Wind Power Forecasting Enhancement Utilizing Adaptive Quantile Function and CNN-LSTM: A Probabilistic Approach-Reference-Cited by-同舟云学术

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Wind Power Forecasting Enhancement Utilizing Adaptive Quantile Function and CNN-LSTM: A Probabilistic Approach

Published:2024-05 Issue:3 Volume:60 Page:4446-4457
ISSN:0093-9994
Container-title:IEEE Transactions on Industry Applications
language:
Short-container-title:IEEE Trans. on Ind. Applicat.

Author:

Abedinia Oveis¹^ORCID,Ghasemi-Marzbali Ali²^ORCID,Shafiei Mohammad²,Sobhani Behrooz³^ORCID,Gharehpetian Gevork B.⁴^ORCID,Bagheri Mehdi¹^ORCID

Affiliation:

1. Department of Electrical and Computer Engineering, School of Engineering and Digital Sciences, Nazarbayev University, Astana, Kazakhstan

2. Department of Electrical and Biomedical Engineering, Mazandaran University of Science and Technology, Babol, Iran

3. Department of Electrical Engineering, School of Engineering, University of Mohaghegh Ardabili, Ardabil, Iran

4. Department of Electrical Engineering, Amirkabir University of Technology, Tehran, Iran

Funder

Faculty Development Competitive Research Grant, Nazarbayev University

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/28/10535351/10400927.pdf?arnumber=10400927

Reference25 articles.

1. A multi-level model for hybrid short term wind forecasting based on SVM, wavelet transform and feature selection

2. Ultra-short-term wind power forecasting based on deep Bayesian model with uncertainty

3. Probabilistic Forecast for Multiple Wind Farms Based on Regular Vine Copulas

4. A review of wind speed and wind power forecasting with deep neural networks

5. A Multi-Model Combination Approach for Probabilistic Wind Power Forecasting

Cited by 1 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. Ultra-Short-Term Wind Power Prediction Based on the ZS-DT-PatchTST Combined Model;Energies;2024-08-29

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