Short-term wind power output prediction using hybrid-enhanced seagull optimization algorithm and support vector machine: A high-precision method-Reference-Cited by-同舟云学术

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Short-term wind power output prediction using hybrid-enhanced seagull optimization algorithm and support vector machine: A high-precision method

Published:2024-03-27 Issue:12 Volume:21 Page:2858-2871
ISSN:1543-5075
Container-title:International Journal of Green Energy
language:en
Short-container-title:International Journal of Green Energy

Author:

Liu Yuwei¹²,Li Lingling¹²,Liu Jiaqi¹²

Affiliation:

1. State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology, Tianjin, China

2. Key Laboratory of Electromagnetic Field and Electrical Apparatus Reliability of Hebei Province, Hebei University of Technology, Tianjin, China

Funder

there is no funding associated

Publisher

Informa UK Limited

Link

https://www.tandfonline.com/doi/pdf/10.1080/15435075.2024.2334498

Reference47 articles.

1. An improved seagull optimization algorithm for optimal coordination of distance and directional over-current relays

2. Wind turbine power output very short-term forecast: A comparative study of data clustering techniques in a PSO-ANFIS model

3. A survey on control issues in renewable energy integration and microgrid

4. Energy sustainability evolution in the Mediterranean countries and synergies from a global energy scenario for the area

5. A whale optimization algorithm with chaos mechanism based on quasi-opposition for global optimization problems

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. A Wind Power Combination Forecasting Method Based on GASF Image Representation and UniFormer;Journal of Marine Science and Engineering;2024-07-13

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