Abstract
This chapter presents a comprehensive overview of short term wind forecasting models based on time series analysis. Several different approaches, presently considered as mature, are re-examined with an eye towards setting automated procedures to clarify grey areas in their application. Additionally, some approaches recently proposed in the literature are examined that include the application of localized linear models, and clustering algorithms coupled with linear and nonlinear models. Additionally, the impact of changing synoptic weather characteristics is captured, through the utilization of global meteorological variables and the subsequent development of a customized regime model. The application of the developed approach on an annual hourly wind speed data set is presented.
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Sfetsos, A. (2011). A Comprehensive Overview of Short Term Wind Forecasting Models Based on Time Series Analysis. In: Gopalakrishnan, K., Khaitan, S.K., Kalogirou, S. (eds) Soft Computing in Green and Renewable Energy Systems. Studies in Fuzziness and Soft Computing, vol 269. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-22176-7_4
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DOI: https://doi.org/10.1007/978-3-642-22176-7_4
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