Deep non-crossing probabilistic wind speed forecasting with multi-scale features-Reference-Cited by-同舟云学术

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Deep non-crossing probabilistic wind speed forecasting with multi-scale features

Published:2022-04 Issue: Volume:257 Page:115433
ISSN:0196-8904
Container-title:Energy Conversion and Management
language:en
Short-container-title:Energy Conversion and Management

Author:

Zou Runmin,Song Mengmeng,Wang Yun,Wang Ji,Yang Kaifeng,Affenzeller Michael

Publisher

Elsevier BV

Subject

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

Reference102 articles.

1. Multifactor spatio-temporal correlation model based on a combination of convolutional neural network and long short-term memory neural network for wind speed forecasting;Chen;Energy Convers Manage,2019

2. Estimation of fractal representation of wind speed fluctuation by artificial neural network with different training algorithms;Petković;Flow Meas Instrum,2017

3. Short-term wind speed forecasting using recurrent neural networks with error correction;Duan;Energy,2020

4. GWEC. Global wind report 2021. report, global wind energy council; 2021.

5. A new hybrid ensemble deep reinforcement learning model for wind speed short term forecasting;Liu;Energy,2020

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1. A double‐layer neural network wind speed prediction framework based on training set segmentation and error correction;IET Renewable Power Generation;2024-01-23

2. A new perspective of wind speed forecasting: Multi-objective and model selection-based ensemble interval-valued wind speed forecasting system;Energy Conversion and Management;2024-01

3. Attention mechanism is useful in spatio-temporal wind speed prediction: Evidence from China;Applied Soft Computing;2023-11

4. Ultra-short-term wind speed prediction based on deep spatial-temporal residual network;Journal of Renewable and Sustainable Energy;2023-07-01

5. A novel data-driven deep learning approach for wind turbine power curve modeling;Energy;2023-05

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