A multi-objective wind speed and wind power prediction interval forecasting using variational modes decomposition based Multi-kernel robust ridge regression-Reference-Cited by-同舟云学术

A multi-objective wind speed and wind power prediction interval forecasting using variational modes decomposition based Multi-kernel robust ridge regression

Published:2019-06 Issue: Volume:136 Page:701-731
ISSN:0960-1481
Container-title:Renewable Energy
language:en
Short-container-title:Renewable Energy

Author:

Naik Jyotirmayee,Dash Pradipta Kishore,Dhar Snehamoy

Publisher

Elsevier BV

Subject

Renewable Energy, Sustainability and the Environment

Reference52 articles.

1. An experimental investigation of FNN model for wind speed forecasting using EEMD and CS;Wang;Math. Probl. Eng.,2015

2. Current methods and advances in forecasting of wind power generation;Foley;Renew. Energy,2012

3. An advanced statistical method for wind power forecasting;Sideratos;IEEE Trans. Power Syst.,2007

4. From probabilistic forecasts to statistical scenarios of short-term wind power production;Pinson;Wind Energy,2009

5. Prediction intervals to account for uncertainties in travel time prediction;Khosravi;IEEE Trans. Intell. Transport. Syst.,2011

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