Deep Learning Approach for Wind Power Forecasting
Author:
Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-19-2004-2_32
Reference19 articles.
1. Saroha S, Aggrawal SK (2015) A review and evaluation of current wind power prediction technologies. WSEAS Trans Power Syst
2. Colak I, Sagiroglu S, Yesilbudak M (2012) Data mining and wind power prediction: a literature review. Renew Energy 46:241–247
3. Chang W-Y (2014) A literature review of wind forecasting methods. J Power Energy Eng, 2:161–168. https://doi.org/10.4236/jpee.2014.24023
4. Chen K, Yu J (2014) Short-term wind speed prediction using an unscented Kalman filter based state-space support vector regression approach. Appl Energy 113:690–705
5. Catalao JPS, Pousinho HMI, Mendes VMF (2015) Hybrid wavelet-PSO-ANFIS approach for short-term wind power forecasting in Portugal. IEEE Trans Sustain Energy 2(1):50–59
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