Sustainable energies and machine learning: An organized review of recent applications and challenges

Beyond the hype: Big data concepts, methods, and analytics

Gandomi, A., & Haider, M. (2015) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2014.10.007 · Cited by 4,226 · see citing works

MES (multi-energy systems): An overview of concepts and evaluation models

Mancarella, P. (2013) · Energy · doi:10.1016/j.energy.2013.10.041 · Cited by 1,440 · see citing works

A review and evaluation of the state-of-the-art in PV solar power forecasting: Techniques and optimization

Ahmed, R., Sreeram, V., Mishra, Y., & Arif, M. D. (2020) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2020.109792 · Cited by 1,259 · see citing works

A review of deep learning for renewable energy forecasting

Wang, H., Lei, Z., Zhang, X., Zhou, B., & Peng, J. (2019) · Energy Conversion and Management · doi:10.1016/j.enconman.2019.111799 · Cited by 1,211 · see citing works

A New Typology Design of Performance Metrics to Measure Errors in Machine Learning Regression Algorithms

Botchkarev, A. (2019) · Interdisciplinary Journal of Information Knowledge and Management · doi:10.28945/4184 · Cited by 961 · see citing works

A survey on deep learning methods for power load and renewable energy forecasting in smart microgrids

Aslam, S., Herodotou, H., Mohsin, S. M., Javaid, N., Ashraf, N., & Aslam, S. (2021) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2021.110992 · Cited by 677 · see citing works

A Critical Review of Machine Learning of Energy Materials

Chen, C., Zuo, Y., Ye, W., Li, X., Deng, Z., & Ong, S. P. (2020) · Advanced Energy Materials · doi:10.1002/aenm.201903242 · Cited by 609 · see citing works

A Review of Deep Reinforcement Learning for Smart Building Energy Management

Yu, L., Qin, S., Zhang, M., Shen, C. Q., Jiang, T., & Guan, X. (2021) · IEEE Internet of Things Journal · doi:10.1109/jiot.2021.3078462 · Cited by 532 · see citing works

Selecting appropriate methodological framework for time series data analysis

Shrestha, M. B., & Bhatta, G. R. (2018) · The Journal of Finance and Data Science · doi:10.1016/j.jfds.2017.11.001 · Cited by 525 · see citing works

A deep learning framework for building energy consumption forecast

Somu, N., Raman M R, G., & Ramamritham, K. (2021) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2020.110591 · Cited by 500 · see citing works

Machine learning at the service of meta-heuristics for solving combinatorial optimization problems: A state-of-the-art

Karimi-Mamaghan, M., Mohammadi, M., Meyer, P. E., Karimi-Mamaghan, A. M., & Talbi, E. (2021) · European Journal of Operational Research · doi:10.1016/j.ejor.2021.04.032 · Cited by 466 · 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

Applications of reinforcement learning in energy systems

Perera, A. T. D., & Kamalaruban, P. (2021) · Renewable & Sustainable Energy Reviews · doi:10.1016/j.rser.2020.110618 · Cited by 388 · 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

Optimization problems for machine learning: A survey

Gambella, C., Ghaddar, B., & Naoum‐Sawaya, J. (2020) · European Journal of Operational Research · doi:10.1016/j.ejor.2020.08.045 · Cited by 383 · see citing works

Machine learning in state of health and remaining useful life estimation: Theoretical and technological development in battery degradation modelling

Rauf, H., Khalid, M., & Arshad, N. (2021) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2021.111903 · Cited by 331 · see citing works

Evaluation of a multiple linear regression model and SARIMA model in forecasting heat demand for district heating system

Fang, T., & Lahdelma, R. (2016) · Applied Energy · doi:10.1016/j.apenergy.2016.06.133 · Cited by 325 · see citing works

A review of data mining technologies in building energy systems: Load prediction, pattern identification, fault detection and diagnosis

