Wind Power Forecasting Method Considering Temporal-Spatial Feature Mining and Error Correcting-Reference-Cited by-同舟云学术

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Wind Power Forecasting Method Considering Temporal-Spatial Feature Mining and Error Correcting

Published:2023-12-08 Issue: Volume:43 Page:332-336
ISSN:
Container-title:2023 13th International Conference on Power and Energy Systems (ICPES)
language:
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Author:

Yuan Kai¹,Chen Yanhui²,Sun Chongbo¹,Hou Ruosong³,Song Yi¹,Yan Jingxian¹,Wan Zhiwei¹,An Jiakun³

Affiliation:

1. State Grid Economic and Technological Research Institute Co. Ltd.,Beijing,China

2. Zhejiang University,College of Electrical Engineering and Polytechnic Institute,Hangzhou,China

3. Economic and Technological Research Institute of State Grid Hebei Electric Power Co., Ltd,Shijiazhuang,China

Funder

State Grid Corporation of China

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10399944/10399945/10400164.pdf?arnumber=10400164

Reference14 articles.

1. Hybrid Forecasting Model for Very-Short Term Wind Power Forecasting Based on Grey Relational Analysis and Wind Speed Distribution Features

2. Comparative study among physical models and artificial intelligence methods for PV power forecasting

3. A data-driven multi-model methodology with deep feature selection for short-term wind forecasting

4. Spatio-Temporal Graph Deep Neural Network for Short-Term Wind Speed Forecasting

5. Wind Power Short-Term Forecasting Model Based on the Hierarchical Output Power and Poisson Re-Sampling Random Forest Algorithm

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