Short-term wind power forecasting based on support vector machine with improved dragonfly algorithm-Reference-Cited by-同舟云学术

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Short-term wind power forecasting based on support vector machine with improved dragonfly algorithm

Published:2020-01 Issue: Volume:242 Page:118447
ISSN:0959-6526
Container-title:Journal of Cleaner Production
language:en
Short-container-title:Journal of Cleaner Production

Author:

Li Ling-Ling,Zhao Xue,Tseng Ming-Lang,Tan Raymond R.

Funder

Natural Science Foundation of Hebei Province of China

key project of Tianjin Natural Science Foundation

Publisher

Elsevier BV

Subject

Industrial and Manufacturing Engineering,Strategy and Management,General Environmental Science,Renewable Energy, Sustainability and the Environment

Reference54 articles.

1. A new intelligent method based on combination of VMD and ELM for short term wind power forecasting;Abdoos;Neurocomputing,2016

2. Optimal offering and bidding strategies of renewable energy based large consumer using a novel hybrid robust-stochastic approach;Abedinia;J. Clean. Prod.,2019

3. Power system voltage stability assessment using a hybrid approach combining dragonfly optimization algorithm and support vector regression;Amroune;Arabian J. Sci. Eng.,2018

4. Risk-assessment of photovoltaic-wind-battery-grid based large industrial consumer using information gap decision theory;Bagal;Sol. Energy,2018

5. An advanced bayesian method for short-term probabilistic forecasting of the generation of wind power;Bracale;Energies,2015

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