Zhao, Y., Zhang, C., Zhang, Y., Wang, Z., & Li, J. (2019) · Energy and Built Environment · doi:10.1016/j.enbenv.2019.11.003 · Cited by 303 · see citing works

Prognostics and health management of Lithium-ion battery using deep learning methods: A review

Zhang, Y., & Li, Y. (2022) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2022.112282 · Cited by 297 · see citing works

A review of reinforcement learning based energy management systems for electrified powertrains: Progress, challenge, and potential solution

Ganesh, A. H., & Xu, B. (2021) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2021.111833 · Cited by 290 · see citing works

ANFIS, SVM and ANN soft-computing techniques to estimate daily global solar radiation in a warm sub-humid environment

Quej, V. H., Almorox, J., Arnaldo, J. A., & Saito, L. (2017) · Journal of Atmospheric and Solar-Terrestrial Physics · doi:10.1016/j.jastp.2017.02.002 · 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

Day-ahead photovoltaic power forecasting approach based on deep convolutional neural networks and meta learning

Zang, H., Cheng, L., Ding, T., Cheung, K. W., Wei, Z., & Sun, G. (2019) · International Journal of Electrical Power & Energy Systems · doi:10.1016/j.ijepes.2019.105790 · Cited by 272 · see citing works

Trends and Future Perspective Challenges in Big Data

Naeem, M., Jamal, T., Díaz-Martínez, J., Butt, S. A., Montesano, N., Tariq, M. I., De-La-Hoz-Franco, E., & Valdiris, E. M. D. L. H. (2021) · Smart innovation, systems and technologies · doi:10.1007/978-981-16-5036-9_30 · Cited by 272 · see citing works

A deep learning-based forecasting model for renewable energy scenarios to guide sustainable energy policy: A case study of Korea

Nam, K., Hwangbo, S., & Yoo, C. (2020) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2020.109725 · Cited by 271 · see citing works

Slow pyrolysis as a platform for negative emissions technology: An integration of machine learning models, life cycle assessment, and economic analysis

Cheng, F., Luo, H., & Colosi, L. M. (2020) · Energy Conversion and Management · doi:10.1016/j.enconman.2020.113258 · Cited by 271 · 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

Combined electricity-heat-cooling-gas load forecasting model for integrated energy system based on multi-task learning and least square support vector machine

Tan, Z., De, G., Li, M., Lin, H., Yang, S., Huang, L., & Tan, Q. (2019) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2019.119252 · Cited by 253 · see citing works

A review on global solar radiation prediction with machine learning models in a comprehensive perspective

Zhou, Y., Liu, Y., Wang, D., Liu, X., & Wang, Y. (2021) · Energy Conversion and Management · doi:10.1016/j.enconman.2021.113960 · Cited by 246 · see citing works

Hierarchical stochastic scheduling of multi-community integrated energy systems in uncertain environments via Stackelberg game

Li, Y., Wang, B., Yang, Z., Li, J., & Chen, C. (2021) · Applied Energy · doi:10.1016/j.apenergy.2021.118392 · Cited by 233 · see citing works

Recent evolution of the Mekong Delta and the impacts of dams

Li, X., Liu, P., Saito, Y., & Nguyen, V. L. (2017) · Earth-Science Reviews · doi:10.1016/j.earscirev.2017.10.008 · Cited by 221 · see citing works

How photovoltaics can contribute to GHG emission reductions of 55% in the EU by 2030

Jäger‐Waldau, A., Kougias, I. P., Taylor, N., & Thiel, C. (2020) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2020.109836 · Cited by 211 · see citing works

A comparative study of clustering techniques for electrical load pattern segmentation

Rajabi, A., Eskandari, M., Ghadi, M. J., Li, L., Zhang, J.-F., & Siano, P. (2020) · Renewable & Sustainable Energy Reviews · doi:10.1016/j.rser.2019.109628 · Cited by 192 · see citing works

