Open Access Helper

OpenAlex Citation Counts

OpenAlex Citations Logo

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:

Prediction interval forecasting of wind speed and wind power using modes decomposition based low rank multi-kernel ridge regression
Jyotirmayee Naik, Ranjeeta Bisoi, P.K. Dash
Renewable Energy (2018) Vol. 129, pp. 357-383
Closed Access | Times Cited: 101

Showing 1-25 of 101 citing articles:

Mid-to-long term wind and photovoltaic power generation prediction based on copula function and long short term memory network
Shuang Han, Yanhui Qiao, Jie Yan, et al.
Applied Energy (2019) Vol. 239, pp. 181-191
Closed Access | Times Cited: 229
A novel hybrid model based on VMD-WT and PCA-BP-RBF neural network for short-term wind speed forecasting
Yagang Zhang, Bing Chen, Guifang Pan, et al.
Energy Conversion and Management (2019) Vol. 195, pp. 180-197
Closed Access | Times Cited: 222
Stacked LSTM Sequence-to-Sequence Autoencoder with Feature Selection for Daily Solar Radiation Prediction: A Review and New Modeling Results
Sujan Ghimire, Ravinesh C. Deo, Hua Wang, et al.
Energies (2022) Vol. 15, Iss. 3, pp. 1061-1061
Open Access | Times Cited: 76
A wind speed interval prediction system based on multi-objective optimization for machine learning method
Ranran Li, Yu Jin
Applied Energy (2018) Vol. 228, pp. 2207-2220
Closed Access | Times Cited: 149
Deep Learning Method Based on Gated Recurrent Unit and Variational Mode Decomposition for Short-Term Wind Power Interval Prediction
Ruoheng Wang, Chaoshun Li, Wenlong Fu, et al.
IEEE Transactions on Neural Networks and Learning Systems (2019) Vol. 31, Iss. 10, pp. 3814-3827
Closed Access | Times Cited: 144
Multi‐step wind power forecast based on VMD‐LSTM
Li Han, Rongchang Zhang, Xuesong Wang, et al.
IET Renewable Power Generation (2019) Vol. 13, Iss. 10, pp. 1690-1700
Closed Access | Times Cited: 137
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
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
Smart wind speed forecasting approach using various boosting algorithms, big multi-step forecasting strategy
Yanfei Li, Huipeng Shi, Feng-ze Han, et al.
Renewable Energy (2018) Vol. 135, pp. 540-553
Closed Access | Times Cited: 106
Deterministic and probabilistic multi-step forecasting for short-term wind speed based on secondary decomposition and a deep learning method
Ling Xiang, Jingxu Li, Aijun Hu, et al.
Energy Conversion and Management (2020) Vol. 220, pp. 113098-113098
Closed Access | Times Cited: 102
The short-term interval prediction of wind power using the deep learning model with gradient descend optimization
Chaoshun Li, Geng Tang, Xiaoming Xue, et al.
Renewable Energy (2020) Vol. 155, pp. 197-211
Closed Access | Times Cited: 96
Modes decomposition method in fusion with robust random vector functional link network for crude oil price forecasting
Ranjeeta Bisoi, P.K. Dash, Sthita Prajna Mishra
Applied Soft Computing (2019) Vol. 80, pp. 475-493
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
Machine learning and data-driven fault detection for ship systems operations
Michail Cheliotis, Iraklis Lazakis, Γεράσιμος Θεοτοκάτος
Ocean Engineering (2020) Vol. 216, pp. 107968-107968
Open Access | Times Cited: 87
A Novel Wind Speed Interval Prediction Based on Error Prediction Method
Geng Tang, Yifan Wu, Chaoshun Li, et al.
IEEE Transactions on Industrial Informatics (2020) Vol. 16, Iss. 11, pp. 6806-6815
Closed Access | Times Cited: 79
Medium-term wind power forecasting based on multi-resolution multi-learner ensemble and adaptive model selection
Chao Chen, Hui Liu
Energy Conversion and Management (2020) Vol. 206, pp. 112492-112492
Closed Access | Times Cited: 72
Natural phase space reconstruction-based broad learning system for short-term wind speed prediction: Case studies of an offshore wind farm
Xuefang Xu, Shiting Hu, Peiming Shi, et al.
Energy (2022) Vol. 262, pp. 125342-125342
Closed Access | Times Cited: 58
An algorithm for forecasting day-ahead wind power via novel long short-term memory and wind power ramp events
Yang Cui, Zhenghong Chen, Yingjie He, et al.
Energy (2022) Vol. 263, pp. 125888-125888
Closed Access | Times Cited: 50
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: 44
An overview of deterministic and probabilistic forecasting methods of wind energy
Yuying Xie, Chaoshun Li, Mengying Li, et al.
iScience (2022) Vol. 26, Iss. 1, pp. 105804-105804
Open Access | Times Cited: 42
Daily scale air quality index forecasting using bidirectional recurrent neural networks: Case study of Delhi, India
Chaitanya B. Pande, Nand Lal Kushwaha, Omer A. Alawi, et al.
Environmental Pollution (2024) Vol. 351, pp. 124040-124040
Closed Access | Times Cited: 9
A novel combined model based on echo state network for multi-step ahead wind speed forecasting: A case study of NREL
Yanhua Chen, Zhaoshuang He, Zhihao Shang, et al.
Energy Conversion and Management (2018) Vol. 179, pp. 13-29
Closed Access | Times Cited: 74
Chaotic wind power time series prediction via switching data-driven modes
Tinghui Ouyang, Heming Huang, Yusen He, et al.
Renewable Energy (2019) Vol. 145, pp. 270-281
Closed Access | Times Cited: 70
Forecasting Short-Term Wind Speed Based on IEWT-LSSVM Model Optimized by Bird Swarm Algorithm
Ling Xiang, Zeqi Deng, Aijun Hu
IEEE Access (2019) Vol. 7, pp. 59333-59345
Open Access | Times Cited: 62
Machine Learning (ML)‐Assisted Design and Fabrication for Solar Cells
Fan Li, Xiaoqi Peng, Wang Zuo, et al.
Energy & environment materials (2019) Vol. 2, Iss. 4, pp. 280-291
Open Access | Times Cited: 62
Page 1 - Next Page

Scroll to top