High-quality short-term forecasts of wind speed are vital to making wind power a
more reliable energy source. Gneiting et al. (2006) have introduced a model for the average
wind speed two hours ahead based on both spatial and temporal information. The
forecasts produced by this model are accurate, and subject to accuracy, the predictive distribution
is sharp, i.e., highly concentrated around its center. However, this model is split
into nonunique regimes based on the wind direction at an off-site location. This work both
generalizes and improves upon this model by treating wind direction as a circular variable
and including it in the model. It is robust in many experiments, such as predicting at new
locations. This is compared with the more common approach of modeling wind speeds and
directions in the Cartesian space and use a skew-t distribution for the errors. The quality
of the predictions from all of these models can be more realistically assessed with a loss
measure that depends upon the power curve relating wind speed to power output. This
proposed loss measure yields more insight into the true value of each model's predictions.
One method of evaluating time series forecasts, such as wind speed forecasts, is to
test the null hypothesis of no difference in the accuracy of two competing sets of forecasts. Diebold and Mariano (1995) proposed a test in this setting that has been extended and
widely applied. It allows the researcher to specify a wide variety of loss functions, and the
forecast errors can be non-Gaussian, nonzero mean, serially correlated, and contemporaneously
correlated. In this work, a similar unconditional test of forecast accuracy for spatial
data is proposed. The forecast errors are no longer potentially serially correlated but spatially
correlated. Simulations will illustrate the properties of this test, and an example with
daily average wind speeds measured at over 100 locations in Oklahoma will demonstrate
its use. This test is compared with a wavelet-based method introduced by Shen et al. (2002)
in which the presence of a spatial signal at each location in the dataset is tested.
| Identifer | oai:union.ndltd.org:tamu.edu/oai:repository.tamu.edu:1969.1/ETD-TAMU-2009-08-910 |
| Date | 2009 August 1900 |
| Creators | Hering, Amanda S. |
| Contributors | Genton, Marc G. |
| Source Sets | Texas A and M University |
| Language | en_US |
| Detected Language | English |
| Type | Book, Thesis, Electronic Dissertation, text |
| Format | application/pdf |
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