Machine learning prediction and optimization of bio-oil production from hydrothermal liquefaction of algae

Zhang, W., Li, J., Liu, T., Leng, S., Yang, L., Peng, H., Jiang, S., Zhou, W., Leng, L., & Li, H. (2021) · Bioresource Technology · doi:10.1016/j.biortech.2021.126011 · Cited by 188 · see citing works

Applications of blockchain and artificial intelligence technologies for enabling prosumers in smart grids: A review

Hua, W., Chen, Y., Qadrdan, M., Jiang, J. S., Sun, H., & Wu, J. (2022) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2022.112308 · Cited by 187 · see citing works

Integrated framework of extreme learning machine (ELM) based on improved atom search optimization for short-term wind speed prediction

Hua, L., Zhang, C., Peng, T., Ji, C., & Nazir, M. S. (2021) · Energy Conversion and Management · doi:10.1016/j.enconman.2021.115102 · Cited by 169 · see citing works

Ensemble Approach of Optimized Artificial Neural Networks for Solar Photovoltaic Power Prediction

Al‐Dahidi, S., Ayadi, O., Alrbai, M., & Adeeb, J. (2019) · IEEE Access · doi:10.1109/access.2019.2923905 · Cited by 161 · see citing works

Wind Power Prediction Based on PSO-SVR and Grey Combination Model

Zhang, Y., Sun, H., & Guo, Y. (2019) · IEEE Access · doi:10.1109/access.2019.2942012 · Cited by 161 · see citing works

Recent trends on nanofluid heat transfer machine learning research applied to renewable energy

Ma, T., Guo, Z., Lin, M., & Wang, Q. (2020) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2020.110494 · Cited by 157 · see citing works

Thermal load forecasting in district heating networks using deep learning and advanced feature selection methods

Suryanarayana, G., Lago, J., Geysen, D., Aleksiejuk, P., & Johansson, C. (2018) · Energy · doi:10.1016/j.energy.2018.05.111 · Cited by 155 · see citing works

Prediction of biodiesel production from microalgal oil using Bayesian optimization algorithm-based machine learning approaches

Sultana, N. N., Hossain, S. M. Z., Abusaad, M., Alanbar, N., Senan, Y., & Razzak, S. A. (2021) · Fuel · doi:10.1016/j.fuel.2021.122184 · Cited by 148 · see citing works

A multi-energy load prediction model based on deep multi-task learning and ensemble approach for regional integrated energy systems

Xuan, W., Wang, S., Qianyu, Z., Shaomin, W., & Liwei, F. (2020) · International Journal of Electrical Power & Energy Systems · doi:10.1016/j.ijepes.2020.106583 · Cited by 141 · see citing works

Anomaly detection in wind turbine SCADA data for power curve cleaning

Morrison, R., Liu, X., & Lin, Z. (2021) · Renewable Energy · doi:10.1016/j.renene.2021.11.118 · Cited by 138 · see citing works

Machine learning approach for a circular economy with waste recycling in smart cities

Chen, X. (2022) · Energy Reports · doi:10.1016/j.egyr.2022.01.193 · Cited by 134 · see citing works

Circular Integration of processes, industries, and economies

Walmsley, T. G., Ong, B. H. Y., Klemeš, J. J., Tan, R. R., & Varbanov, P. S. (2019) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2019.03.039 · Cited by 133 · see citing works

Electrical efficiency of the photovoltaic/thermal collectors cooled by nanofluids: Machine learning simulation and optimization by evolutionary algorithm

Cao, Y., Kamrani, E., Mirzaei, S., Khandakar, A. A., & Vaferi, B. (2021) · Energy Reports · doi:10.1016/j.egyr.2021.11.252 · Cited by 132 · 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

Practical issues in implementing machine-learning models for building energy efficiency: Moving beyond obstacles

