Dynamic Data Reconciliation for Improving the Forecasting Performance of Wind Power based on Long Short-term Memory-Reference-Cited by-同舟云学术

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Dynamic Data Reconciliation for Improving the Forecasting Performance of Wind Power based on Long Short-term Memory

Published:2023-11-17 Issue: Volume: Page:1559-1563
ISSN:
Container-title:2023 China Automation Congress (CAC)
language:
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Author:

Zhu Wangwang¹,Zhang Ying²,Zhou Weihua²,Zhang Zhengjiang³,Liu Yi¹

Affiliation:

1. Institute of Process Equipment and Control Engineering Zhejiang University of Technology,Hangzhou,China,310023

2. Taizhou Vocational and Technical College,Department of Mechanical and Electrical Engineering,Taizhou,China,318000

3. Wenzhou University,National-Local Joint Engineering Laboratory for Digitalize Electrical Design Technology,Wenzhou,China,325035

Funder

National Natural Science Foundation of China

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10450150/10449968/10450856.pdf?arnumber=10450856

Reference19 articles.

1. A gated recurrent unit neural networks based wind speed error correction model for short-term wind power forecasting

2. Data-driven Soft Sensors in the process industry

3. Prediction of Pollutant Emissions of Biomass Flames Through Digital Imaging, Contourlet Transform, and Support Vector Regression Modeling

4. Mixture semisupervised principal component regression model and soft sensor application

5. Artificial neural networks for water quality soft-sensing in wastewater treatment: a review

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