Machine learning solutions for renewable energy systems: Applications, challenges, limitations, and future directions

Machine Learning for Fluid Mechanics

Brunton, S. L., Noack, B. R., & Koumoutsakos, P. (2020) · Annual Review of Fluid Mechanics · doi:10.1146/annurev-fluid-010719-060214 · Cited by 3,063 · see citing works

A review of renewable energy sources, sustainability issues and climate change mitigation

Owusu, P. A., & Sarkodie, S. A. (2016) · Cogent Engineering · doi:10.1080/23311916.2016.1167990 · Cited by 2,839 · see citing works

Big Data and Machine Learning in Health Care

Beam, A. L., & Kohane, I. S. (2018) · JAMA · doi:10.1001/jama.2017.18391 · Cited by 2,506 · see citing works

Machine learning methods for solar radiation forecasting: A review

Voyant, C., Notton, G., Kalogirou, S., Nivet, M.-L., Paoli, C., Motte, F., & Fouilloy, A. (2017) · Renewable Energy · doi:10.1016/j.renene.2016.12.095 · Cited by 1,836 · see citing works

Optimization methods applied to renewable and sustainable energy: A review

Baños, R., Manzano-Agugliaro, F. R., Montoya, F. G., Gil, C., Alcayde, A., & Gómez, J. C. L. (2011) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2010.12.008 · Cited by 1,649 · see citing works

The National Solar Radiation Data Base (NSRDB)

Sengupta, M., Xie, Y., Lopez, A., Habte, A., Maclaurin, G. J., & Shelby, J. (2018) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2018.03.003 · Cited by 1,466 · see citing works

Towards Sustainable Energy: A Systematic Review of Renewable Energy Sources, Technologies, and Public Opinions

Qazi, A., Fayaz, H., Rahim, N. A., Hardaker, G., Alghazzawi, D., Shaban, K. B., & Haruna, K. (2019) · IEEE Access · doi:10.1109/access.2019.2906402 · Cited by 1,179 · see citing works

BadNets: Identifying Vulnerabilities in the Machine Learning Model Supply Chain

Gu, T., Dolan-Gavitt, B., & Garg, S. (2017) · arXiv · doi:10.48550/arxiv.1708.06733 · Cited by 1,061 · see citing works

Explainable machine learning for scientific insights and discoveries

Roscher, R., Bohn, B., Duarte, M. F., & Garcke, J. (2020) · IEEE Access · doi:10.1109/access.2020.2976199 · Cited by 1,040 · see citing works

The security of machine learning

Barreno, M., Nelson, B. A., Joseph, A. D., & Tygar, J. D. (2010) · Machine Learning · doi:10.1007/s10994-010-5188-5 · Cited by 839 · see citing works

A comparison of performance of several artificial intelligence methods for forecasting monthly discharge time series

Wang, W., Chau, K., Cheng, C., & Qiu, L. (2009) · Journal of Hydrology · doi:10.1016/j.jhydrol.2009.06.019 · Cited by 824 · see citing works

Artificial Intelligence and Economic Growth

Aghion, P., Jones, B., & Jones, C. I. (2017) · National Bureau of Economic Research · doi:10.3386/w23928 · Cited by 633 · see citing works

The state of the art of wind energy conversion systems and technologies: A review

Cheng, M., & Zhu, Y. (2014) · Energy Conversion and Management · doi:10.1016/j.enconman.2014.08.037 · Cited by 602 · see citing works

Wind power forecasting based on daily wind speed data using machine learning algorithms

Demolli, H., Dokuz, A. Ş., Ecemiş, A., & Gökçek, M. (2019) · Energy Conversion and Management · doi:10.1016/j.enconman.2019.111823 · Cited by 474 · see citing works

Deep learning models for solar irradiance forecasting: A comprehensive review

Kumari, P., & Toshniwal, D. (2021) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2021.128566 · Cited by 463 · see citing works

Short-term wind speed forecasting using wavelet transform and support vector machines optimized by genetic algorithm

Liu, D., Niu, D., Wang, H., & Fan, L. (2013) · Renewable Energy · doi:10.1016/j.renene.2013.08.011 · Cited by 451 · see citing works

Identification of PV solar cells and modules parameters using the genetic algorithms: Application to maximum power extraction

Zagrouba, M., Sellami, A., Bouaicha, M., & Ksouri, M. (2010) · Solar Energy · doi:10.1016/j.solener.2010.02.012 · Cited by 444 · see citing works

Hour-ahead wind power forecast based on random forests

Lahouar, A., & Slama, J. B. H. (2017) · Renewable Energy · doi:10.1016/j.renene.2017.03.064 · Cited by 442 · see citing works

Prediction of daily global solar radiation using different machine learning algorithms: Evaluation and comparison

Ağbulut, Ü., Gürel, A. E., & Biçen, Y. (2020) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2020.110114 · Cited by 427 · see citing works

The Impact of Machine Learning on Economics

Athey, S. C. (2019) · University of Chicago Press eBooks · doi:10.7208/chicago/9780226613475.003.0021 · Cited by 419 · see citing works

Fault detection of wind turbine based on SCADA data analysis using CNN and LSTM with attention mechanism

Xiang, L., Wang, P., Yang, X., Hu, A., & Su, H. (2021) · Measurement · doi:10.1016/j.measurement.2021.109094 · Cited by 414 · see citing works

A machine learning framework to forecast wave conditions

James, S. C., Zhang, Y., & O’Donncha, F. (2018) · Coastal Engineering · doi:10.1016/j.coastaleng.2018.03.004 · Cited by 408 · see citing works

Solar radiation forecasting using artificial neural network and random forest methods: Application to normal beam, horizontal diffuse and global components

Benali, L., Notton, G., Fouilloy, A., Voyant, C., & Dizène, R. (2018) · Renewable Energy · doi:10.1016/j.renene.2018.08.044 · Cited by 403 · see citing works

Insights into renewable hydrogen energy: Recent advances and prospects

Pareek, A., Dom, R., Gupta, J., Chandran, J., Adepu, V., & Borse, P. H. (2020) · Materials Science for Energy Technologies · doi:10.1016/j.mset.2019.12.002 · Cited by 396 · see citing works

Machine-learning methods for integrated renewable power generation: A comparative study of artificial neural networks, support vector regression, and Gaussian Process Regression

Sharifzadeh, M., Sikinioti-Lock, A., & Shah, N. (2019) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2019.03.040 · Cited by 386 · see citing works

A support vector machine–firefly algorithm-based model for global solar radiation prediction

Olatomiwa, L. J., Mekhilef, S., Shamshirband, S., Mohammadi, K., Petković, D., & Sudheer, C. (2015) · Solar Energy · doi:10.1016/j.solener.2015.03.015 · Cited by 355 · see citing works

Improved solar photovoltaic energy generation forecast using deep learning-based ensemble stacking approach

Khan, W. A., Walker, S. S. W., & Zeiler, W. (2021) · Energy · doi:10.1016/j.energy.2021.122812 · Cited by 354 · see citing works

A data-driven multi-model methodology with deep feature selection for short-term wind forecasting

Feng, C., Cui, M., Hodge, B. S., & Zhang, J. (2017) · Applied Energy · doi:10.1016/j.apenergy.2017.01.043 · Cited by 320 · see citing works

Machine learning for renewable energy materials

Gu, G. H., Noh, J., Kim, I., & Jung, Y. (2019) · Journal of Materials Chemistry A · doi:10.1039/c9ta02356a · Cited by 311 · see citing works

Wind energy resource atlas of the United States

(2003) · Choice Reviews Online · doi:10.5860/choice.41-2192 · Cited by 306 · see citing works

Wind speed prediction method using Shared Weight Long Short-Term Memory Network and Gaussian Process Regression

Zhang, Z., Ye, L., Qin, H., Liu, Y., Wang, C., Yu, X. J., Yin, X., & Li, J. (2019) · Applied Energy · doi:10.1016/j.apenergy.2019.04.047 · Cited by 299 · see citing works

Daily streamflow forecasting by machine learning methods with weather and climate inputs

Rasouli, K., Hsieh, W. W., & Cannon, A. J. (2011) · Journal of Hydrology · doi:10.1016/j.jhydrol.2011.10.039 · Cited by 298 · see citing works

Review Article-Renewable Energies

Mohtasham, J. (2015) · Energy Procedia · doi:10.1016/j.egypro.2015.07.774 · Cited by 298 · see citing works

Review of small hydropower technology

Okot, D. K. (2013) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2013.05.006 · Cited by 287 · see citing works

2011 IEEE International Conference on Prognostics and Health Management

(2010) · IEEE Transactions on Reliability · doi:10.1109/tr.2010.2095274 · Cited by 286 · see citing works

Review of renewable energy-based hydrogen production processes for sustainable energy innovation

Wang, M., Wang, G., Sun, Z., Zhang, Y., & Xu, D. (2019) · Global Energy Interconnection · doi:10.1016/j.gloei.2019.11.019 · Cited by 284 · see citing works

Predictive modeling of biomass gasification with machine learning-based regression methods

Elmaz, F., Yücel, Ö., & Mutlu, A. Y. (2019) · Energy · doi:10.1016/j.energy.2019.116541 · Cited by 282 · see citing works

Machine learning methods for modelling the gasification and pyrolysis of biomass and waste

Ascher, S., Watson, I., & You, S. (2021) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2021.111902 · Cited by 280 · see citing works

Machine learning on sustainable energy: A review and outlook on renewable energy systems, catalysis, smart grid and energy storage

Rangel-Martínez, D., Nigam, K. D. P., & Ricardez‐Sandoval, L. (2021) · Process Safety and Environmental Protection · doi:10.1016/j.cherd.2021.08.013 · Cited by 265 · see citing works

A Novel Convolutional Neural Network-Based Approach for Fault Classification in Photovoltaic Arrays

Aziz, F., Ul-Haq, A., Ahmad, S., Mahmoud, Y., Jalal, M., & Ali, U. (2020) · IEEE Access · doi:10.1109/access.2020.2977116 · Cited by 257 · see citing works

