which are a relatively new type of weather forecast generated from
atmospheric models. We also consider density forecasting from statistical
time series models. The best results for wind power density prediction
and point forecasting were produced by an approach that involves
calibration and smoothing of the ensemble-based wind power density.
@misc{Taylor.McSharry2007,
abstract = {Wind power is an increasingly used form of renewable energy. The
uncertainty in wind generation is very largely due to the inherent
variability in wind speed, and this needs to be understood by operators
of power systems and wind farms. To assist with the management of
this risk, this paper investigates methods for predicting the probability
density function of generated wind power from one to 10 days ahead
at five UK wind farm locations. These density forecasts provide a
description of the expected future value and the associated uncertainty.
We construct density forecasts from weather ensemble predictions,
which are a relatively new type of weather forecast generated from
atmospheric models. We also consider density forecasting from statistical
time series models. The best results for wind power density prediction
and point forecasting were produced by an approach that involves
calibration and smoothing of the ensemble-based wind power density.},
added-at = {2011-09-01T13:26:03.000+0200},
author = {Taylor, J. W. and McSharry, P. E.},
biburl = {https://www.bibsonomy.org/bibtex/291a3f6c5642640b44d9e487f625484e0/procomun},
interhash = {f907bed8f72930d8dbd74bd1d90c8dfb},
intrahash = {91a3f6c5642640b44d9e487f625484e0},
keywords = {Density GARCH Power, Wind ensemble forecasting, models, predictions, speed weather},
owner = {oscar},
refid = {Taylor.McSharry2007},
timestamp = {2011-09-02T08:25:25.000+0200},
title = {Wind Power Density Forecasting Using Ensemble Predictions and Time
Series Models},
year = 2007
}