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OpenAlex is a bibliographic catalogue of scientific papers, authors and institutions accessible in open access mode, named after the Library of Alexandria. It's citation coverage is excellent and I hope you will find utility in this listing of citing articles!

If you click the article title, you'll navigate to the article, as listed in CrossRef. If you click the Open Access links, you'll navigate to the "best Open Access location". Clicking the citation count will open this listing for that article. Lastly at the bottom of the page, you'll find basic pagination options.

Requested Article:

A multi-objective wind speed and wind power prediction interval forecasting using variational modes decomposition based Multi-kernel robust ridge regression
Jyotirmayee Naik, P.K. Dash, Snehamoy Dhar
Renewable Energy (2019) Vol. 136, pp. 701-731
Closed Access | Times Cited: 120

Showing 1-25 of 120 citing articles:

A review of deep learning for renewable energy forecasting
Huaizhi Wang, Zhenxing Lei, Xian Zhang, et al.
Energy Conversion and Management (2019) Vol. 198, pp. 111799-111799
Closed Access | Times Cited: 875
Multi-step wind speed forecasting based on hybrid multi-stage decomposition model and long short-term memory neural network
Sinvaldo Rodrigues Moreno, Ramon Gomes da Silva, Viviana Cocco Mariani, et al.
Energy Conversion and Management (2020) Vol. 213, pp. 112869-112869
Closed Access | Times Cited: 189
A review on multi-objective optimization framework in wind energy forecasting techniques and applications
Hui Liu, Ye Li, Zhu Duan, et al.
Energy Conversion and Management (2020) Vol. 224, pp. 113324-113324
Closed Access | Times Cited: 159
A novel decomposition-ensemble learning framework for multi-step ahead wind energy forecasting
Ramon Gomes da Silva, Matheus Henrique Dal Molin Ribeiro, Sinvaldo Rodrigues Moreno, et al.
Energy (2020) Vol. 216, pp. 119174-119174
Closed Access | Times Cited: 155
Review of meta-heuristic algorithms for wind power prediction: Methodologies, applications and challenges
Peng Lu, Lin Ye, Yongning Zhao, et al.
Applied Energy (2021) Vol. 301, pp. 117446-117446
Closed Access | Times Cited: 134
Efficient bootstrap stacking ensemble learning model applied to wind power generation forecasting
Matheus Henrique Dal Molin Ribeiro, Ramon Gomes da Silva, Sinvaldo Rodrigues Moreno, et al.
International Journal of Electrical Power & Energy Systems (2021) Vol. 136, pp. 107712-107712
Closed Access | Times Cited: 123
Interval forecasting for urban water demand using PSO optimized KDE distribution and LSTM neural networks
Baigang Du, Shuo Huang, Jun Guo, et al.
Applied Soft Computing (2022) Vol. 122, pp. 108875-108875
Closed Access | Times Cited: 99
Deep learning combined wind speed forecasting with hybrid time series decomposition and multi-objective parameter optimization
Sheng-Xiang Lv, Lin Wang
Applied Energy (2022) Vol. 311, pp. 118674-118674
Closed Access | Times Cited: 87
A convolutional Transformer-based truncated Gaussian density network with data denoising for wind speed forecasting
Yun Wang, Houhua Xu, Mengmeng Song, et al.
Applied Energy (2023) Vol. 333, pp. 120601-120601
Closed Access | Times Cited: 62
Hybrid forecasting model for non-stationary daily runoff series: A case study in the Han River Basin, China
Tuo Xie, Gang Zhang, Jinwang Hou, et al.
Journal of Hydrology (2019) Vol. 577, pp. 123915-123915
Closed Access | Times Cited: 131
Current advances and approaches in wind speed and wind power forecasting for improved renewable energy integration: A review