Solar Power Forecasting Using CNN-LSTM Hybrid Model

Lim, S.-C., Huh, J., Hong, S.-H., Park, C.-Y., & Kim, J.-C. (2022) · Energies · doi:10.3390/en15218233 · Cited by 253 · see citing works

A benchmarking of machine learning techniques for solar radiation forecasting in an insular context

Lauret, P., Voyant, C., Soubdhan, T., David, M., & Poggi, P. (2015) · Solar Energy · doi:10.1016/j.solener.2014.12.014 · Cited by 251 · see citing works

Optimization of solar systems using artificial neural-networks and genetic algorithms

Kalogirou, S. A. (2003) · Applied Energy · doi:10.1016/s0306-2619(03)00153-3 · Cited by 246 · see citing works

Comparing drivers, barriers, and opportunities of business models for renewable energies: A review

Engelken, M., Römer, B., Drescher, M. A., Welpe, I. M., & Picot, A. O. (2016) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2015.12.163 · Cited by 245 · see citing works

A review on application of artificial intelligence in teaching and learning in educational contexts

Fahimirad, M., & Kotamjani, S. S. (2018) · International Journal of Learning and Development · doi:10.5296/ijld.v8i4.14057 · Cited by 240 · see citing works

Significant wave height forecasting via an extreme learning machine model integrated with improved complete ensemble empirical mode decomposition

Ali, M., & Prasad, R. (2019) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2019.01.014 · Cited by 240 · see citing works

Reinforcement learning in sustainable energy and electric systems: a survey

Yang, T., Zhao, L., Li, W., & Zomaya, A. Y. (2020) · Annual Reviews in Control · doi:10.1016/j.arcontrol.2020.03.001 · Cited by 239 · see citing works

Hydrogen production via biomass gasification, and modeling by supervised machine learning algorithms

Özbaş, E. E., Aksu, D., Öngen, A., Aydın, M. A., & Özcan, H. K. (2019) · International Journal of Hydrogen Energy · doi:10.1016/j.ijhydene.2019.02.108 · Cited by 236 · see citing works

Time-series prediction of wind speed using machine learning algorithms: A case study Osorio wind farm, Brazil

Khosravi, A., Machado, L. H. J., & Nunes, R. (2018) · Applied Energy · doi:10.1016/j.apenergy.2018.05.043 · Cited by 229 · see citing works

An SVM-Based Solution for Fault Detection in Wind Turbines

Santos, P., Villa, L. F., Reñones, A., Bustillo, A., & Maudes, J. (2015) · Sensors · doi:10.3390/s150305627 · Cited by 225 · see citing works

Prediction of Hydropower Generation Using Grey Wolf Optimization Adaptive Neuro-Fuzzy Inference System

Dehghani, M., Riahi-Madvar, H., Hooshyaripor, F., Mosavi, A. H., Shamshirband, S., Zavadskas, E. K., & Chau, K. (2019) · Energies · doi:10.3390/en12020289 · Cited by 211 · see citing works

Applications of artificial intelligence‐based modeling for bioenergy systems: A review

Liao, M., & Yao, Y. (2021) · GCB Bioenergy · doi:10.1111/gcbb.12816 · Cited by 197 · see citing works

Fault detection by an ensemble framework of Extreme Gradient Boosting (XGBoost) in the operation of offshore wind turbines

Trizoglou, P., Liu, X., & Lin, Z. (2021) · Renewable Energy · doi:10.1016/j.renene.2021.07.085 · Cited by 194 · see citing works

Wind turbine power modelling and optimization using artificial neural network with wind field experimental data

Sun, H., Qiu, C., Lu, L., Gao, X., Chen, J., & Yang, H. (2020) · Applied Energy · doi:10.1016/j.apenergy.2020.115880 · Cited by 193 · see citing works

Wind, Solar, and Photovoltaic Renewable Energy Systems with and without Energy Storage Optimization: A Survey of Advanced Machine Learning and Deep Learning Techniques

Abualigah, L., Zitar, R. A., Almotairi, K. H., Hussein, A. M., Elaziz, M. A., Nikoo, M. R., & Gandomi, A. H. (2022) · Energies · doi:10.3390/en15020578 · Cited by 184 · see citing works

Wind Turbine Fault Diagnosis and Predictive Maintenance Through Statistical Process Control and Machine Learning

Hsu, G. J. Y., Wang, Y., Lin, K.-C., Chen, M., & Hsu, J. H.-Y. (2020) · IEEE Access · doi:10.1109/access.2020.2968615 · Cited by 183 · see citing works

Forecasting of total daily solar energy generation using ARIMA: A case study

Atique, S., Noureen, S. S., Roy, V., Subburaj, V. H., Bayne, S. B., & MacFie, J. (2019) · IEEE Annual Computing and Communication Workshop and Conference (CCWC) · doi:10.1109/ccwc.2019.8666481 · Cited by 178 · see citing works

Daily global solar radiation prediction based on a hybrid Coral Reefs Optimization – Extreme Learning Machine approach

Salcedo‐Sanz, S., Casanova-Mateo, C., Pastor-Sánchez, Á., & Sánchez-Girón, M. (2014) · Solar Energy · doi:10.1016/j.solener.2014.04.009 · Cited by 169 · see citing works

Analysis of daily solar power prediction with data-driven approaches

Long, H., Zhang, Z., & Su, Y. (2014) · Applied Energy · doi:10.1016/j.apenergy.2014.03.084 · Cited by 165 · see citing works

Prospective Methodologies in Hybrid Renewable Energy Systems for Energy Prediction Using Artificial Neural Networks

Rahman, M. M., Shakeri, M., Tiong, S., Khatun, F., Amin, N., Pasupuleti, J., & Hasan, M. K. (2021) · Sustainability · doi:10.3390/su13042393 · Cited by 162 · see citing works

Solar irradiation prediction with machine learning: Forecasting models selection method depending on weather variability

Fouilloy, A., Voyant, C., Notton, G., Motte, F., Paoli, C., Nivet, M. L., Guillot, E., & Duchaud, J.-L. (2018) · Energy · doi:10.1016/j.energy.2018.09.116 · Cited by 156 · see citing works

Feature selection in wind speed prediction systems based on a hybrid coral reefs optimization – Extreme learning machine approach

Salcedo‐Sanz, S., Pastor-Sánchez, Á., Prieto, L., Blanco-Aguilera, A., & García‐Herrera, R. (2014) · Energy Conversion and Management · doi:10.1016/j.enconman.2014.06.041 · Cited by 153 · see citing works

Machine Learning in Education - a Survey of Current Research Trends

Kučak, D., Juričić, V., & Đambić, G. (2018) · Annals of DAAAM for ... & proceedings of the ... International DAAAM Symposium · doi:10.2507/29th.daaam.proceedings.059 · Cited by 150 · see citing works

Diagnosing wind turbine faults using machine learning techniques applied to operational data

Leahy, K., Hu, R. L., Konstantakopoulos, I. C., Spanos, C. J., & Agogino, A. M. (2016) · IEEE Conference on Prognostics and Health Management · doi:10.1109/icphm.2016.7542860 · Cited by 141 · see citing works

DeepSolarEye: Power Loss Prediction and Weakly Supervised Soiling Localization via Fully Convolutional Networks for Solar Panels

Mehta, S., Azad, A. P., Chemmengath, S., Raykar, V. C., & Shivkumar Kalyanaraman, A. (2018) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv.2018.00043 · Cited by 140 · see citing works

Machine Learning Based Energy Management Model for Smart Grid and Renewable Energy Districts

Ahmed, W., Ansari, H., Khan, B., Ullah, Z., Ali, S. M., Mehmood, C. A., Qureshi, M. B., Hussain, I., Jawad, M., Khan, M. U. S., et al. (2020) · IEEE Access · doi:10.1109/access.2020.3029943 · Cited by 137 · see citing works

A Two-Step Approach to Solar Power Generation Prediction Based on Weather Data Using Machine Learning

Kim, S. G., Jung, J., & Sim, M. K. (2019) · Sustainability · doi:10.3390/su11051501 · Cited by 131 · see citing works

Machine learning-based statistical testing hypothesis for fault detection in photovoltaic systems

Fazai, R., Abodayeh, K., Mansouri, M. M., Trabelsi, M., Nounou, H. N., Nounou, M. N., & Georghiou, G. E. (2019) · Solar Energy · doi:10.1016/j.solener.2019.08.032 · Cited by 131 · see citing works

Machine Learning Application in Reservoir Water Level Forecasting for Sustainable Hydropower Generation Strategy

Sapitang, M., Ridwan, W. M., Kushiar, K. F., Ahmed, A. N., & El‐Shafie, A. (2020) · Sustainability · doi:10.3390/su12156121 · Cited by 127 · see citing works

A feature extraction and machine learning framework for bearing fault diagnosis

Bodi, C., Weng, Y., & Zhang, N. (2022) · Renewable Energy · doi:10.1016/j.renene.2022.04.061 · Cited by 119 · see citing works

Data Security Issues in Deep Learning: Attacks, Countermeasures, and Opportunities

Xu, G., Li, H., Ren, H., Yang, K., & Deng, R. H. (2019) · IEEE Communications Magazine · doi:10.1109/mcom.001.1900091 · Cited by 118 · see citing works

Hybrid approaches based on deep whole-sky-image learning to photovoltaic generation forecasting

Kong, W., Jia, Y., Dong, Z. Y., Meng, K., & Chai, S. (2020) · Applied Energy · doi:10.1016/j.apenergy.2020.115875 · Cited by 118 · see citing works

Opportunities and Challenges of Renewable Energy Production in Ethiopia

Tiruye, G. A., Besha, A. T., Mekonnen, Y. S., Benti, N. E., Gebreslase, G. A., & Tufa, R. A. (2021) · Sustainability · doi:10.3390/su131810381 · Cited by 117 · see citing works

Two-Stage Attention Over LSTM With Bayesian Optimization for Day-Ahead Solar Power Forecasting

Aslam, M., Lee, S., Khang, S.-H., & Hong, S. (2021) · IEEE Access · doi:10.1109/access.2021.3100105 · Cited by 116 · see citing works