Wang, Z., Liu, J., Zhang, Y., Yuan, H., Zhang, R., & Srinivasan, R. S. (2021) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2021.110929 · Cited by 129 · see citing works

Machine learning and artificial intelligence in haematology

Shouval, R., Fein, J. A., Savani, B. N., Mohty, M., & Nagler, A. (2020) · British Journal of Haematology · doi:10.1111/bjh.16915 · Cited by 128 · see citing works

Performance prediction, optimal design and operational control of thermal energy storage using artificial intelligence methods

He, Z., Guo, W., & Zhang, P. (2021) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2021.111977 · Cited by 127 · see citing works

Machine Learning for Sustainable Energy Systems

Donti, P. L., & Kolter, J. Z. (2021) · Annual Review of Environment and Resources · doi:10.1146/annurev-environ-020220-061831 · Cited by 123 · see citing works

Wind speed forecasting using deep neural network with feature selection

Liu, X., Zhang, H., Kong, X., & Lee, K. Y. (2020) · Neurocomputing · doi:10.1016/j.neucom.2019.08.108 · Cited by 118 · see citing works

A novel transfer learning approach for wind power prediction based on a serio-parallel deep learning architecture

Yin, H., Ou, Z., Fu, J., Cai, Y., Chen, S., & Meng, A. (2021) · Energy · doi:10.1016/j.energy.2021.121271 · Cited by 118 · see citing works

Sustainable and reliable design of reverse osmosis desalination with hybrid renewable energy systems through supply chain forecasting using recurrent neural networks

Li, Q., Loy-Benitez, J., Nam, K., Hwangbo, S., Rashidi, J., & Yoo, C. (2019) · Energy · doi:10.1016/j.energy.2019.04.114 · Cited by 116 · see citing works

Artificial neural network integrated with thermodynamic equilibrium modeling of downdraft biomass gasification-power production plant

Safarian, S., Saryazdi, S. M. E., Unnþórsson, R., & Richter, C. P. (2020) · Energy · doi:10.1016/j.energy.2020.118800 · Cited by 113 · see citing works

A review of advanced ground source heat pump control: Artificial intelligence for autonomous and adaptive control

Noyé, S., Mulero, R., Carnieletto, L., Carli, M. D., & Castelruiz, A. (2021) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2021.111685 · Cited by 113 · see citing works

Forecasting of consumers heat load in district heating systems using the support vector machine with a discrete wavelet transform algorithm

Protić, M. P., Shamshirband, S., Petković, D., Abbasi, A., Kiah, M. L. M., Unar, J. A., Živković, L. M., & Raos, M. (2015) · Energy · doi:10.1016/j.energy.2015.04.109 · Cited by 109 · see citing works

Review of Ai Applications in Harmonic Analysis in Power Systems

Eslami, A., Negnevitsky, M., Franklin, E., & Lyden, S. (2022) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2021.111897 · Cited by 109 · see citing works

Conditional aggregated probabilistic wind power forecasting based on spatio-temporal correlation

Sun, M., Feng, C., & Zhang, J. (2019) · Applied Energy · doi:10.1016/j.apenergy.2019.113842 · Cited by 108 · see citing works

Optimal Day-Ahead Self-Scheduling and Operation of Prosumer Microgrids Using Hybrid Machine Learning-Based Weather and Load Forecasting

Faraji, J., Ketabi, A., Hashemi‐Dezaki, H., Shafie‐khah, M., & Catalào, J. P. S. (2020) · IEEE Access · doi:10.1109/access.2020.3019562 · Cited by 108 · see citing works

Improving energy efficiency of carbon fiber manufacturing through waste heat recovery: A circular economy approach with machine learning

Khayyam, H., Naebe, M., Milani, A. S., Fakhrhoseini, S. M., Date, A., Shabani, B., Atkiss, S., Ramakrishna, S., Fox, B. L., & Jazar, R. N. (2021) · Energy · doi:10.1016/j.energy.2021.120113 · Cited by 108 · see citing works

