Comparison of Machine Learning Models for Wind Power Forecasting
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Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-99-3315-0_44
Reference14 articles.
1. Lawal A, Rehman S, Alhems LM, Alam MM (2021) Wind speed prediction using hybrid 1D CNN and BLSTM network. IEEE Access 9:156672–156679. https://doi.org/10.1109/ACCESS.2021
2. Shi Z, Liang H, Dinavahi V (2018) Direct interval forecast of uncertain wind power based on recurrent neural networks. IEEE Trans Sustain Energy 9(3):1177–1187
3. Abdelhameed EH, Ahmed Hassan H (2018) Adaptive maximum power tracking control technique for wind energy conversion systems. In: 2018 Twentieth international middle east power systems conference (MEPCON), 2018
4. Mogos AS, Salauddin M, Liang X, Chung CY (2022) An effective very short-term wind speed prediction approach using multiple regression models. IEEE Canadian J Electr Comp Eng 45(3):242–253 (Summer 2022)
5. Peng X, Deng D, Wen J, Xiong L, Feng S, Wang B (2016) Very short term wind power forecasting approach based on numerical weather prediction and error correction method. In: China ınternational conference on electricity distribution (CICED), pp 1–4
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