Significant wave height and energy flux prediction for marine energy applications: A grouping genetic algorithm – Extreme Learning Machine approach

Cornejo-Bueno, L. M., Borge, J. C. N., García-Díaz, P., Rodríguez, G., & Salcedo‐Sanz, S. (2016) · Renewable Energy · doi:10.1016/j.renene.2016.05.094 · Cited by 113 · see citing works

AI-assistance for predictive maintenance of renewable energy systems

Shin, W., Han, J., & Rhee, W. (2021) · Energy · doi:10.1016/j.energy.2021.119775 · Cited by 112 · see citing works

Metaheuristic Algorithm for Photovoltaic Parameters: Comparative Study and Prediction with a Firefly Algorithm

Louzazni, M., Khouya, A., Amechnoue, K., Gandelli, A., Mussetta, M., & Crăciunescu, A. (2018) · Applied Sciences · doi:10.3390/app8030339 · Cited by 109 · see citing works

Artificial Neural Network and Multiple Linear Regression for Flood Prediction in Mohawk River, New York

Tsakiri, K. G., Marsellos, A. E., & Kapetanakis, S. (2018) · Water · doi:10.3390/w10091158 · Cited by 109 · see citing works

A soft computing based-modified ELECTRE model for renewable energy policy selection with unknown information

Mousavi, M., Gitinavard, H., & Mousavi, S. M. (2016) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2016.09.125 · Cited by 106 · see citing works

Maximization of energy absorption for a wave energy converter using the deep machine learning

Li, L., Yuan, Z., & Gao, Y. (2018) · Energy · doi:10.1016/j.energy.2018.09.093 · Cited by 103 · see citing works

AI agents envisioning the future: Forecast-based operation of renewable energy storage systems using hydrogen with Deep Reinforcement Learning

Dreher, A., Bexten, T., Sieker, T., Lehna, M., Schütt, J., Scholz, C., & Wirsum, M. (2022) · Energy Conversion and Management · doi:10.1016/j.enconman.2022.115401 · Cited by 103 · see citing works

Maintenance management based on Machine Learning and nonlinear features in wind turbines

Jiménez, A. A., Zhang, L., Muñoz, C. Q. G., & Márquez, F. P. G. (2019) · Renewable Energy · doi:10.1016/j.renene.2019.06.135 · Cited by 98 · see citing works

Status of renewable energy in the GCC region and future opportunities

Elrahmani, A., Hannun, J., Eljack, F., & Kazi, M.-K. (2020) · Current Opinion in Chemical Engineering · doi:10.1016/j.coche.2020.100664 · Cited by 95 · see citing works

Multi-task learning for the prediction of wind power ramp events with deep neural networks

Dorado-Moreno, M., Navarin, N., Gutiérrez, P. A., Prieto-Godino, L., Sperduti, A., Salcedo‐Sanz, S., & Hervás‐Martínez, C. (2020) · Neural Networks · doi:10.1016/j.neunet.2019.12.017 · Cited by 94 · see citing works

Prediction and optimization of wave energy converter arrays using a machine learning approach

Sarkar, D., Contal, E., Vayatis, N., & Frédéric Dias, A. (2016) · Renewable Energy · doi:10.1016/j.renene.2016.05.083 · Cited by 92 · see citing works

Bayesian optimization of a hybrid system for robust ocean wave features prediction

Cornejo-Bueno, L. M., Garrido‐Merchán, E. C., Hernández-Lobato, D., & Salcedo‐Sanz, S. (2017) · Neurocomputing · doi:10.1016/j.neucom.2017.09.025 · Cited by 89 · see citing works

Optimization of the Operation and Maintenance of renewable energy systems by Deep Reinforcement Learning

Pinciroli, L., Baraldi, P., Ballabio, G., Compare, M., & Zio, E. (2021) · Renewable Energy · doi:10.1016/j.renene.2021.11.052 · Cited by 89 · see citing works

Efficient daily solar radiation prediction with deep learning 4-phase convolutional neural network, dual stage stacked regression and support vector machine CNN-REGST hybrid model

Ghimire, S., Nguyen‐Huy, T., Deo, R. C., Casillas-Pérez, D., & Salcedo‐Sanz, S. (2022) · Sustainable materials and technologies · doi:10.1016/j.susmat.2022.e00429 · Cited by 89 · see citing works

Study on short-term photovoltaic power prediction model based on the Stacking ensemble learning

Guo, X., Gao, Y., Zheng, D., Ning, Y., & Zhao, Q. (2020) · Energy Reports · doi:10.1016/j.egyr.2020.11.006 · Cited by 88 · see citing works

Offshore wind farm design with the Coral Reefs Optimization algorithm

Salcedo‐Sanz, S., Gallo-Marazuela, D., Pastor-Sánchez, Á., Carro‐Calvo, L., Portilla-Figueras, A., & Prieto, L. (2013) · Renewable Energy · doi:10.1016/j.renene.2013.09.004 · Cited by 86 · see citing works

Condition monitoring and fault diagnostics for hydropower plants

Selak, L., Butala, P., & Sluga, A. (2014) · Computers in Industry · doi:10.1016/j.compind.2014.02.006 · Cited by 83 · see citing works

Solar Radiation Forecasting Using Machine Learning and Ensemble Feature Selection

Solano, E. S., Dehghanian, P., & Affonso, C. M. (2022) · Energies · doi:10.3390/en15197049 · Cited by 82 · see citing works

An Enhanced Grey Wolf Optimization Algorithm for Photovoltaic Maximum Power Point Tracking Control Under Partial Shading Conditions

Millah, I. S., Chang, P. C., Teshome, D. F., Subroto, R. K., Lian, K. L., & Lin, J. (2022) · IEEE Open Journal of the Industrial Electronics Society · doi:10.1109/ojies.2022.3179284 · Cited by 79 · see citing works

Hydroelectric Generating Unit Fault Diagnosis Using 1-D Convolutional Neural Network and Gated Recurrent Unit in Small Hydro

Liao, G.-P., Gao, W., Yang, G.-J., & Guo, M. (2019) · IEEE Sensors Journal · doi:10.1109/jsen.2019.2926095 · Cited by 78 · see citing works

Deformation forecasting of a hydropower dam by hybridizing a long short‐term memory deep learning network with the coronavirus optimization algorithm

Bui, K.-T. T., Torres, J. F., Gutiérrez‐Avilés, D., Nhu, V., Bui, D. T., & Alvarez, F. M. (2022) · Computer-Aided Civil and Infrastructure Engineering · doi:10.1111/mice.12810 · Cited by 76 · see citing works

Prediction of global solar irradiation using hybridized k-means and support vector regression algorithms

Ayodele, T. R., Ogunjuyigbe, A. S. O., Amedu, A., & Munda, J. L. (2019) · Renewable energy focus · doi:10.1016/j.ref.2019.03.003 · Cited by 73 · see citing works

Pyrolysis of biological wastes for bioenergy production: Thermo-kinetic studies with machine-learning method and Py-GC/MS analysis

Shahbeig, H., & Nosrati, M. (2020) · Fuel · doi:10.1016/j.fuel.2020.117238 · Cited by 73 · see citing works

Using machine learning to derive spatial wave data: A case study for a marine energy site

Chen, J., Pillai, A. C., Johanning, L., & Ashton, I. G. C. (2021) · Environmental Modelling & Software · doi:10.1016/j.envsoft.2021.105066 · Cited by 73 · see citing works

A crowdsourced dataset of aerial images with annotated solar photovoltaic arrays and installation metadata

Kasmi, G., Saint‐Drenan, Y., Trebosc, D., Jolivet, R., Leloux, J., Sarr, B., & Dubus, L. (2023) · Scientific Data · doi:10.1038/s41597-023-01951-4 · Cited by 71 · see citing works

Regression by classification

Torgo, L., & da Gama, J. M. P. (1996) · Lecture notes in computer science · doi:10.1007/3-540-61859-7_6 · Cited by 70 · see citing works

Machine learning based switching model for electricity load forecasting

Shu, F., Chen, L., & Lee, W. (2008) · Energy Conversion and Management · doi:10.1016/j.enconman.2008.01.008 · Cited by 66 · see citing works

Forecasting Hydrogen Production from Wind Energy in a Suburban Environment Using Machine Learning

Javaid, A., Javaid, U., Sajid, M. F., Rashid, M., Uddin, E., Ayaz, Y., & Waqas, A. (2022) · Energies · doi:10.3390/en15238901 · Cited by 66 · see citing works

Short-term wind power forecasting using long-short term memory based recurrent neural network model and variable selection

Cali, Ü., & Sharma, V. (2019) · International Journal of Smart Grid and Clean Energy · doi:10.12720/sgce.8.2.103-110 · Cited by 65 · see citing works

Prediction of higher heating value of biomass materials based on proximate analysis using gradient boosted regression trees method

Samadi, S. H., Ghobadian, B., & Nosrati, M. (2019) · Energy Sources Part A Recovery Utilization and Environmental Effects · doi:10.1080/15567036.2019.1630521 · Cited by 65 · see citing works

Optimization and analysis of bioenergy production using machine learning modeling: Multi-layer perceptron, Gaussian processes regression, K-nearest neighbors, and Artificial neural network models

Jin, H., Kim, Y.-G., Jin, Z., Rushchitc, A. A., & Al-Shati, A. S. (2022) · Energy Reports · doi:10.1016/j.egyr.2022.10.334 · Cited by 65 · see citing works

Forecast of Solar Energy Production - A Deep Learning Approach

Zhang, R., Feng, M., Zhang, W., Lu, S., & Wang, F. (2018) · IEEE International Conference on Big Knowledge (ICBK) · doi:10.1109/icbk.2018.00018 · Cited by 65 · see citing works

CNN-based Deep Learning Approach for Micro-crack Detection of Solar Panels

Rahman, M. R., Tabassum, S., Haque, E., Nishat, M. M., Faisal, F., & Hossain, E. (2021) · International Conference on Sustainable Technologies for Industry 5.0 (STI) · doi:10.1109/sti53101.2021.9732592 · Cited by 65 · see citing works

