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

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 3,032

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

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

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

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

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

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

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

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

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

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

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 482

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

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 456

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

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

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

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

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 414

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

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 413

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 397

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

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 367

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

Review Article-Renewable Energies

Mohtasham, J. (2015) · Energy Procedia · doi:10.1016/j.egypro.2015.07.774 · Cited by 326

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 322

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

Wind energy resource atlas of the United States

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

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 305

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 301

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 288

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

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 286

2011 IEEE International Conference on Prognostics and Health Management

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

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

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 277

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 262

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

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

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 248

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

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

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 241

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

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 239

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 231

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

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

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 209

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 198

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

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

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

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

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

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

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

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

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 155

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

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

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 143

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

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

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 136

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

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 130

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 127

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

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

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

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

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

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

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

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

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 106

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 106

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

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

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

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 93

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

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 91

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 90

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

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

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

Artificial Intelligence and Economic Growth

Aghion, P., Jones, B. F., & Jones, C. I. (2017) · The Economics of Artificial Intelligence · doi:10.3386/w23928 · Cited by 86

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

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 79

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

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

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 75

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

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

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

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

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 67

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

Wind Farm Modeling with Interpretable Physics-Informed Machine Learning

Howland, M. F., & Dabiri, J. O. (2019) · Energies · doi:10.3390/en12142716 · Cited by 66

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

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

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

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2010 IEEE International Conference on Power and Energy

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

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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

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

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

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