Santhosh Madasthu, Chintham Venkaiah, D. M. Vinod Kumar
Engineering Reports (2020) Vol. 2, Iss. 6
Open Access | Times Cited: 123
Electrical load forecasting: A deep learning approach based on K-nearest neighbors
Yunxuan Dong, Xuejiao Ma, Tonglin Fu
Applied Soft Computing (2020) Vol. 99, pp. 106900-106900
Closed Access | Times Cited: 111
A cascaded deep learning wind power prediction approach based on a two-layer of mode decomposition
Hao Yin, Zuhong Ou, Shengquan Huang, et al.
Energy (2019) Vol. 189, pp. 116316-116316
Closed Access | Times Cited: 90
A novel composite neural network based method for wind and solar power forecasting in microgrids
Azim Heydari, Davide Astiaso Garcia, Farshid Keynia, et al.
Applied Energy (2019) Vol. 251, pp. 113353-113353
Closed Access | Times Cited: 87
Modes decomposition forecasting approach for ultra-short-term wind speed
Zhongda Tian
Applied Soft Computing (2021) Vol. 105, pp. 107303-107303
Closed Access | Times Cited: 78
Hybrid multi-stage decomposition with parametric model applied to wind speed forecasting in Brazilian Northeast
Sinvaldo Rodrigues Moreno, Viviana Cocco Mariani, Leandro dos Santos Coelho
Renewable Energy (2020) Vol. 164, pp. 1508-1526
Closed Access | Times Cited: 74
Short-term wind speed interval prediction based on artificial intelligence methods and error probability distribution
Yagang Zhang, Guifang Pan, Yunpeng Zhao, et al.
Energy Conversion and Management (2020) Vol. 224, pp. 113346-113346
Closed Access | Times Cited: 73
Conformalized temporal convolutional quantile regression networks for wind power interval forecasting
Jianming Hu, Qingxi Luo, Jingwei Tang, et al.
Energy (2022) Vol. 248, pp. 123497-123497
Closed Access | Times Cited: 47
Short term wind energy prediction model based on data decomposition and optimized LSSVM
Yagang Zhang, Ruixuan Li
Sustainable Energy Technologies and Assessments (2022) Vol. 52, pp. 102025-102025
Closed Access | Times Cited: 41
A hybrid decomposition and Machine learning model for forecasting Chlorophyll-a and total nitrogen concentration in coastal waters
Xiaotong Zhu, Hongwei Guo, Jinhui Jeanne Huang‬‬‬‬, et al.
Journal of Hydrology (2023) Vol. 619, pp. 129207-129207
Closed Access | Times Cited: 36
Renewables integration into power systems through intelligent techniques: Implementation procedures, key features, and performance evaluation
Sayemul Islam, Naruttam Kumar Roy
Energy Reports (2023) Vol. 9, pp. 6063-6087
Open Access | Times Cited: 27
Wind power forecasting: A hybrid forecasting model and multi-task learning-based framework
Yugui Tang, Kuo Yang, Shujing Zhang, et al.
Energy (2023) Vol. 278, pp. 127864-127864
Closed Access | Times Cited: 26
A multi-step ahead point-interval forecasting system for hourly PM2.5 concentrations based on multivariate decomposition and kernel density estimation
Hongtao Li, Yang Yu, Zhipeng Huang, et al.
Expert Systems with Applications (2023) Vol. 226, pp. 120140-120140
Closed Access | Times Cited: 24
Combined electricity load-forecasting system based on weighted fuzzy time series and deep neural networks
Zhining Cao, Jianzhou Wang, Yurui Xia
Engineering Applications of Artificial Intelligence (2024) Vol. 132, pp. 108375-108375
Closed Access | Times Cited: 10
An attention-based multi-input LSTM with sliding window-based two-stage decomposition for wind speed forecasting
Dongchuan Yang, Mingzhu Li, Ju’e Guo, et al.
Applied Energy (2024) Vol. 375, pp. 124057-124057
Closed Access | Times Cited: 9
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