Nonparametric Probabilistic Forecasting for Wind Power Generation Using Quadratic Spline Quantile Function and Autoregressive Recurrent Neural Network-Reference-Cited by-同舟云学术

Nonparametric Probabilistic Forecasting for Wind Power Generation Using Quadratic Spline Quantile Function and Autoregressive Recurrent Neural Network

Published:2022-10 Issue:4 Volume:13 Page:1930-1943
ISSN:1949-3029
Container-title:IEEE Transactions on Sustainable Energy
language:
Short-container-title:IEEE Trans. Sustain. Energy

Author:

Wang Ke¹,Zhang Yao¹^ORCID,Lin Fan¹,Wang Jianxue¹^ORCID,Zhu Morun¹

Affiliation:

1. State Key Laboratory of Electrical Insulation and Power Equipment, School of Electrical Engineering, Xi'an Jiaotong University, Xi'an, China

Funder

National Natural Science Foundation of China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Renewable Energy, Sustainability and the Environment

Link

http://xplorestaging.ieee.org/ielx7/5165391/9894114/09779444.pdf?arnumber=9779444

Reference45 articles.

1. Convergence rate of b-spline estimators of nonparametric conditional quantile functions∗

2. A new hybrid model for point and probabilistic forecasting of wind power

3. An Advanced Approach for Construction of Optimal Wind Power Prediction Intervals

4. Leveraging Turbine-Level Data for Improved Probabilistic Wind Power Forecasting

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