Wind Farm Modeling with Interpretable Physics-Informed Machine Learning

Howland, M. F., & Dabiri, J. O. (2019) · Energies · doi:10.3390/en12142716 · Cited by 64 · see citing works

2010 IEEE International Conference on Power and Energy

(2010) · doi:10.1109/pecon17546.2010 · Cited by 64 · see citing works

Feasibility study of hydrogen production from wind power in the region of Ghardaia

Aiche-Hamane, L., Belhamel, M., Benyoucef, B., & Hamane, M. (2009) · International Journal of Hydrogen Energy · doi:10.1016/j.ijhydene.2008.12.037 · Cited by 63 · see citing works

Agnostic deep neural network approach to the estimation of hydrogen production for solar-powered systems

Mert, İ. (2020) · International Journal of Hydrogen Energy · doi:10.1016/j.ijhydene.2020.11.161 · Cited by 63 · see citing works

Deep Belief Networks With Genetic Algorithms in Forecasting Wind Speed

Lin, K.-P., Pai, P., & Ting, Y.-J. (2019) · IEEE Access · doi:10.1109/access.2019.2929542 · Cited by 62 · see citing works

Application of machine learning for wind energy from design to energy-Water nexus: A Survey

Elyasichamazkoti, F., & Khajehpoor, A. (2021) · Energy Nexus · doi:10.1016/j.nexus.2021.100011 · Cited by 62 · see citing works

Geothermal heat exchanger energy prediction based on time series and monitoring sensors optimization

Baruque, B., Porras, S., Jove, E., & ROLLE, J. L. C. (2019) · Energy · doi:10.1016/j.energy.2018.12.207 · Cited by 59 · see citing works

A New Hybrid Approach to Forecast Wind Power for Large Scale Wind Turbine Data Using Deep Learning with TensorFlow Framework and Principal Component Analysis

Khan, M., Liu, T., & Ullah, F. (2019) · Energies · doi:10.3390/en12122229 · Cited by 59 · see citing works

A Machine Learning‐Based Model for Stability Prediction of Decentralized Power Grid Linked with Renewable Energy Resources

Ibrar, M., Awais, M., Shaukat, K., Alam, T. M., Khurshid, S. K., Hameed, I. A., Aljuaid, H., & Luo, S. (2022) · Wireless Communications and Mobile Computing · doi:10.1155/2022/2697303 · Cited by 59 · see citing works

Decision tree based online stability assessment scheme for power systems with renewable generations

Wang, T., Bi, T., Wang, H. F., & Liu, J. (2015) · CSEE Journal of Power and Energy Systems · doi:10.17775/cseejpes.2015.00019 · Cited by 57 · see citing works

Prediction of hydropower generation via machine learning algorithms at three Gorges Dam, China

Hanoon, M. S., Ahmed, A. N., Razzaq, A., Oudah, A. Y., Alkhayyat, A. H., Huang, Y. F., kumar, P., & El‐Shafie, A. (2022) · Ain Shams Engineering Journal · doi:10.1016/j.asej.2022.101919 · Cited by 57 · see citing works

Incorporating AI into military decision making: an experiment

Rasch, R., Kott, A., & Forbus, K. D. (2003) · IEEE Intelligent Systems · doi:10.1109/mis.2003.1217624 · Cited by 56 · see citing works

A machine learning approach for mapping the very shallow theoretical geothermal potential

Assouline, D., Mohajeri, N., Guðmundsson, Á., & Scartezzini, J. (2019) · Geothermal Energy · doi:10.1186/s40517-019-0135-6 · Cited by 56 · see citing works

Hydro-power production capacity prediction based on machine learning regression techniques

Condemi, C., Casillas-Pérez, D., Mastroeni, L., Jiménez-Fernández, S., & Salcedo‐Sanz, S. (2021) · Knowledge-Based Systems · doi:10.1016/j.knosys.2021.107012 · Cited by 54 · see citing works

Machine learning-based surrogate model for accelerating simulation-driven optimisation of hydropower Kaplan turbine

Masood, Z., Khan, S., & Li, Q. (2021) · Renewable Energy · doi:10.1016/j.renene.2021.04.005 · Cited by 53 · see citing works

Wind power forecasting based on time series model using deep machine learning algorithms

Chandran, V., Patil, C. K., Manoharan, A. M., Ghosh, A., Sumithra, M. G., Karthick, A., Rahim, R., & Arun, K. (2021) · Materials Today Proceedings · doi:10.1016/j.matpr.2021.03.728 · Cited by 52 · see citing works

A Combined Prognostic Model Based on Machine Learning for Tidal Current Prediction

Kavousi‐Fard, A., & Su, W. (2017) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2017.2659538 · Cited by 50 · see citing works

Soft computing based on hierarchical evaluation approach and criteria interdependencies for energy decision-making problems: A case study

Gitinavard, H., Mousavi, S. M., & Vahdani, B. (2016) · Energy · doi:10.1016/j.energy.2016.10.070 · Cited by 49 · see citing works

Parameter Extraction of Three Diode Solar Photovoltaic Model Using Improved Grey Wolf Optimizer

Ramadan, A., Kamel, S. M., Khurshaid, T., Oh, S.-R., & Rhee, S.-B. (2021) · Sustainability · doi:10.3390/su13126963 · Cited by 49 · see citing works

Multiple Site Intraday Solar Irradiance Forecasting by Machine Learning Algorithms: MGGP and MLP Neural Networks

de Paiva, G. M., Pimentel, S. P., de Alvarenga, B. P., Marra, E. G., Mussetta, M., & Leva, S. (2020) · Energies · doi:10.3390/en13113005 · Cited by 48 · see citing works

Distributed Optimization of Solar Micro-grid Using Multi Agent Reinforcement Learning

Raju, L., Sankar, S., & Milton, R. S. (2015) · Procedia Computer Science · doi:10.1016/j.procs.2015.02.016 · Cited by 47 · see citing works

Optimal Parameter Estimation of Solar PV Panel Based on Hybrid Particle Swarm and Grey Wolf Optimization Algorithms.

Rezk, H., Arfaoui, J., & Gomaa, M. R. (2021) · International Journal of Interactive Multimedia and Artificial Intelligence · doi:10.9781/ijimai.2020.12.001 · Cited by 47 · see citing works

The Tall Tower Dataset: a unique initiative to boost wind energy research

Ramón, J., Lledó, L., Pérez‐Zanón, N., Soret, A., & Doblas-Reyes, F. J. (2020) · Earth system science data · doi:10.5194/essd-12-429-2020 · Cited by 44 · see citing works

Quantifying the Uncertainties in Data-Driven Models for Reservoir Inflow Prediction

Zhang, X., Wang, H., Peng, A., Wang, W., Li, B., & Huang, X. (2020) · Water Resources Management · doi:10.1007/s11269-020-02514-7 · Cited by 43 · see citing works

A Mediterranean Sea Offshore Wind classification using MERRA-2 and machine learning models

Nezhad, M. M., Heydari, A., Neshat, M., Keynia, F., Piras, G., & Garcia, D. A. (2022) · Renewable Energy · doi:10.1016/j.renene.2022.03.110 · Cited by 43 · see citing works

Machine Learning Models Based on Random Forest Feature Selection and Bayesian Optimization for Predicting Daily Global Solar Radiation

Chaibi, M., Benghoulam, E. M., Tarik, L., Berrada, M., & Hmaidi, A. E. (2021) · International Journal of Renewable Energy Development · doi:10.14710/ijred.2022.41451 · Cited by 42 · see citing works

Ensemble Machine Learning for Predicting the Power Output from Different Solar Photovoltaic Systems

Raj, V., Dotse, S.-Q., Sathyajith, M., Petra, M. I., & Yassin, H. (2023) · Energies · doi:10.3390/en16020671 · Cited by 42 · see citing works

Modeling Subsurface Performance of a Geothermal Reservoir Using Machine Learning

Duplyakin, D., Beckers, K. F., Siler, D. L., Martin, M. J., & Johnston, H. (2022) · Energies · doi:10.3390/en15030967 · Cited by 39 · see citing works

Grey wolf optimizer for optimal sizing of hybrid wind and solar renewable energy system

Geleta, D. K., Manshahia, M. S., Vasant, P., & Banik, A. (2020) · Computational Intelligence · doi:10.1111/coin.12349 · Cited by 38 · see citing works

Optimization of a seasonal storage solar system using Genetic Algorithms

Durão, B., Joyce, A., & Mendes, J. F. (2014) · Solar Energy · doi:10.1016/j.solener.2013.12.031 · Cited by 37 · see citing works

A Dual-Step Integrated Machine Learning Model for 24h-Ahead Wind Energy Generation Prediction Based on Actual Measurement Data and Environmental Factors

Ma, Y., & Zhai, M. (2019) · Applied Sciences · doi:10.3390/app9102125 · Cited by 37 · see citing works

Solar Irradiance Probabilistic Forecasting Using Machine Learning, Metaheuristic Models and Numerical Weather Predictions

Sansine, V., Ortéga, P., Hissel, D., & Hopuare, M. (2022) · Sustainability · doi:10.3390/su142215260 · Cited by 37 · see citing works

When Machine Learning meets Security Issues: A survey

Guan, Z., Bian, L., Shang, T., & Liu, J. (2018) · IEEE International Conference on Intelligence and Safety for Robotics (ISR) · doi:10.1109/iisr.2018.8535799 · Cited by 37 · see citing works

Machine learning to identify geologic factors associated with production in geothermal fields: a case-study using 3D geologic data, Brady geothermal field, Nevada

Siler, D. L., Pepin, J. D., Vesselinov, V. V., Mudunuru, M. K., & Ahmmed, B. (2021) · Geothermal Energy · doi:10.1186/s40517-021-00199-8 · Cited by 35 · see citing works

Machine Learning-Based Small Hydropower Potential Prediction under Climate Change

Jung, J., Han, H., Kim, K., & Kim, H. S. (2021) · Energies · doi:10.3390/en14123643 · Cited by 32 · see citing works

