A Hybrid Three-Staged, Short-Term Wind-Power Prediction Method Based on SDAE-SVR Deep Learning and BA Optimization-Reference-Cited by-同舟云学术

A Hybrid Three-Staged, Short-Term Wind-Power Prediction Method Based on SDAE-SVR Deep Learning and BA Optimization

Published:2022 Issue: Volume:10 Page:123595-123604
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Duan Ruiqin¹,Peng Xiaosheng²^ORCID,Li Cong²^ORCID,Yang Zimin²,Jiang Yan¹,Li Xiufeng¹,Liu Shuangquan¹

Affiliation:

1. System Operation Department, Yunnan Power Grid Corporation Ltd., Kunming, China

2. State Key Laboratory of Advanced Electromagnetic Engineering and Technology, School of Electrical and Electronic Engineering, Huazhong University of Science and Technology, Wuhan, China

Funder

Reserve Talents Program for Middle-Aged and Young Leaders of Disciplines in Science and Technology of Yunnan Province, China

Science and Technology Program of Yunnan Power Grid Corporation Ltd

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Engineering,General Materials Science,General Computer Science,Electrical and Electronic Engineering

Link

http://xplorestaging.ieee.org/ielx7/6287639/9668973/09955524.pdf?arnumber=9955524

Reference27 articles.

1. Ultra-Short-Term Prediction of Wind Power Based on Sample Similarity Analysis

2. Short-Term Residential Load Forecasting Based on LSTM Recurrent Neural Network

3. Reliability Assessment of Wind Power Converter Considering SCADA Multistate Parameters Prediction Using FP-Growth, WPT, K-Means and LSTM Network

4. A Novel Efficient DLUBE Model Constructed by Error Interval Coefficients for Clustered Wind Power Prediction

5. Research on Distributed wind Power Reactive Voltage Coordinated Control Strategy Connected to Distribution Network

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