Uncertainty estimation for wind energy conversion by probabilistic wind turbine power curve modelling

Yan, J., Zhang, H., Liu, Y., Han, S., & Li, L. (2019) · Applied Energy · doi:10.1016/j.apenergy.2019.01.180 · Cited by 105 · see citing works

Parallel LSTM‐Based Regional Integrated Energy System Multienergy Source‐Load Information Interactive Energy Prediction

Wang, B., Zhang, L., Ma, H., Wang, H., & Wan, S. (2019) · Complexity · doi:10.1155/2019/7414318 · Cited by 102 · see citing works

An adaptive hybrid model for day-ahead photovoltaic output power prediction

Zhang, J., Tan, Z., & Wei, Y. (2019) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2019.118858 · Cited by 101 · see citing works

Multi-energy net load forecasting for integrated local energy systems with heterogeneous prosumers

Zhou, B., Meng, Y., Huang, W., Wang, H., Deng, L., Huang, S., & Wei, J. (2020) · International Journal of Electrical Power & Energy Systems · doi:10.1016/j.ijepes.2020.106542 · Cited by 99 · see citing works

Improving short-term wind power prediction using hybrid improved cuckoo search arithmetic - Support vector regression machine

Li, L., Cen, Z.-Y., Tseng, M., Shen, Q., & Ali, M. H. (2020) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2020.123739 · Cited by 97 · see citing works

The EV-olution of the power system: A spatio-temporal optimisation model to investigate the impact of electric vehicle deployment

Heuberger, C. F., Bains, P., & Dowell, N. M. (2019) · Applied Energy · doi:10.1016/j.apenergy.2019.113715 · Cited by 96 · see citing works

Modelling of municipal solid waste gasification using an optimised ensemble soft computing model

Kardani, N., Zhou, A., Nazem, M., & Lin, X. (2020) · Fuel · doi:10.1016/j.fuel.2020.119903 · Cited by 94 · see citing works

Predicting the effect of bed materials in bubbling fluidized bed gasification using artificial neural networks (ANNs) modeling approach

Serrano, D., Golpour, I., & Sánchez-Delgado, S. (2020) · Fuel · doi:10.1016/j.fuel.2020.117021 · Cited by 93 · see citing works

Prediction of Reservoir Temperatures Using Hydrogeochemical Data, Western Anatolia Geothermal Systems (Turkey): A Machine Learning Approach

Haklıdır, F. S. T., & Haklıdır, M. (2019) · Natural Resources Research · doi:10.1007/s11053-019-09596-0 · Cited by 92 · see citing works

Data-driven reliability assessment method of Integrated Energy Systems based on probabilistic deep learning and Gaussian mixture Model-Hidden Markov Model

Chi, L., Su, H., Zio, E., Qadrdan, M., Li, X., Zhang, L., Fan, L., Zhou, J., Yang, Z., & Zhang, J. (2021) · Renewable Energy · doi:10.1016/j.renene.2021.04.102 · Cited by 92 · see citing works

Temporal and spatial characteristics of wave energy in the Persian Gulf based on the ERA5 reanalysis dataset

Mahmoodi, K., Ghassemi, H., & Razminia, A. (2019) · Energy · doi:10.1016/j.energy.2019.115991 · Cited by 90 · see citing works

Multicarrier Energy Systems: Shaping Our Energy Future

O’Malley, M., Anwar, M. B., Heinen, S., Kober, T., McCalley, J. D., McPherson, M., Muratori, M., Orths, A., Ruth, M. F., Schmidt, T. J., et al. (2020) · Proceedings of the IEEE · doi:10.1109/jproc.2020.2992251 · Cited by 90 · see citing works

Machine learning approaches to rediscovery and optimization of hydrogen storage on porous bio-derived carbon

Rahimi, M., Abbaspour‐Fard, M. H., & Rohani, A. (2021) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2021.129714 · Cited by 88 · see citing works