Short-Term Solar Power Forecasting via General Regression Neural Network with Grey Wolf Optimization

Tu, C., Tsai, W.-C., Hong, C.-M., & Lin, W.-M. (2022) · Energies · doi:10.3390/en15186624 · Cited by 32 · see citing works

“Dust in the Wind...”, Deep Learning Application to Wind Energy Time Series Forecasting

Manero, J., Béjar, J., & Cortés, U. (2019) · Energies · doi:10.3390/en12122385 · Cited by 31 · see citing works

Sustainable feedstocks selection and renewable products allocation: A new hybrid adaptive utility-based consensus model

Gitinavard, H., Shirazi, M. A., & Zarandi, M. H. F. (2020) · Journal of Environmental Management · doi:10.1016/j.jenvman.2020.110428 · Cited by 31 · see citing works

Numerical modelling of hydrodynamics and tidal energy extraction in the Alderney Race: a review

Thiébot, J., Coles, D. S., Bennis, A., Guillou, N., Neill, S. P., Guillou, S., & Piggott, M. D. (2020) · Philosophical Transactions of the Royal Society A Mathematical Physical and Engineering Sciences · doi:10.1098/rsta.2019.0498 · Cited by 30 · see citing works

Smart Climate Hydropower Tool: A Machine-Learning Seasonal Forecasting Climate Service to Support Cost–Benefit Analysis of Reservoir Management

Essenfelder, A. H., Larosa, F., Mazzoli, P., Bagli, S., Broccoli, D., Luzzi, V., Myšiak, J., Mercogliano, P., & Valle, F. D. (2020) · Atmosphere · doi:10.3390/atmos11121305 · Cited by 28 · see citing works

Fault Diagnosis in Hybrid Renewable Energy Sources with Machine Learning Approach

Wang, H. (2021) · Journal of Trends in Computer Science and Smart Technology · doi:10.36548/jtcsst.2021.3.005 · Cited by 27 · see citing works

A Deep Learning Approach for Modeling of Geothermal Energy Prediction

Gangwani, P. (2020) · Cited by 26 · see citing works

The Effectiveness of Feature Selection Method in Solar Power Prediction

Hossain, M. R., Oo, A. M. T., & Ali, A. B. M. S. (2013) · Journal of Renewable Energy · doi:10.1155/2013/952613 · Cited by 24 · see citing works

Monitoring hydropower reliability in Malawi with satellite data and machine learning

Falchetta, G., Kasamba, C., & Parkinson, S. C. (2019) · Environmental Research Letters · doi:10.1088/1748-9326/ab6562 · Cited by 23 · see citing works

Deep Learning for Deep Waters: An Expert-in-the-Loop Machine Learning Framework for Marine Sciences

Ryazanov, I., Nylund, A. T., Basu, D., Hassellöv, I., & Schliep, A. (2021) · Journal of Marine Science and Engineering · doi:10.3390/jmse9020169 · Cited by 23 · see citing works

Geothermal Energy: A Review

Igwe, C. I. (2021) · Zenodo (CERN European Organization for Nuclear Research) · doi:10.5281/zenodo.18608934 · Cited by 21 · see citing works

Sustainable High-Tech Brick Production with Energy-Oriented Consumption: An Integrated Possibilistic Approach Based on Criteria Interdependencies

Solgi, E., Gitinavard, H., & Tavakkoli-Moghaddam, R. R. (2021) · Sustainability · doi:10.3390/su14010202 · Cited by 21 · see citing works

Sécurisation des Algorithmes d'Apprentissage Automatique

Granese, F. (2023) · theses.fr (ABES) · Cited by 21 · see citing works

Monitoring Solar Panels using Machine Learning Techniques

Haba, C.-G. (2019) · International Conference on Modern Power Systems (MPS) · doi:10.1109/mps.2019.8759651 · Cited by 21 · see citing works

When less is more: How increasing the complexity of machine learning strategies for geothermal energy assessments may not lead toward better estimates

Mordensky, S., Lipor, J. J., DeAngelo, J., Burns, E. R., & Lindsey, C. R. (2023) · Geothermics · doi:10.1016/j.geothermics.2023.102662 · Cited by 20 · see citing works

An Effective Fuzzy Feature Selection and Prediction Method for Modeling Tidal Current: A Case of Persian Gulf

Papari, B., Edrington, C. S., & Kavousi-Fard, F. (2017) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2017.2696541 · Cited by 17 · see citing works

Application of machine learning for hydropower plant silt data analysis

Kumar, K., & Saini, R. P. (2020) · Materials Today Proceedings · doi:10.1016/j.matpr.2020.09.375 · Cited by 17 · see citing works

Machine Learning for Solar Resource Assessment Using Satellite Images

Palacios, L. E. O., Guerrero, V. B., & Ordóñez, H. (2022) · Energies · doi:10.3390/en15113985 · Cited by 17 · see citing works

Effect of wide observation of nature in renewable energy engineering education

Ghandriz, Y., Noorbakhsh, S. M. Z., Gavagsaz‐Ghoachani, R., & Phattanasak, M. (2021) · Research, Invention, and Innovation Congress: Innovative Electricals and Electronics (RI2C) · doi:10.1109/ri2c51727.2021.9559741 · Cited by 15 · see citing works

Machine Learning Tools for Fossil and Geothermal Energy Production and Carbon Geo-sequestration—a Step Towards Energy Digitization and Geoscientific Digitalization

Misra, S., Liu, R., Chakravarty, A., & Gonzalez, K. (2021) · Circular Economy and Sustainability · doi:10.1007/s43615-021-00105-1 · Cited by 13 · see citing works

Optimal operation of hydropower reservoirs under climate change

Ehteram, M., Ahmed, A. N., Fai, C. M., Latif, S. D., Chau, K., Chong, K. L., & El‐Shafie, A. (2022) · Environment Development and Sustainability · doi:10.1007/s10668-022-02497-y · Cited by 13 · see citing works

Locating Renewable Energy Generators Using K-Nearest Neighbors (KNN) Algorithm

Asadi, M., & Pourhossein, K. (2019) · Iranian Conference on Renewable Energy & Distributed Generation (ICREDG) · doi:10.1109/icredg47187.2019.190179 · Cited by 13 · see citing works

Hybrid metaheuristic of artificial neural network — Bat algorithm in forecasting electricity production and water consumption at Sultan Azlan shah Hydropower plant

Hussin, S. N. H. S., Malek, M. A., Jaddi, N. S., & Hamid, Z. A. (2016) · IEEE International Conference on Power and Energy · doi:10.1109/pecon.2016.7951467 · Cited by 11 · see citing works

A system dynamics approach to analysing bioethanol and biodiesel supply chains: increasing bioethanol and biodiesel market shares in the USA

Ghaderi, H., Gitinavard, H., & Pishvaee, M. S. (2019) · International Journal of Energy Technology and Policy · doi:10.1504/ijetp.2020.103849 · Cited by 10 · see citing works

Application of augmented bat algorithm with artificial neural network in forecasting river inflow in Malaysia

Wee, W. J., Chong, K. L., Ahmed, A. N., Malek, M. B. A., Huang, Y. F., Sherif, M. M., & El‐Shafie, A. (2022) · Applied Water Science · doi:10.1007/s13201-022-01831-z · Cited by 10 · see citing works

A possibilistic programming approach for biomass supply chain network design under hesitant fuzzy membership function estimation

Gitinavard, H., Shirazi, M. A., & Zarandi, M. H. F. (2021) · Scientia Iranica · doi:10.24200/sci.2021.55021.4035 · Cited by 9 · see citing works

Application of support vector regression integrated with firefly optimization algorithm for predicting global solar radiation

Samadianfard, S., Jarhan, S., & Nahand, H. S. (2018) · Journal of Energy Systems · doi:10.30521/jes.458328 · Cited by 6 · see citing works

Artificial Neural Network Prediction of Minimum Fluidization Velocity for Mixtures of Biomass and Inert Solid Particles

Reyes-Urrutia, A., Capossio, J. P., Venier, C. M., Torres, E., Rodríguez, R., & Mazza, G. (2023) · Fluids · doi:10.3390/fluids8040128 · Cited by 5 · see citing works

Analysis of bioenergy by using linear regression

Iqbal, M. S., Khan, T., Kausar, S., & Bin, L. (2019) · SN Applied Sciences · doi:10.1007/s42452-019-1270-1 · Cited by 4 · see citing works

Advances in Artificial Intelligence: 13th Brazilian Symposium on Artificial Intelligence, Sbia '96, Curitiba, Brazil, October 23-25, 1996 Proceedings

Borges, D. L., & Kaestner, C. A. A. (1996) · Cited by 2 · see citing works

Machine learning applications in energy systems: current trends, challenges, and research directions

Aslam, S., Aung, P. P., Rafsanjani, A. S., & Majeed, A. P. P. A. (2025) · Energy Informatics · doi:10.1186/s42162-025-00524-6 · Cited by 112 · see citing works

Adaptive energy management strategy for optimal integration of wind/PV system with hybrid gravity/battery energy storage using forecast models

Emrani, A., Achour, Y., Sanjari, M. J., & Berrada, A. (2024) · Journal of Energy Storage · doi:10.1016/j.est.2024.112613 · Cited by 78 · see citing works

How do semiconductors, artificial intelligence, geopolitical risk, and their moderating effects shape renewable energy production in leading semiconductor manufacturing countries?

