An Ultra-Short-Term and Short-Term Wind Power Forecasting Approach Based on Optimized Artificial Neural Network with Time Series Reconstruction

Lihan, Zha,; Jiang, Dongxiang

doi:10.1109/spies55999.2022.10082352

2022 4th International Conference on Smart Power &Amp; Internet Energy Systems (SPIES) 2022

DOI: 10.1109/spies55999.2022.10082352

|View full text |Cite

An Ultra-Short-Term and Short-Term Wind Power Forecasting Approach Based on Optimized Artificial Neural Network with Time Series Reconstruction

Zha, Lihan

Dongxiang Jiang

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2024

Publication Types

Select...

Article1

Relationship

Self Cite0

Independent1

Authors

Journals

Cited by 1 publication

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

A novel hybrid model based on multiple influencing factors and temporal convolutional network coupling ReOSELM for wind power prediction

Ge,

Zhang,

Wang

et al. 2024

Energy Conversion and Management

View full text Add to dashboard Cite

A novel hybrid model based on multiple influencing factors and temporal convolutional network coupling ReOSELM for wind power prediction

Ge,

Zhang,

Wang

et al. 2024

Energy Conversion and Management

View full text Add to dashboard Cite

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

hi@scite.ai

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

An Ultra-Short-Term and Short-Term Wind Power Forecasting Approach Based on Optimized Artificial Neural Network with Time Series Reconstruction

Cited by 1 publication

References 17 publications

A novel hybrid model based on multiple influencing factors and temporal convolutional network coupling ReOSELM for wind power prediction

A novel hybrid model based on multiple influencing factors and temporal convolutional network coupling ReOSELM for wind power prediction

Contact Info

Product

Resources

About