Very short-term probabilistic wind power prediction using sparse machine learning and nonparametric density estimation algorithms
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DOI: 10.1016/j.renene.2021.05.123
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- Yukta Mehta & Rui Xu & Benjamin Lim & Jane Wu & Jerry Gao, 2023. "A Review for Green Energy Machine Learning and AI Services," Energies, MDPI, vol. 16(15), pages 1-30, July.
- Adam Krechowicz & Maria Krechowicz & Katarzyna Poczeta, 2022. "Machine Learning Approaches to Predict Electricity Production from Renewable Energy Sources," Energies, MDPI, vol. 15(23), pages 1-41, December.
- Ejigu Tefera Habtemariam & Kula Kekeba & María Martínez-Ballesteros & Francisco Martínez-Álvarez, 2023. "A Bayesian Optimization-Based LSTM Model for Wind Power Forecasting in the Adama District, Ethiopia," Energies, MDPI, vol. 16(5), pages 1-22, February.
- Wang, Jianzhou & Yu, Yue & Zeng, Bo & Lu, Haiyan, 2024. "Hybrid ultra-short-term PV power forecasting system for deterministic forecasting and uncertainty analysis," Energy, Elsevier, vol. 288(C).
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Keywords
Interval forecast; Multivariate estimation; Nonparametric density estimation; Sparse modeling; Wind power prediction;All these keywords.
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