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OpenAlex Citation Counts

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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 wavelet-coupled support vector machine model for forecasting global incident solar radiation using limited meteorological dataset
Ravinesh C. Deo, Xiaohu Wen, Qi Feng
Applied Energy (2016) Vol. 168, pp. 568-593
Closed Access | Times Cited: 330

Showing 1-25 of 330 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: 861
Deep learning based ensemble approach for probabilistic wind power forecasting
Huaizhi Wang, Gangqiang Li, Guibin Wang, et al.
Applied Energy (2016) Vol. 188, pp. 56-70
Closed Access | Times Cited: 641
Comparison of Support Vector Machine and Extreme Gradient Boosting for predicting daily global solar radiation using temperature and precipitation in humid subtropical climates: A case study in China
Junliang Fan, Xiukang Wang, Lifeng Wu, et al.
Energy Conversion and Management (2018) Vol. 164, pp. 102-111
Closed Access | Times Cited: 523
Application of support vector machine models for forecasting solar and wind energy resources: A review
Alireza Zendehboudi, M.A. Baseer, R. Saidur
Journal of Cleaner Production (2018) Vol. 199, pp. 272-285
Closed Access | Times Cited: 508
History and trends in solar irradiance and PV power forecasting: A preliminary assessment and review using text mining
Dazhi Yang, Jan Kleissl, Christian A. Gueymard, et al.
Solar Energy (2018) Vol. 168, pp. 60-101
Open Access | Times Cited: 435
Predicting compressive strength of lightweight foamed concrete using extreme learning machine model
Zaher Mundher Yaseen‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬, Ravinesh C. Deo, Ameer A. Hilal, et al.
Advances in Engineering Software (2017) Vol. 115, pp. 112-125
Closed Access | Times Cited: 361
Hybrid CNN-LSTM Model for Short-Term Individual Household Load Forecasting
Musaed Alhussein, Khursheed Aurangzeb, Syed Irtaza Haider
IEEE Access (2020) Vol. 8, pp. 180544-180557
Open Access | Times Cited: 353
A High Precision Artificial Neural Networks Model for Short-Term Energy Load Forecasting
Ping‐Huan Kuo, Chiou‐Jye Huang
Energies (2018) Vol. 11, Iss. 1, pp. 213-213
Open Access | Times Cited: 300
Prediction of daily global solar radiation using different machine learning algorithms: Evaluation and comparison
Ümit Ağbulut, Ali Etem Gürel, Yunus Biçen
Renewable and Sustainable Energy Reviews (2020) Vol. 135, pp. 110114-110114
Closed Access | Times Cited: 295
Stream-flow forecasting using extreme learning machines: A case study in a semi-arid region in Iraq
Zaher Mundher Yaseen‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬, Othman Jaafar, Ravinesh C. Deo, et al.
Journal of Hydrology (2016) Vol. 542, pp. 603-614
Closed Access | Times Cited: 294
Forecasting methods in energy planning models
Kumar Biswajit Debnath, Monjur Mourshed
Renewable and Sustainable Energy Reviews (2018) Vol. 88, pp. 297-325
Open Access | Times Cited: 293
Drought forecasting in eastern Australia using multivariate adaptive regression spline, least square support vector machine and M5Tree model
Ravinesh C. Deo, Özgür Kişi, Vijay P. Singh
Atmospheric Research (2016) Vol. 184, pp. 149-175
Closed Access | Times Cited: 282
Hourly forecasting of solar irradiance based on CEEMDAN and multi-strategy CNN-LSTM neural networks
Bixuan Gao, Xiaoqiao Huang, Junsheng Shi, et al.
Renewable Energy (2020) Vol. 162, pp. 1665-1683
Closed Access | Times Cited: 277
Review of distributed generation (DG) system planning and optimisation techniques: Comparison of numerical and mathematical modelling methods
Wai Lip Theo, Jeng Shiun Lim, Wai Shin Ho, et al.
Renewable and Sustainable Energy Reviews (2016) Vol. 67, pp. 531-573
Closed Access | Times Cited: 266
Short-term electricity demand forecasting with MARS, SVR and ARIMA models using aggregated demand data in Queensland, Australia
Mohanad S. AL‐Musaylh, Ravinesh C. Deo, Jan Adamowski, et al.
Advanced Engineering Informatics (2017) Vol. 35, pp. 1-16
Closed Access | Times Cited: 262
Novel approach for streamflow forecasting using a hybrid ANFIS-FFA model
Zaher Mundher Yaseen‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬, Isa Ebtehaj, Hossein Bonakdari, et al.
Journal of Hydrology (2017) Vol. 554, pp. 263-276
Closed Access | Times Cited: 241
Prediction of short-term PV power output and uncertainty analysis
Luyao Liu, Yi Zhao, Dongliang Chang, et al.
Applied Energy (2018) Vol. 228, pp. 700-711
Closed Access | Times Cited: 238
A comprehensive review of hybrid models for solar radiation forecasting
Mawloud Guermoui, Farid Melgani, Kacem Gairaa, et al.
Journal of Cleaner Production (2020) Vol. 258, pp. 120357-120357
Closed Access | Times Cited: 225
A current perspective on the accuracy of incoming solar energy forecasting
Robert Blaga, Andreea Săbăduş, Nicoleta Stefu, et al.
Progress in Energy and Combustion Science (2018) Vol. 70, pp. 119-144
Closed Access | Times Cited: 214
Significant wave height forecasting via an extreme learning machine model integrated with improved complete ensemble empirical mode decomposition
Mumtaz Ali, Ramendra Prasad
Renewable and Sustainable Energy Reviews (2019) Vol. 104, pp. 281-295
Closed Access | Times Cited: 201
Computational intelligence approach for modeling hydrogen production: a review
Sina Ardabili, Bahman Najafi, Shahaboddin Shamshirband, et al.
Engineering Applications of Computational Fluid Mechanics (2018) Vol. 12, Iss. 1, pp. 438-458
Open Access | Times Cited: 197
Streamflow prediction using an integrated methodology based on convolutional neural network and long short-term memory networks
Sujan Ghimire, Zaher Mundher Yaseen‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬, Aitazaz A. Farooque, et al.
Scientific Reports (2021) Vol. 11, Iss. 1
Open Access | Times Cited: 194
Soil moisture forecasting by a hybrid machine learning technique: ELM integrated with ensemble empirical mode decomposition
Ramendra Prasad, Ravinesh C. Deo, Yan Li, et al.
Geoderma (2018) Vol. 330, pp. 136-161
Closed Access | Times Cited: 188
Forecasting long-term global solar radiation with an ANN algorithm coupled with satellite-derived (MODIS) land surface temperature (LST) for regional locations in Queensland
Ravinesh C. Deo, Mehmet Şahin
Renewable and Sustainable Energy Reviews (2017) Vol. 72, pp. 828-848
Open Access | Times Cited: 178
Photovoltaic power forecasting based on a support vector machine with improved ant colony optimization
Mingzhang Pan, Chao Li, Gao Ran, et al.
Journal of Cleaner Production (2020) Vol. 277, pp. 123948-123948
Closed Access | Times Cited: 178
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