Short‐term wind power forecasting and uncertainty analysis using a hybrid intelligent method
Author:
Affiliation:
1. Department of Electrical EngineeringKun Shan UniversityTainan71070Taiwan
2. Department of Computer and Communication EngineeringKun Shan UniversityTainan71070Taiwan
3. Department of Electrical EngineeringCheng Shiu UniversityKaohsiung83347Taiwan
Publisher
Institution of Engineering and Technology (IET)
Subject
Renewable Energy, Sustainability and the Environment
Link
https://onlinelibrary.wiley.com/doi/pdf/10.1049/iet-rpg.2016.0672
Reference25 articles.
1. The state‐of‐the‐art in short term prediction of wind power: a literature overview ANEMOS Deliverable 1.1 2003 http://ecolo.org/documents/documents_in_english/wind‐predict‐ANEMOS.pdf
2. Short-Term Wind-Power Prediction Based on Wavelet Transform–Support Vector Machine and Statistic-Characteristics Analysis
3. Short-term wind power forecasting using ridgelet neural network
4. Hybrid intelligent approach for short-term wind power forecasting in Portugal
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