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Forecasting volatility of wind power production

Zhiwei Shen and Matthias Ritter

Applied Energy, 2016, vol. 176, issue C, pages 295-308

Abstract: Given the increasing share of wind energy in the portfolio of energy sources, there is the need for a more thorough understanding of its uncertainties due to changing weather conditions. To account for the uncertainty in predicting wind power production, this article examines the volatility forecasting abilities of different GARCH-type models for wind power production. Moreover, due to characteristic features of the wind power process, such as heteroscedasticity and nonlinearity, we also investigate the use of a Markov regime-switching GARCH (MRS-GARCH) model on forecasting volatility of wind power. Realized volatility, which is derived from lower-scale data, serves as a benchmark for latent volatility. We find that the MRS-GARCH model significantly outperforms traditional GARCH models in predicting the volatility of wind power, while the exponential GARCH model is superior among traditional GARCH models.

Keywords: Wind energy; Volatility forecasting; GARCH models; Markov regime-switching; Realized volatility (search for similar items in EconPapers)
JEL-codes: C22 Q42 Q47 (search for similar items in EconPapers)
Date: 2016
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