%0 Journal Article %1 Shen2016Forecasting %A Shen, Zhiwei %A Ritter, Matthias %D 2016 %J Applied Energy %K wind energy renewables nowcasting %P 295--308 %R 10.1016/j.apenergy.2016.05.071 %T Forecasting volatility of wind power production %U http://dx.doi.org/10.1016/j.apenergy.2016.05.071 %V 176 %X An increasing share of wind energy requires assessing the wind power volatility. Volatility forecasting with different GARCH models for wind power is examined. We introduce realized volatility of wind power as a benchmark for latent volatility. Markov regime switching GARCH model significantly outperforms classical GARCH models. 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.

@article{Shen2016Forecasting, abstract = { An increasing share of wind energy requires assessing the wind power volatility. Volatility forecasting with different GARCH models for wind power is examined. We introduce realized volatility of wind power as a benchmark for latent volatility. Markov regime switching GARCH model significantly outperforms classical GARCH models. 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.}, added-at = {2018-06-18T21:23:34.000+0200}, author = {Shen, Zhiwei and Ritter, Matthias}, biburl = {https://www.bibsonomy.org/bibtex/2e44b69a3d910890fc9a781ae6d9f5afb/pbett}, citeulike-article-id = {14062576}, citeulike-attachment-1 = {Shen_Ritter_2016.pdf; /pdf/user/pbett/article/14062576/1071740/Shen_Ritter_2016.pdf; 7726cdcb4dcc5fa9b4ffd35f3c87f09049e4bcb1}, citeulike-linkout-0 = {http://dx.doi.org/10.1016/j.apenergy.2016.05.071}, comment = {(private-note)Stat modelling, focusing on forecasting on timescales below a few hours.}, doi = {10.1016/j.apenergy.2016.05.071}, file = {Shen_Ritter_2016.pdf}, interhash = {2591df1ef3ba5db444e784ca2ba98ba7}, intrahash = {e44b69a3d910890fc9a781ae6d9f5afb}, issn = {03062619}, journal = {Applied Energy}, keywords = {wind energy renewables nowcasting}, month = aug, pages = {295--308}, posted-at = {2016-06-08 13:50:13}, priority = {2}, timestamp = {2018-06-22T18:36:26.000+0200}, title = {Forecasting volatility of wind power production}, url = {http://dx.doi.org/10.1016/j.apenergy.2016.05.071}, volume = 176, year = 2016 }