Ultra-short-term wind power probabilistic forecasting based on an evolutionary non-crossing multi-output quantile regression deep neural network-Reference-Cited by-同舟云学术

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Ultra-short-term wind power probabilistic forecasting based on an evolutionary non-crossing multi-output quantile regression deep neural network

Published:2024-02 Issue: Volume:301 Page:118062
ISSN:0196-8904
Container-title:Energy Conversion and Management
language:en
Short-container-title:Energy Conversion and Management

Author:

Zhu Jianhua,He Yaoyao^ORCID,Yang Xiaodong,Yang Shanlin

Funder

Science Fund for Distinguished Young Scholars of Anhui Province

National Natural Science Foundation of China

Anhui Provincial Natural Science Foundation

Major Science and Technology Projects in Anhui Province

Publisher

Elsevier BV

Subject

Energy Engineering and Power Technology,Fuel Technology,Nuclear Energy and Engineering,Renewable Energy, Sustainability and the Environment

Reference53 articles.

1. Wind power prediction using a three stage genetic ensemble and auxiliary predictor;Shahid;Appl Soft Comput,2020

2. Reliability impacts of the dynamic thermal rating and battery energy storage systems on wind-integrated power networks;Teh;Sustain Energy Grids Netw,2019

3. Global wind report 2022,2022

4. Short-term wind power prediction optimized by multi-objective dragonfly algorithm based on variational mode decomposition;Zhou;Chaos Solitons Fractals,2022

5. Integration of wind and demand response for optimum generation reliability, cost and carbon emission;Khoo;IEEE Access,2020

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