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Wind speed and power ultra short-term robust forecasting based on Takagi–Sugeno fuzzy model

Liu F., Li R., Dreglea A. Wind speed and power ultra short-term robust forecasting based on Takagi–Sugeno fuzzy model // Energies. Vol.12. No.18. ID: 3551. 2019. DOI: 10.3390/en12183551 Accurate wind power and wind speed forecasting remains a critical challenge in wind power systems management. This paper proposes an ultra short-time forecasting method based on the Takagi–Sugeno (T–S) fuzzy model for wind power and wind speed. The model does not rely on a large amount of historical data and can...

Теги: linearization , machine learning , wind power: wind speed: t–s fuzzy model: forecasting , backpropagation , clustering algorithms , fuzzy clustering , learning systems , least squares approximations , neural networks , support vector machines , wind , wind power , b
Раздел: ИСЭМ СО РАН
Short-term wind power forecasting based on T-S fuzzy model

Liu F., Li R., Li Y. et al. Short-term wind power forecasting based on T-S fuzzy model // Asia-Pacific Power and Energy Engineering Conference, APPEEC. Vol.Decem. 2016. P.414-418. ISBN 9781509054183. DOI: 10.1109/APPEEC.2016.7779537. Due to the impacts of wind speed, wind direction, temperature and pressure, it is uncertain and nonlinear for the wind power forecasting. To address these problems, this paper proposes a wind power short-time forecasting method based on the T-S fuzzy model, which does...

Теги: fuzzy c-means (fcm) , recursive least squares method (rls) , t-s fuzzy model , wind power forecasting , clustering algorithms , forecasting , fuzzy systems , least squares approximations , signal processing , support vector machines , wind , wind effects , wind power
Раздел: ИСЭМ СО РАН


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