Rasheed, M. Q., Yuhuan, Z., Nazir, M., Ahmed, Z., & Yu, X. (2024) · Technology in Society · doi:10.1016/j.techsoc.2024.102761 · Cited by 67 · see citing works

Explainable artificial intelligence of tree-based algorithms for fault detection and diagnosis in grid-connected photovoltaic systems

Noura, H., Allal, Z., Salman, O., & Chahine, K. (2024) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2024.109503 · Cited by 63 · see citing works

A review on machine learning applications in hydrogen energy systems

Allal, Z., Noura, H., Salman, O., Vernier, F., & Chahine, K. (2025) · International Journal of Thermofluids · doi:10.1016/j.ijft.2025.101119 · Cited by 50 · see citing works

Advancing organic photovoltaic cells for a sustainable future: The role of artificial intelligence (AI) and deep learning (DL) in enhancing performance and innovation

Togun, H., Basem, A., Jweeg, M. J., Biswas, N., Abed, A. M., Paul, D., Mohammed, H. I., Chattopadhyay, A. K., Sharma, B. K., & Abdulrazzaq, T. (2025) · Solar Energy · doi:10.1016/j.solener.2025.113378 · Cited by 50 · see citing works

Overview of emerging electronics technologies for artificial intelligence: A review

Gao, P., & Adnan, M. (2025) · Materials Today Electronics · doi:10.1016/j.mtelec.2025.100136 · Cited by 39 · see citing works

Exploiting Artificial Neural Networks for the State of Charge Estimation in EV/HV Battery Systems: A Review

Dini, P., & Paolini, D. (2025) · Batteries · doi:10.3390/batteries11030107 · Cited by 40 · see citing works

Wind turbine fault detection and identification using a two-tier machine learning framework

Allal, Z., Noura, H., Vernier, F., Salman, O., & Chahine, K. (2024) · Intelligent Systems with Applications · doi:10.1016/j.iswa.2024.200372 · Cited by 33 · see citing works

Photovoltaic solar energy prediction using the seasonal-trend decomposition layer and ASOA optimized LSTM neural network model

Mohanasundaram, V., & Balamurugan, R. (2025) · Scientific Reports · doi:10.1038/s41598-025-87625-0 · Cited by 32 · see citing works

Recent Advances in Molybdenum Disulfide (MoS 2 ) and MXene‐based Heterostructures for Photovoltaic and Water Splitting Applications: A Review

Obodo, P. C., Obodo, K. O., Aigbe, U. O., & Aigbodion, V. S. (2025) · ChemistrySelect · doi:10.1002/slct.202404995 · Cited by 31 · see citing works

Random forest based wind power prediction method for sustainable energy system

Mustaffa, Z., & Sulaiman, M. H. (2025) · Cleaner Energy Systems · doi:10.1016/j.cles.2025.100210 · Cited by 31 · see citing works

A comparative study of ensemble methods and multi-output classifiers for predictive maintenance of hydraulic systems

Noura, H., Chu, T., Allal, Z., Salman, O., & Chahine, K. (2024) · Results in Engineering · doi:10.1016/j.rineng.2024.102900 · Cited by 28 · see citing works

Predicting green hydrogen production using electrolyzers driven by photovoltaic panels and wind turbines based on machine learning techniques: A pathway to on-site hydrogen refuelling stations

Urhan, B. B., Erdoğmuş, A., Dokuz, A. Ş., & Gökçek, M. (2025) · International Journal of Hydrogen Energy · doi:10.1016/j.ijhydene.2025.01.017 · Cited by 28 · see citing works

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Nguyen, T. H., Paramasivam, P., Dong, V. H., Le, H. C., & Nguyen, D. C. (2024) · JOIV International Journal on Informatics Visualization · doi:10.62527/joiv.8.1.2637 · Cited by 36 · see citing works

Toward 100% Renewable Power Grids: A Review

Yang, Y., Wen, Y., Zhang, Z., Dong, Y., Shen, C., & Liu, Y. (2025) · IEEE Access · doi:10.1109/access.2025.3558772 · Cited by 24 · see citing works

Advanced Machine Learning in Smart Grids: An overview

Noura, H., Yaacoub, J.-P. A., Salman, O., & Chehab, A. (2025) · Internet of Things and Cyber-Physical Systems · doi:10.1016/j.iotcps.2025.05.002 · Cited by 24 · see citing works

The use of single-metal atom-based photocatalysts for the production of ammonia through photocatalytic nitrogen fixation

Zhang, P., Yu, Y., Tuerhong, R., Du, X., Chai, K., Su, X., Su, Q., Meng, S., & Han, L. (2024) · Inorganic Chemistry Frontiers · doi:10.1039/d4qi02449d · Cited by 23 · see citing works

A CFD-driven machine learning approach for predicting PEM electrolyzer performance

Özdemir, S. N., & Pektezel, O. (2025) · Fuel · doi:10.1016/j.fuel.2025.136479 · Cited by 23 · see citing works

A multivariate nonlinear regression prediction model for the performance of cooling tower assisted ground source heat pump system

Lan, T., Hu, R., Tang, Q., Han, M., Wu, S., & Liu, G. (2024) · Energy Conversion and Management · doi:10.1016/j.enconman.2024.119333 · Cited by 21 · see citing works

Progress in prediction of photocatalytic CO2 reduction using machine learning approach: A mini review

Ali, M. M., Hossen, M. A., & Aziz, A. A. (2025) · Next Materials · doi:10.1016/j.nxmate.2025.100522 · Cited by 18 · see citing works

Leveraging Machine Learning for Predictive Sustainability in Business Operations: A Classification Approach to Optimize Sustainable Resource Management

Shannaq, B., & Ali, O. (2025) · Studies in big data · doi:10.1007/978-3-031-83911-5_57 · Cited by 18 · see citing works

Integrating Machine Learning and Multi-Objective Optimization in Biofuel Systems: A Review

Malashin, I. P., Martysyuk, D., Тынченко, В. С., Gantimurov, A., Nelyub, V. A., & Бородулин, А. С. (2025) · IEEE Access · doi:10.1109/access.2025.3568297 · Cited by 17 · see citing works

A novel intensified anaerobic co-digestion process for high-quality biomethane production using ultrasound energy: Optimization and robustness tests with ML-RSM-TLBO algorithm

Ahmad, A., Yadav, A. K., Pal, A., & Hasan, S. (2024) · Energy Conversion and Management · doi:10.1016/j.enconman.2024.119408 · Cited by 17 · see citing works

Leveraging machine learning for optimized microgrid management: Advances, applications, challenges, and future directions

Negi, G. S., Mohan, H., Gupta, M. K., Singh, R., Gehlot, A., Thakur, A. K., Dogra, S., & Gupta, L. R. (2025) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2025.116345 · Cited by 16 · see citing works

Hydroelectric Power Potentiality Analysis for the Future Aspect of Trends with R2 Score Estimation by XGBoost and Random Forest Regressor Time Series Models

Chowdhury, S., Saha, A. K., & Das, D. K. (2025) · Procedia Computer Science · doi:10.1016/j.procs.2025.01.004 · Cited by 15 · see citing works

Addressing Challenges for the Effective Adoption of Artificial Intelligence in the Energy Sector

Park, C. (2025) · Sustainability · doi:10.3390/su17135764 · Cited by 17 · see citing works

Machine learning for renewable energy advancements: Prospects and emerging techniques

Khan, S., Mim, J. J., Shorna, J. F., Hasan, A. M., Tarek, H. R., Islam, M. A., & Hossain, N. (2026) · Energy Reports · doi:10.1016/j.egyr.2025.109008 · Cited by 13 · see citing works

Day-Ahead Energy Price Forecasting with Machine Learning: Role of Endogenous Predictors

Ibebuchi, C. C. (2025) · Forecasting · doi:10.3390/forecast7020018 · Cited by 13 · see citing works

Advances in solar energy technologies: A comprehensive review of photovoltaic, thermal, and hybrid systems for sustainable energy solutions

Tibebu, S., Bogale, D., Asfaw, A. H., & Worku, A. (2025) · Next research. · doi:10.1016/j.nexres.2025.101176 · Cited by 12 · see citing works

Global renewable energy forecasting using hybrid ML/DL models: Economic and geospatial insights

Sarıman, G., & Keçebaş, A. (2025) · Energy Policy · doi:10.1016/j.enpol.2025.114929 · Cited by 12 · see citing works

Performance Monitoring of Photovoltaic Modules Using Machine‐Learning‐Based Solutions: A Survey of Current Trends

Nyangiwe, N. N., Kenfack, A. D. K., Thantsha, N. M., & Msimanga, M. (2025) · Energy Science & Engineering · doi:10.1002/ese3.70423 · Cited by 12 · see citing works

Time Series Analysis of Solar Power Generation Based on Machine Learning for Efficient Monitoring

Farooq, U., Mushtaq, M. F., Ullah, Z., Ejaz, M. T., Akram, U., & Aslam, S. (2025) · Engineering Reports · doi:10.1002/eng2.70023 · Cited by 11 · see citing works

Leveraging microbial synergy: Predicting the optimal consortium to enhance the performance of microbial fuel cell using Subspace-kNN

Mehta, J., Chatterjee, S., & Shah, M. T. (2024) · Journal of Environmental Management · doi:10.1016/j.jenvman.2024.122252 · Cited by 10 · see citing works

A hybrid approach to wind power intensity classification using decision trees and large language models

Akıncı, T. Ç., Nogay, H. S., Penchev, M., Martinez‐Morales, A. A., & Raju, A. (2025) · Renewable Energy · doi:10.1016/j.renene.2025.123388 · Cited by 10 · see citing works

Optimization and deep learning methods for the techno-economic analysis of hydrogen generation from solar, wind, and municipal biowaste to partially replace natural gas in a 10 MW gas power plant in Oman

Sekhar, S. J., Bora, S., Gopinath, A. S., Kalabarige, L. R., & Mathimani, T. (2025) · International Journal of Hydrogen Energy · doi:10.1016/j.ijhydene.2025.150224 · Cited by 9 · see citing works

Artificial intelligence in waste management systems: Applications, challenges, and prospects

Belyamani, I. (2025) · Waste Management Bulletin · doi:10.1016/j.wmb.2025.100269 · Cited by 9 · see citing works

Intelligent Control Mechanisms for Renewable Energy Integration Using Machine Learning Models

Chahar, S., Kaur, K., Kaswan, K. S., & Malik, K. (2025) · International Conference on Pervasive Computational Technologies (ICPCT) · doi:10.1109/icpct64145.2025.10940347 · Cited by 8 · see citing works

Synthesis and characterization of polyvinyl alcohol crosslinked with various degrees of oxalic acid for advanced ionic conductive applications