Wind speed behaviors feather analysis and its utilization on wind speed prediction using 3D-CNN

Zhu, X., Liu, R., Chen, Y., Gao, X., Wang, Y., & Xu, Z. (2021) · Energy · doi:10.1016/j.energy.2021.121523 · Cited by 86 · see citing works

Maximizing energy production from hydropower dams using short-term weather forecasts

Ahmad, S., & Hossain, F. (2019) · Renewable Energy · doi:10.1016/j.renene.2019.07.126 · Cited by 85 · see citing works

Application of complex systems topologies in artificial neural networks optimization: An overview

Kaviani, S., & Sohn, I. (2021) · Expert Systems with Applications · doi:10.1016/j.eswa.2021.115073 · Cited by 84 · see citing works

Machine learning-based utilization of renewable power curtailments under uncertainty by planning of hydrogen systems and battery storages

Shams, M. H., Niaz, H., Na, J., Anvari‐Moghaddam, A., & Liu, J. J. (2021) · Journal of Energy Storage · doi:10.1016/j.est.2021.103010 · Cited by 84 · see citing works

Combined model with secondary decomposition-model selection and sample selection for multi-step wind power forecasting

Wu, Z., Xia, X., Xiao, L., & Liu, Y. (2019) · Applied Energy · doi:10.1016/j.apenergy.2019.114345 · Cited by 82 · see citing works

A novel optimization method combining metaheuristics and machine learning for daily optimal operations in building energy and storage systems

Ikeda, S., & Nagai, T. (2021) · Applied Energy · doi:10.1016/j.apenergy.2021.116716 · Cited by 80 · see citing works

Stochastic multi-carrier energy management in the smart islands using reinforcement learning and unscented transform

Zou, H., Tao, J., Elsayed, S. K., Elattar, E. E., Almalaq, A., & Mohamed, M. A. (2021) · International Journal of Electrical Power & Energy Systems · doi:10.1016/j.ijepes.2021.106988 · Cited by 80 · see citing works

Graph optimization neural network with spatio-temporal correlation learning for multi-node offshore wind speed forecasting

Geng, X., Xu, L., He, X., & Yu, J. (2021) · Renewable Energy · doi:10.1016/j.renene.2021.08.066 · Cited by 80 · see citing works

An Exploratory State-of-the-Art Review of Artificial Intelligence Applications in Circular Economy using Structural Topic Modeling

Agrawal, R., Wankhede, V. A., Kumar, A., V, Luthra, S., Majumdar, A., & Kazançoğlu, Y. (2021) · Operations Management Research · doi:10.1007/s12063-021-00212-0 · Cited by 79 · see citing works

Hydrogen-based self-sustaining integrated renewable electricity network (HySIREN) using a supply-demand forecasting model and deep-learning algorithms

Hwangbo, S., Nam, K., Heo, S., & Yoo, C. (2019) · Energy Conversion and Management · doi:10.1016/j.enconman.2019.02.017 · Cited by 77 · see citing works

Wastewater treatment technology selection under various influent conditions and effluent standards based on life cycle assessment

Zhang, Y., Zhang, C., Qiu, Y., Li, B. J., Pang, H., Xue, Y., Liu, Y., Yuan, Z., & Huang, X. (2019) · Resources Conservation and Recycling · doi:10.1016/j.resconrec.2019.104562 · Cited by 71 · see citing works

Gasification of food waste in supercritical water: An innovative synthesis gas composition prediction model based on Artificial Neural Networks

Shenbagaraj, S., Sharma, P. K., Sharma, A. K., Raghav, G., Kota, K. B., & Ashokkumar, V. (2021) · International Journal of Hydrogen Energy · doi:10.1016/j.ijhydene.2021.01.122 · Cited by 70 · see citing works

Assessment of plant biomass for pellet production using multivariate statistics (PCA and HCA)

