Short-term wind speed forecasting using deep reinforcement learning with improved multiple error correction approach-Reference-Cited by-同舟云学术

Short-term wind speed forecasting using deep reinforcement learning with improved multiple error correction approach

Published:2022-01 Issue: Volume:239 Page:122128
ISSN:0360-5442
Container-title:Energy
language:en
Short-container-title:Energy

Author:

Yang Rui,Liu Hui,Nikitas Nikolaos^ORCID,Duan Zhu,Li Yanfei,Li Ye

Funder

National Natural Science Foundation of China

Publisher

Elsevier BV

Subject

General Energy,Pollution,Mechanical Engineering,Building and Construction,Electrical and Electronic Engineering,Industrial and Manufacturing Engineering,Civil and Structural Engineering

Reference64 articles.

1. Wind ENERGY in Russia: the current state and development trends;Kudelin;Energy Strategy Rev,2021

2. Assessing the wind energy potential of China in considering its variability/intermittency;Gao;Energy Convers Manag,2020

3. Technical challenges associated with the integration of wind power into power systems;Georgilakis;Renew Sustain Energy Rev,2008

4. Global wind: 2019 Report;GWEC,2019

5. A hybrid neural network model for short-term wind speed forecasting based on decomposition, multi-learner ensemble, and adaptive multiple error corrections;Liu;Renew Energy,2021

Cited by 38 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. Green Finance and Industrial Low-Carbon Transition: A Case Study on Green Economy Policy in Kazakhstan;Sustainability;2024-09-05

2. Two-stage correction prediction of wind power based on numerical weather prediction wind speed superposition correction and improved clustering;Energy;2024-09

3. A novel dynamic ensemble of numerical weather prediction for multi-step wind speed forecasting with deep reinforcement learning and error sequence modeling;Energy;2024-09

4. Aprendizaje por refuerzo como soporte a la predicción de la precipitación mensual. Caso de estudio: Departamento de Boyacá - Colombia;TecnoLógicas;2024-06-27

5. A Heat Load Prediction Method for District Heating Systems Based on the AE-GWO-GRU Model;Applied Sciences;2024-06-23