Hdidar, M., Chaari, M., Haddar, N., Megdiche, M., & Arous, M. (2025) · Ionics · doi:10.1007/s11581-025-06303-3 · Cited by 8 · see citing works

Machine learning-based prediction and optimization of solid oxide electrolysis cells for green hydrogen production using RF, FFNN, and PSO approaches

Shomope, I., Al–Othman, A., Tawalbeh, M., & Alshraideh, H. A. (2025) · Energy · doi:10.1016/j.energy.2025.139719 · Cited by 8 · see citing works

Evaluating the Dependability Measures of a Hybrid Wind–Wave Power Generation System Under Varied Weather Conditions

Psomas, P. M., Platis, A. Ν., Dagkinis, I., Dragović, B. M., Lilas, T., & Nikitakos, N. V. (2024) · Journal of Marine Science and Application · doi:10.1007/s11804-024-00467-6 · Cited by 7 · see citing works

Implementation of a hierarchical cluster model to analyze wind and solar availability in the department of Antioquia, Colombia

Restrepo, A., Villegas, D. J., Rodriguez, C., Cogollo, A. D., Bedoya, I. D., & Amell, A. (2024) · Case Studies in Chemical and Environmental Engineering · doi:10.1016/j.cscee.2024.101006 · Cited by 7 · see citing works

Research progress and prospects of machine learning applications in renewable energy: a comprehensive bibliometric-based review

Wang, X., Shen, Y., & Su, C. (2024) · International Journal of Environmental Science and Technology · doi:10.1007/s13762-024-06210-6 · Cited by 7 · see citing works

Proactive thermal management of photovoltaic systems using nanofluid cooling and advanced machine learning models

Masalha, I., Alahmer, A. I., Badran, O., Al-Khawaldeh, M. A., Masuri, S. U., & Maaitah, H. (2025) · Journal of Thermal Analysis and Calorimetry · doi:10.1007/s10973-025-14746-z · Cited by 7 · see citing works

Machine learning-based prediction model of wind turbine power generation

Allal, Z., Noura, H., Salman, O., & Chahine, K. (2025) · Cleaner Energy Systems · doi:10.1016/j.cles.2025.100218 · Cited by 7 · see citing works

Wind power generation prediction utilizing Kolmogorov-Arnold networks

Sulaiman, M. H., Mustaffa, Z., Saari, M. M., & Ibrahim, O. (2025) · Engineering Research Express · doi:10.1088/2631-8695/add0f8 · Cited by 6 · see citing works

Machine Learning-Based Predictive Modeling for Solid Oxide Electrolysis Cell (SOEC) Electrochemical Performance

Estrada, N. G. A., & Cervera, R. B. M. (2025) · Applied Sciences · doi:10.3390/app15179388 · Cited by 6 · see citing works

Strengthening Energy Security and Advancing Renewable Energy Adoption Through Supply Chain Digitalization

Alyamani, R., Solangi, Y. A., & Magazzino, C. (2025) · Business Strategy and the Environment · doi:10.1002/bse.70516 · Cited by 6 · see citing works

ML for Sustainable Solutions: Applications in Renewable Energy Optimization and Climate Change Prediction

Awachat, A., Dube, A., & Chaudhri, S. (2025) · International Conference on Sentiment Analysis and Deep Learning (ICSADL) · doi:10.1109/icsadl65848.2025.10933273 · Cited by 6 · see citing works

A Comprehensive Review of AI‐Powered Energy Systems

Razmjoo, A., Asef, P., Javadi, M., Marzband, M., Refaat, S. S., & Rawa, M. (2026) · Energy Science & Engineering · doi:10.1002/ese3.70564 · Cited by 5 · see citing works

Integrating machine learning and IoT in hydrogen production, storage, and distribution for a decarbonized transport future

Bekmyrza, K. Z., Kuterbekov, K. A., Kabyshev, A. M., Kubenova, M. M., Baratova, A. A., Aidarbekov, N. K., & Vincelas, F. F. C. (2025) · International Journal of Low-Carbon Technologies · doi:10.1093/ijlct/ctaf103 · Cited by 4 · see citing works

Neuromorphic energy economics: toward biologically inspired and sustainable power market design

Ye, A., Xu, D., Li, Y., Du, J., Wu, Z., & Tang, J. (2025) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2025.1597038 · Cited by 4 · see citing works

Overview of the Application of Artificial Intelligence in China’s Park-Level Integrated Energy System: Current Status, Challenges, and Future Paths

Tian, S., Li, Q., Qian, F., Zhang, L., & Yang, Y. (2025) · Energies · doi:10.3390/en18205442 · Cited by 3 · see citing works

Hydrogen absorption in metal hydrides: A dual-head tree-based framework for material class classification

Allal, Z., Noura, H., Vernier, F., Salman, O., & Chahine, K. (2026) · International Journal of Thermofluids · doi:10.1016/j.ijft.2025.101521 · Cited by 3 · see citing works

Forecasting and techno-economic optimization of residential PV-battery systems: A synergistic approach for system enhancement

Babu, R. M. (2026) · Next Materials · doi:10.1016/j.nxmate.2026.101968 · Cited by 3 · see citing works

Artificial intelligence in renewable energy technologies: Advancing optimization, integration, and carbon neutrality

Sohail, A. (2026) · Next Energy · doi:10.1016/j.nxener.2026.100575 · Cited by 3 · see citing works

Digital twin and artificial intelligence–driven optimization for renewable-dominated energy systems: a system-level integration framework

Devarajan, Y., Thandavamoorthy, R., Jain, M. V., Bramaramba, V., Samantaray, S., Anand, A., Vichitra, M., & Mehar, K. (2026) · Energy Conversion and Management X · doi:10.1016/j.ecmx.2026.102010 · Cited by 3 · see citing works

Thwarting Cybersecurity Attacks with Explainable Concept Drift

Shaer, I., & Shami, A. A. (2024) · International Wireless Communications and Mobile Computing Conference (IWCMC) · doi:10.1109/iwcmc61514.2024.10592411 · Cited by 3 · see citing works

Meteorological effects on the thermal performance of earth–air heat exchanger: Insights from interpretable Machine learning

Yue, Y., Yan, Z., Ni, P., Lei, F., & Zhang, D. (2025) · Energy and Buildings · doi:10.1016/j.enbuild.2025.116304 · Cited by 3 · see citing works

Optimizing Solar Energy Output Through Automated Dust Detection using CNN-SVM

Mehta, S., & Singh, M. (2024) · International Conference on Smart Electronics and Communication (ICOSEC) · doi:10.1109/icosec61587.2024.10722535 · Cited by 3 · see citing works

The New Power Couple: Artificial Intelligence and Renewable Energy

Mauro, G. (2024) · Journal of Strategic Innovation and Sustainability · doi:10.33423/jsis.v19i3.7374 · Cited by 4 · see citing works

UTILIZING MACHINE LEARNING ALGORITHMS TO IMPROVE THE ACCURACY OF SOLAR ENERGY YIELD PREDICTIONS BASED ON VARIOUS INPUT VARIABLES

Mamun, M. A. A., Newaz, A. A. H., Arafat, Y., Hossain, A., & Melon, M. M. H. (2024) · Nanotechnology Perceptions · doi:10.62441/nano-ntp.vi.3466 · Cited by 3 · see citing works

Application of geographic information system and remote sensing technology in ecosystem services and biodiversity conservation

Khaskheli, M. A., Nizamani, M. M., Laghari, U. A., Laghari, A. H., Khanzada, A., Sundas, F., Khaskheli, R. A., Khaskheli, R. A., Kalhoro, F. A., Nizamani, M. M., et al. (2025) · Elsevier eBooks · doi:10.1016/b978-0-443-24712-5.00006-3 · Cited by 2 · see citing works

Analysis and Comparison of Tree-Based Shallow Learning Methods for Forecasting Solar Energy Production

Gülmez, M., Yüksek, G., & Alkaya, A. (2025) · International Symposium on Innovative Approaches in Smart Technologies (ISAS) · doi:10.1109/isas66241.2025.11101861 · Cited by 2 · see citing works

Multiplicative Seasonality Prophet Model for PV Energy Forecasting and Anomaly Detection

Guarino, S., Buscemi, A., Dio, V. D., & Brano, V. L. (2025) · International Conference on Clean Electrical Power · doi:10.1109/iccep65222.2025.11143727 · Cited by 2 · see citing works

Investigation of flame propagation behavior and heat release characteristics of ammonia/methane/air with pre-chamber jet ignition

Hou, C., Shi, S., Dai, Z., Yu, Y., Zhang, X., Cui, Z., & Tian, J. (2025) · Fuel · doi:10.1016/j.fuel.2025.137035 · Cited by 2 · see citing works

Comprehensive 4E, reliability and availability analyses with multi‑objective optimization of a novel eco‑friendly biogas‑driven multigeneration cycle

Asemani, P., Yari, M., Mahmoudi, S. M. S., & Zare, A. A. D. (2025) · Thermal Science and Engineering Progress · doi:10.1016/j.tsep.2025.104241 · Cited by 2 · see citing works

Simulation, optimization, and machine learning strategies for CH₃NH₃PbBr₃ perovskite solar cells

Fahim, S. R., Sarker, M. S., Piya, M. I., Bhuiyan, J. A., Mamur, H., & Bhuiyan, M. R. A. (2025) · Next Energy · doi:10.1016/j.nxener.2025.100491 · Cited by 2 · see citing works

Developing Immune-Inspired Transport–Energy Integration Systems with Multi-Source Renewable Energy

Zhang, J. (2026) · Urban sustainability · doi:10.1007/978-981-95-5393-8_2 · Cited by 2 · see citing works

BactoRamanBioNet: A Multimodal Neural Network for Bacterial Species Identification Using Raman Spectroscopy and Biological Knowledge

Xu, Y., Song, J., Zhang, Z., Feng, L., Yang, Y., Liang, Y., & Guo, Y. (2026) · Sensors · doi:10.3390/s26061828 · Cited by 2 · see citing works

M3F-TS: multi-modal mutual-guided fusion with temporal-semantic synchronization for time series forecasting

