A Spliced Gamma-Generalized Pareto Model for Short-Term Extreme Wind Speed Probabilistic Forecasting
Type
ArticleKAUST Department
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) DivisionStatistics
Statistics Program
KAUST Grant Number
OSR-CRG2017-3434Date
2019-07-23Embargo End Date
2020-07-23Permanent link to this record
http://hdl.handle.net/10754/656160
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Show full item recordAbstract
Renewable sources of energy such as wind power have become a sustainable alternative to fossil fuel-based energy. However, the uncertainty and fluctuation of the wind speed derived from its intermittent nature bring a great threat to the wind power production stability, and to the wind turbines themselves. Lately, much work has been done on developing models to forecast average wind speed values, yet surprisingly little has focused on proposing models to accurately forecast extreme wind speeds, which can damage the turbines. In this work, we develop a flexible spliced Gamma-Generalized Pareto model to forecast extreme and non-extreme wind speeds simultaneously. Our model belongs to the class of latent Gaussian models, for which inference is conveniently performed based on the integrated nested Laplace approximation method. Considering a flexible additive regression structure, we propose two models for the latent linear predictor to capture the spatio-temporal dynamics of wind speeds. Our models are fast to fit and can describe both the bulk and the tail of the wind speed distribution while producing short-term extreme and non-extreme wind speed probabilistic forecasts. Supplementary materials accompanying this paper appear online.Citation
Castro-Camilo, D., Huser, R., & Rue, H. (2019). A Spliced Gamma-Generalized Pareto Model for Short-Term Extreme Wind Speed Probabilistic Forecasting. Journal of Agricultural, Biological and Environmental Statistics, 24(3), 517–534. doi:10.1007/s13253-019-00369-zSponsors
We thank Amanda Hering for helpful suggestions, and for providing the wind speed data. We also extend our thanks to Thomas Opitz for helpful discussion. Support from the KAUST Supercomputing Laboratory and access to Shaheen is also gratefully acknowledged. This publication is based upon work supported by KAUST Office of Sponsored Research (OSR) under Award No. OSR-CRG2017-3434.Publisher
Springer NatureAdditional Links
http://link.springer.com/10.1007/s13253-019-00369-zae974a485f413a2113503eed53cd6c53
10.1007/s13253-019-00369-z