Short-Term Wind Power Prediction Based on DBSCAN Clustering and Support Vector Machine Regression-Reference-Cited by-同舟云学术

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Short-Term Wind Power Prediction Based on DBSCAN Clustering and Support Vector Machine Regression

Published:2020-05 Issue: Volume: Page:
ISSN:
Container-title:2020 5th International Conference on Computer and Communication Systems (ICCCS)
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Author:

Wang Siqi,Chen Chen

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9114888/9118408/09118606.pdf?arnumber=9118606

Cited by 6 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. Unveiling the backbone of the renewable energy forecasting process: Exploring direct and indirect methods and their applications;Energy Reports;2024-06

2. Extracting the Extreme Mode Operation of Coal Mills Using the Gaussian Mixture Model Method;2023 IEEE 3rd International Conference on Power, Electronics and Computer Applications (ICPECA);2023-01-29

3. Prediction method of wind farm power generation capacity based on feature clustering and correlation analysis;Electric Power Systems Research;2022-11

4. A Hybrid Algorithm for Short-Term Wind Power Prediction;Energies;2022-10-05

5. A Forecasting Model of Wind Power Based on IPSO–LSTM and Classified Fusion;Energies;2022-07-29

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