Wind speed forecasting based on second order blind identification and autoregressive model

Wind power presents undesirable intermittencies due to the considerable variations in the wind speed which may affect adversely the smooth operation of the grid. Effective wind forecast is essential in order to report the amount of energy supply with high accuracy, which is crucial for planning ener...

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Bibliographic Details
Published in2011 IEEE 19th Signal Processing and Communications Applications Conference (SIU) pp. 618 - 621
Main Authors Firat, U., Engin, S. N., Saraclar, M., Ertuzun, A. B.
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.04.2011
Subjects
Conferences
Independent component analysis
Predictive models
Signal processing
Wind forecasting
Wind power generation
Wind speed
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Summary:Wind power presents undesirable intermittencies due to the considerable variations in the wind speed which may affect adversely the smooth operation of the grid. Effective wind forecast is essential in order to report the amount of energy supply with high accuracy, which is crucial for planning energy resources for power system operators. In this paper a new statistical method is presented based on independent component analysis (ICA) and autoregressive (AR) model. ICA is utilized in order to exploit the hidden factors which may exist in the wind speed time-series. ICA methods based on exploiting the time structure like second order blind identification (SOBI) can be used as a preliminary step in wind speed forecasting.
ISBN:1457704625
9781457704628
ISSN:2165-0608
2693-3616
DOI:10.1109/SIU.2011.5929726