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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:

Ensemble methods for wind and solar power forecasting—A state-of-the-art review
Ye Ren, Ponnuthurai Nagaratnam Suganthan, N. V. Srikanth
Renewable and Sustainable Energy Reviews (2015) Vol. 50, pp. 82-91
Closed Access | Times Cited: 370

Showing 1-25 of 370 citing articles:

Ensemble deep learning: A review
M. A. Ganaie, Minghui Hu, A. K. Malik, et al.
Engineering Applications of Artificial Intelligence (2022) Vol. 115, pp. 105151-105151
Open Access | Times Cited: 1195
Review of photovoltaic power forecasting
J. Antonanzas, Natalia Osorio, Rodrigo Escobar, et al.
Solar Energy (2016) Vol. 136, pp. 78-111
Closed Access | Times Cited: 1044
Forecasting of photovoltaic power generation and model optimization: A review
Utpal Kumar Das, Kok Soon Tey, Mehdi Seyedmahmoudian, et al.
Renewable and Sustainable Energy Reviews (2017) Vol. 81, pp. 912-928
Closed Access | Times Cited: 937
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: 867
A review and evaluation of the state-of-the-art in PV solar power forecasting: Techniques and optimization
Razin Ahmed, Victor Sreeram, Yateendra Mishra, et al.
Renewable and Sustainable Energy Reviews (2020) Vol. 124, pp. 109792-109792
Closed Access | Times Cited: 838
Solar photovoltaic generation forecasting methods: A review
Sobrina Sobri, Sam Koohi-Kamalі, Nasrudin Abd Rahim
Energy Conversion and Management (2017) Vol. 156, pp. 459-497
Closed Access | Times Cited: 786
Ensemble Classification and Regression-Recent Developments, Applications and Future Directions [Review Article]
Ye Ren, Le Zhang, Ponnuthurai Nagaratnam Suganthan
IEEE Computational Intelligence Magazine (2016) Vol. 11, Iss. 1, pp. 41-53
Closed Access | Times Cited: 593
Deep Learning for solar power forecasting — An approach using AutoEncoder and LSTM Neural Networks
André Gensler, Janosch Henze, Bernhard Sick, et al.
2022 IEEE International Conference on Systems, Man, and Cybernetics (SMC) (2016)
Closed Access | Times Cited: 553
A review of wind speed and wind power forecasting with deep neural networks
Yun Wang, Runmin Zou, Fang Liu, et al.
Applied Energy (2021) Vol. 304, pp. 117766-117766
Closed Access | Times Cited: 545
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
Ensemble approach based on bagging, boosting and stacking for short-term prediction in agribusiness time series
Matheus Henrique Dal Molin Ribeiro, Leandro dos Santos Coelho
Applied Soft Computing (2019) Vol. 86, pp. 105837-105837
Closed Access | Times Cited: 428
Review on probabilistic forecasting of photovoltaic power production and electricity consumption
Dennis van der Meer, Joakim Widén, Joakim Munkhammar
Renewable and Sustainable Energy Reviews (2017) Vol. 81, pp. 1484-1512
Closed Access | Times Cited: 380
A review on renewable energy and electricity requirement forecasting models for smart grid and buildings
Tanveer Ahmad, Hongcai Zhang, Biao Yan
Sustainable Cities and Society (2020) Vol. 55, pp. 102052-102052
Closed Access | Times Cited: 368
Short-term wind power forecasting based on support vector machine with improved dragonfly algorithm
Lin Li, Xue Zhao, Ming‐Lang Tseng, et al.
Journal of Cleaner Production (2019) Vol. 242, pp. 118447-118447
Closed Access | Times Cited: 363
A review and discussion of decomposition-based hybrid models for wind energy forecasting applications
Zheng Qian, Yan Pei, Hamidreza Zareipour, et al.
Applied Energy (2018) Vol. 235, pp. 939-953
Closed Access | Times Cited: 332
Taxonomy research of artificial intelligence for deterministic solar power forecasting
Huaizhi Wang, Yangyang Liu, Bin Zhou, et al.
Energy Conversion and Management (2020) Vol. 214, pp. 112909-112909
Closed Access | Times Cited: 264
Deterministic wind energy forecasting: A review of intelligent predictors and auxiliary methods
Hui Liu, Chao Chen, Xinwei Lv, et al.
Energy Conversion and Management (2019) Vol. 195, pp. 328-345
Closed Access | Times Cited: 250
Current status of wind energy forecasting and a hybrid method for hourly predictions
İnci Okumuş, Ali Dinler
Energy Conversion and Management (2016) Vol. 123, pp. 362-371
Closed Access | Times Cited: 244
A comprehensive evaluation of ensemble learning for stock-market prediction
Isaac Kofi Nti, Adebayo Felix Adekoya, Benjamin Asubam Weyori
Journal Of Big Data (2020) Vol. 7, Iss. 1
Open Access | Times Cited: 236
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
Day-Ahead Hourly Forecasting of Power Generation From Photovoltaic Plants
Lorenzo Gigoni, Alessandro Betti, Emanuele Crisostomi, et al.
IEEE Transactions on Sustainable Energy (2017) Vol. 9, Iss. 2, pp. 831-842
Open Access | Times Cited: 194
Verification of deterministic solar forecasts
Dazhi Yang, Stefano Alessandrini, J. Antonanzas, et al.
Solar Energy (2020) Vol. 210, pp. 20-37
Open Access | Times Cited: 193
Deep Concatenated Residual Network With Bidirectional LSTM for One-Hour-Ahead Wind Power Forecasting
Min-Seung Ko, Kwangsuk Lee, Jae-Kyeong Kim, et al.
IEEE Transactions on Sustainable Energy (2020) Vol. 12, Iss. 2, pp. 1321-1335
Open Access | Times Cited: 176
Solar power generation forecasting using ensemble approach based on deep learning and statistical methods
Mariam AlKandari, Imtiaz Ahmad
Applied Computing and Informatics (2019) Vol. 20, Iss. 3/4, pp. 231-250
Open Access | Times Cited: 158
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