One‐day ahead wind speed/power prediction based on polynomial autoregressive model
Author:
Affiliation:
1. Department of Electrical‐Electronics EngineeringIzmir Institute of TechnologyİzmirTurkey
2. Institute of Science and Technology of Information ‘A. Faedo’CNR, G. Moruzzi 156124PisaItaly
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.2016.0972
Reference39 articles.
1. Recursive wind speed forecasting based on Hammerstein auto‐regressive model;Maatallah O.;Appl. Energy,2015
2. Data mining and wind power prediction: A literature review
3. Improving supervised wind power forecasting models using extended numerical weather variables and unlabelled data;Fang S.;IET Renew. Power Gener.,2017
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