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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:

Machine-learning methods for integrated renewable power generation: A comparative study of artificial neural networks, support vector regression, and Gaussian Process Regression
Mahdi Sharifzadeh, Alexandra Sikinioti-Lock, Nilay Shah
Renewable and Sustainable Energy Reviews (2019) Vol. 108, pp. 513-538
Closed Access | Times Cited: 294

Showing 1-25 of 294 citing articles:

Machine learning prediction of mechanical properties of concrete: Critical review
Wassim Ben Chaabene, Majdi Flah, Moncef L. Nehdi
Construction and Building Materials (2020) Vol. 260, pp. 119889-119889
Closed Access | Times Cited: 536
Review on deep learning applications in frequency analysis and control of modern power system
Yi Zhang, Xiaohan Shi, Hengxu Zhang, et al.
International Journal of Electrical Power & Energy Systems (2021) Vol. 136, pp. 107744-107744
Closed Access | Times Cited: 213
Deep Learning in the Industrial Internet of Things: Potentials, Challenges, and Emerging Applications
Ruhul Amin Khalil, Nasir Saeed, Mudassir Masood, et al.
IEEE Internet of Things Journal (2021) Vol. 8, Iss. 14, pp. 11016-11040
Open Access | Times Cited: 207
Artificial intelligence and machine learning in energy systems: A bibliographic perspective
Ashkan Entezari, Alireza Aslani, Rahim Zahedi, et al.
Energy Strategy Reviews (2022) Vol. 45, pp. 101017-101017
Open Access | Times Cited: 172
A Survey of Machine Learning Models in Renewable Energy Predictions
Jung-Pin Lai, Yu-Ming Chang, Chieh-Huang Chen, et al.
Applied Sciences (2020) Vol. 10, Iss. 17, pp. 5975-5975
Open Access | Times Cited: 153
Machine Learning and Deep Learning in Energy Systems: A Review
Mohammad Mahdi Forootan, Iman Larki, Rahim Zahedi, et al.
Sustainability (2022) Vol. 14, Iss. 8, pp. 4832-4832
Open Access | Times Cited: 153
Renewable energy in eco-industrial parks and urban-industrial symbiosis: A literature review and a conceptual synthesis
Maria Angela Butturi, Francesco Lolli, Miguel Afonso Sellitto, et al.
Applied Energy (2019) Vol. 255, pp. 113825-113825
Open Access | Times Cited: 147
Prospective Methodologies in Hybrid Renewable Energy Systems for Energy Prediction Using Artificial Neural Networks
Md. Mijanur Rahman, Mohammad Shakeri, Tiong Sieh Kiong, et al.
Sustainability (2021) Vol. 13, Iss. 4, pp. 2393-2393
Open Access | Times Cited: 126
Artificial intelligence techniques for enabling Big Data services in distribution networks: A review
Sara Barja-Martinez, Mònica Aragüés‐Peñalba, Íngrid Munné‐Collado, et al.
Renewable and Sustainable Energy Reviews (2021) Vol. 150, pp. 111459-111459
Open Access | Times Cited: 114
Comparison of neural network, Gaussian regression, support vector machine, long short-term memory, multi-gene genetic programming, and M5 Trees methods for solving civil engineering problems
Erdal Uncuoğlu, Hatice Çıtakoğlu, Levent Latifoğlu, et al.
Applied Soft Computing (2022) Vol. 129, pp. 109623-109623
Closed Access | Times Cited: 79
Quantum computing and quantum artificial intelligence for renewable and sustainable energy: A emerging prospect towards climate neutrality
Akshay Ajagekar, Fengqi You
Renewable and Sustainable Energy Reviews (2022) Vol. 165, pp. 112493-112493
Closed Access | Times Cited: 74
A systematic review on power system resilience from the perspective of generation, network, and load
Chong Wang, Ping Ju, Feng Wu, et al.
Renewable and Sustainable Energy Reviews (2022) Vol. 167, pp. 112567-112567
Closed Access | Times Cited: 68
A Review of Modern Machine Learning Techniques in the Prediction of Remaining Useful Life of Lithium-Ion Batteries
Prabhakar Sharma, Bhaskor Jyoti Bora
Batteries (2022) Vol. 9, Iss. 1, pp. 13-13
Open Access | Times Cited: 68
Forecasting solar energy production: A comparative study of machine learning algorithms
Younes Ledmaoui, Adila El Maghraoui, Mohamed El Aroussi, et al.
Energy Reports (2023) Vol. 10, pp. 1004-1012
Open Access | Times Cited: 56
Machine learning predictions of regional steel price indices for east China
Bingzi Jin, Xiaojie Xu
Ironmaking & Steelmaking Processes Products and Applications (2024)
Closed Access | Times Cited: 51
Short-Term Load Forecasting Models: A Review of Challenges, Progress, and the Road Ahead
Saima Akhtar, Sulman Shahzad, Asad Zaheer, et al.
Energies (2023) Vol. 16, Iss. 10, pp. 4060-4060
Open Access | Times Cited: 50
Potential of Explainable Artificial Intelligence in Advancing Renewable Energy: Challenges and Prospects
Van Nhanh Nguyen, W. Tarełko, Prabhakar Sharma, et al.
Energy & Fuels (2024) Vol. 38, Iss. 3, pp. 1692-1712
Closed Access | Times Cited: 46
Predictive modeling of PV solar power plant efficiency considering weather conditions: A comparative analysis of artificial neural networks and multiple linear regression
Gökhan Şahin, Gültekin Işık, Wilfried van Sark
Energy Reports (2023) Vol. 10, pp. 2837-2849
Open Access | Times Cited: 45
Palladium Price Predictions via Machine Learning
Bingzi Jin, Xiaojie Xu
Materials Circular Economy (2024) Vol. 6, Iss. 1
Closed Access | Times Cited: 42
Predictions of steel price indices through machine learning for the regional northeast Chinese market
Bingzi Jin, Xiaojie Xu
Neural Computing and Applications (2024) Vol. 36, Iss. 33, pp. 20863-20882
Closed Access | Times Cited: 42
Optimizing renewable energy systems through artificial intelligence: Review and future prospects
Kingsley Ukoba, Kehinde O. Olatunji, Eyitayo Adeoye, et al.
Energy & Environment (2024) Vol. 35, Iss. 7, pp. 3833-3879
Open Access | Times Cited: 35
Forecasts of thermal coal prices through Gaussian process regressions
Bingzi Jin, Xiaojie Xu
Ironmaking & Steelmaking Processes Products and Applications (2024) Vol. 51, Iss. 8, pp. 819-834
Closed Access | Times Cited: 31
Machine learning solutions for renewable energy systems: Applications, challenges, limitations, and future directions
Zaid Allal, Hassan Noura, Ola Salman, et al.
Journal of Environmental Management (2024) Vol. 354, pp. 120392-120392
Closed Access | Times Cited: 28
Utilisation of Deep Learning (DL) and Neural Networks (NN) Algorithms for Energy Power Generation: A Social Network and Bibliometric Analysis (2004-2022)
Abdelhamid Zaïdi
International Journal of Energy Economics and Policy (2024) Vol. 14, Iss. 1, pp. 172-183
Open Access | Times Cited: 22
Machine learning for the management of biochar yield and properties of biomass sources for sustainable energy
Van Giao Nguyen, Prabhakar Sharma, Ümit Ağbulut, et al.
Biofuels Bioproducts and Biorefining (2024) Vol. 18, Iss. 2, pp. 567-593
Closed Access | Times Cited: 22
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