Yan, P., Zheng, S., Huang, Z., Peng, L., Liu, J., & Wang, D. (2026) · Measurement · doi:10.1016/j.measurement.2026.121374 · Cited by 2 · see citing works

Using heat recovered from drain water and exhaust gases to enhance heat pump performance - Numerical study with economic and environmental insights

Murr, R., Faraj, J., Hage, H. E., & Khaled, M. (2025) · International Journal of Thermofluids · doi:10.1016/j.ijft.2025.101214 · Cited by 2 · see citing works

Role of Machine Learning in Climate Change Prediction and Mitigation

Jikar, N., Tale, Y., Tale, A., Barhate, A., Verma, P., & Jikar, A. A. (2025) · International Conference on Machine Learning and Autonomous Systems (ICMLAS) · doi:10.1109/icmlas64557.2025.10968563 · Cited by 2 · see citing works

The Impact of Weather Conditions on Renewable Energy Production: A Machine Learning Approach

Özbay, R., Çelebi, M., & Yavanoğlu, U. (2024) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata62323.2024.10825293 · Cited by 1 · see citing works

First principles study to explore the physical properties of Li-based double perovskite hydrides for hydrogen storage applications

Haider, W., & Ahmad, A. (2026) · Chemical Physics · doi:10.1016/j.chemphys.2026.113221 · Cited by 1 · see citing works

Perspective Chapter: The Role of Machine Learning in Developing Sustainable and Resilient Interconnected Infrastructure Systems

Alkhaleel, B. A. (2025) · Civil engineering. · doi:10.5772/intechopen.1012259 · Cited by 1 · see citing works

Machine learning for fuel cell remaining useful life prediction: A review

Allal, Z., Noura, H., Vernier, F., Salman, O., & Chahine, K. (2026) · Energy Conversion and Management X · doi:10.1016/j.ecmx.2026.101597 · Cited by 1 · see citing works

Optimizing wind power integration: Increasing profitability and market efficiency through machine learning based wind speed forecasting in Japan's Electricity Grid

Dawn, S., Das, S. S., Islam, M. M., & Ustun, T. S. (2026) · Energy Exploration & Exploitation · doi:10.1177/01445987251414449 · Cited by 1 · see citing works

Geothermal drilling technologies: A multidisciplinary review of thermo-mechanical limits, rock–fluid interactions, and next-generation solutions

Mahmoud, A., & Gajbhiye, R. N. (2026) · Geothermics · doi:10.1016/j.geothermics.2026.103618 · Cited by 1 · see citing works

Hybrid LSTM-XGBoost model for energy management optimization of hydrogen-integrated renewable energy system

Hamza, E., Yaden, M. F., & Jalal, S. (2026) · Unconventional Resources · doi:10.1016/j.uncres.2026.100389 · Cited by 1 · see citing works

Use of Machine Learning for Solar Power Generation Prediction in the Field of Alternative Renewable Energy Sources

Parra-Quintero, J. D., Ovalle-Cerquera, D., Chica, E., & Rubio-Clemente, A. (2026) · Technologies · doi:10.3390/technologies14040206 · Cited by 1 · see citing works

A review on intelligent fault detection and mitigation in RES-dominated power systems

Tao, J., Bacha, U., Yigang, H., Gony, H. A. I., & Junaid, M. (2026) · Engineering Research Express · doi:10.1088/2631-8695/ae6021 · Cited by 1 · see citing works

Engineering application of hybrid artificial intelligence for optimizing solar multigeneration systems in university buildings

Assareh, E., Izadyar, N., Mobayen, S., Jamei, E., & Monzavian, M. A. (2026) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2026.114886 · Cited by 1 · see citing works

Advancing Energy Systems with CFD from Renewable Technologies to Nuclear Reactor Safety

Raza, M., Kumail, Z., Nawaz, T., & Uddin, S. Z. (2026) · STEAM-H: Science, Technology, Engineering, Agriculture, Mathematics & Health · doi:10.1007/978-3-032-10804-3_6 · Cited by 1 · see citing works

Artificial intelligence-driven energy storage optimization of renewable energy systems: From standalone to multi-energy systems

Wang, X., Zheng, W., Yang, B., Lyu, J., Bao, F., Liu, Z., & Yin, L. (2026) · Journal of Energy Storage · doi:10.1016/j.est.2026.124445 · Cited by 1 · see citing works

Temperature-Tuned Performance of MXene-NiO Nanocomposites for High-Efficiency SupercapacitorsPreprint

Saeed, T., Iqbal, J., & Ahmed, A. (2025) · Research Square · doi:10.21203/rs.3.rs-7309937/v1 · Cited by 1 · see citing works

Wind Energy Prediction on Future Aspect using XGBoost Time Series Model

Chowdhury, S., Saha, A. K., & Das, D. K. (2024) · International Conference on Electronics, Communication and Aerospace Technology (ICECA) · doi:10.1109/iceca63461.2024.10801065

Federated CNNs for Enhanced Dust Detection and Classification on Solar Panels

Mehta, S., & Sarpal, S. S. (2024) · Asian Conference on Innovation in Technology (ASIANCON) · doi:10.1109/asiancon62057.2024.10838158

Prediction Model for Carbon Emissions in Hebei Province: An Empirical Study Based on Boosting Algorithm

Huo, Y. (2025) · Highlights in Science Engineering and Technology · doi:10.54097/wd8c8r08

Improving the Performance of ZnO Nanorod Piezoelectric Nanogenerators by Tuning the PEDOT: PSS as an Intermediate Layer

Asmarita, Y. D., Fadhullah, N. F., Mufti, N., Pujiarti, H., Paradita, D., Kurniawan, R., Al-Shammari, A. A. S., & Ismail, H. (2025) · Journal of Physics Conference Series · doi:10.1088/1742-6596/2974/1/012021

Integrating Machine Learning for Risk Assessment in Renewable Energy Investments in Developing Economies

Anya, A. A., Anya, K. A., Anya, E. K., & Ishola, A. O. (2025) · Journal of Advanced Research and Multidisciplinary Studies · doi:10.52589/jarms-ki3grjpm

Diesel Exhaust Waste Heat Recovery for Domestic Hot Water: A Parametric and Economic Analysis

Ali, S., Alsayah, A. M., Alshukri, M. J., Faraj, J., Castelain, C., & Khaled, M. (2025) · Journal of Physics Conference Series · doi:10.1088/1742-6596/3028/1/012033

Life Sciences and Environmental Challenges: A Biotechnology Perspective

Hussien, A. S., Hussain, N. N., Mohsin, N. A. K., Jaleel, S. A. A., & Saeed, F. K. (2025) · Bright Sky Publications eBooks · doi:10.62906/bs.book.391

Enhancing Solar Cell Efficiency

Channi, H. K. (2025) · IGI Global eBooks · doi:10.4018/979-8-3373-3226-0.ch006

Machine learning algorithms for policy optimization. Application to energy consumption in manufacturing

Nguyen, V.-H., & Le, T.-T. (2025) · Elsevier eBooks · doi:10.1016/b978-0-443-33971-4.00004-0

Advanced data-driven anomalies detection and diagnosis for cyber-physical energy systems

Noura, H., Allal, Z., Salman, O., & Chehab, A. (2025) · Annals of Telecommunications · doi:10.1007/s12243-025-01123-y

ZA novel carbon-reducing aviation fuel and mechanism for small gas turbine

Wei, W., Li, S., Zhang, J., Yang, Y., Huang, G., Liu, L., Xia, M., Wang, J., Tu, Y., Luo, D., et al. (2025) · Energy Sources Part A Recovery Utilization and Environmental Effects · doi:10.1080/15567036.2025.2601825

Optimizing photovoltaic collector performance: A comprehensive study on the thermal performance of nanofluids

Bouzidi, M., Bailek, N., Nasri, A., Zeroualı, B., El-Kenawy, E.-S. M., Younis, J. A., Urooj, S., & Kuriqi, A. (2025) · AIP Advances · doi:10.1063/5.0285480

Comparison of Historical Weather Measurements and Historical Weather Forecasts as Training Data for Predicting Solar Power Plant Production

Radičević, A., Barac, S., Kerman, D., & Elez, M. (2025) · IEEE PES Innovative Smart Grid Technologies Europe · doi:10.1109/isgteurope64741.2025.11305441

Scalable Solar Energy Prediction Using Machine Learning for Off-Grid Areas

Manna, A., Kar, R., Kolay, R., Karmakar, S., Das, S., Ghosh, P., & Mondal, M. A. (2026) · Lecture notes in networks and systems · doi:10.1007/978-3-032-04539-3_2

Machine learning applications for renewable energy systems

Subbarao, C. C., Nethi, V. R. N., Manikantha, M. R., Srinivas, C., Juttuka, A. S., Thammuluri, R., & Sunil, K. (2026) · AIP conference proceedings · doi:10.1063/5.0341954

Machine learning for renewable energy advancements: Prospects and emerging techniques

Khan, S., Mim, J. J., Shorna, J. F., Hasan, A. M., Tarek, H. R., Islam, M. A., & Hossain, N. (2026) · Energy Reports · doi:10.1016/j.egyr.2025.109008 · Cited by 13 · see citing works

Unlocking renewable energy potential: Harnessing machine learning and intelligent algorithms

Le, T. T., Paramasivam, P., Adril, E., Nguyen, V. Q., Le, M. X., Duong, M. T., Le, H. C., & Nguyen, A. Q. (2024) · International Journal of Renewable Energy Development · doi:10.61435/ijred.2024.60387 · Cited by 12 · see citing works

Leveraging Machine Learning for Prediction and Optimizing Renewable Energy SystemsPreprint

Amin, N. H., & Fard, A. F. (2023) · doi:10.14293/pr2199.000003.v1 · Cited by 1 · see citing works

Research progress and prospects of machine learning applications in renewable energy: a comprehensive bibliometric-based review

Wang, X., Shen, Y., & Su, C. (2024) · International Journal of Environmental Science and Technology · doi:10.1007/s13762-024-06210-6 · Cited by 7 · see citing works