Garcia, D. P., Caraschi, J. C., Ventorim, G., Vieira, F. H. A., & Protásio, T. D. P. (2019) · Renewable Energy · doi:10.1016/j.renene.2019.02.103 · Cited by 69 · see citing works

Power forecasting-based coordination dispatch of PV power generation and electric vehicles charging in microgrid

Hao, Y., Dong, L., Liang, J., Liao, X., Wang, L., & Shi, L. (2020) · Renewable Energy · doi:10.1016/j.renene.2020.03.169 · Cited by 69 · see citing works

Tar prediction in bubbling fluidized bed gasification through artificial neural networks

Serrano, D., & Castelló, D. (2020) · Chemical Engineering Journal · doi:10.1016/j.cej.2020.126229 · Cited by 69 · see citing works

Operational Demand Forecasting In District Heating Systems Using Ensembles Of Online Machine Learning Algorithms

Johansson, C., Bergkvist, M., Geysen, D., Somer, O. D., Lavesson, N., & Vanhoudt, D. (2017) · Energy Procedia · doi:10.1016/j.egypro.2017.05.068 · Cited by 66 · see citing works

DIVCLUS-T: A monothetic divisive hierarchical clustering method

Chavent, M., Lechevallier, Y., & Briant, O. (2007) · Computational Statistics & Data Analysis · doi:10.1016/j.csda.2007.03.013 · Cited by 64 · see citing works

Artificial Intelligence-Aided Model Predictive Control for a Grid-Tied Wind-Hydrogen-Fuel Cell System

Chen, X., Cao, W., Zhang, Q., Hu, S., & Zhang, J. (2020) · IEEE Access · doi:10.1109/access.2020.2994577 · Cited by 64 · see citing works

An optimal renewable energy management strategy with and without hydropower using a factor weighted multi-criteria decision making analysis and nation-wide big data - Case study in Iran

Ifaei, P., Faridhosseini, A., & Yoo, C. (2018) · Energy · doi:10.1016/j.energy.2018.06.043 · Cited by 63 · see citing works

High resolution wind speed forecasting based on wavelet decomposed phase space reconstruction and self-organizing map

Hu, R. Q., Hu, W., Gökmen, N., Li, P., Huang, Q. P., & Chen, Z. (2019) · Renewable Energy · doi:10.1016/j.renene.2019.03.041 · Cited by 62 · see citing works

Sparse Bayesian extreme learning machine and its application to biofuel engine performance prediction

Wong, K. I., Vong, C., Wong, P. K., & Luo, J. (2014) · Neurocomputing · doi:10.1016/j.neucom.2013.09.074 · Cited by 60 · see citing works

Design optimization of renewable energy systems for NZEBs based on deep residual learning

Ferrara, M., Santa, F. D., Bilardo, M., Gregorio, A. D., Mastropietro, A., Fugacci, U., Vaccarino, F., & Fabrizio, E. (2021) · Renewable Energy · doi:10.1016/j.renene.2021.05.044 · Cited by 60 · see citing works

Data-driven identification and model predictive control of biomass gasification process for maximum energy production

Elmaz, F., & Yücel, Ö. (2020) · Energy · doi:10.1016/j.energy.2020.117037 · Cited by 57 · see citing works

Wind speed forecasting based on model selection, fuzzy cluster, and multi-objective algorithm and wind energy simulation by Betz's theory

Zhang, S., Wang, C., Liao, P., Ling, X., & Fu, T. (2022) · Expert Systems with Applications · doi:10.1016/j.eswa.2022.116509 · Cited by 57 · see citing works

Online correction predictive energy management strategy using the Q-learning based swarm optimization with fuzzy neural network

Lin, X., Zeng, S., & Li, X. (2021) · Energy · doi:10.1016/j.energy.2021.120071 · Cited by 55 · see citing works

Experiment analysis and computational optimization of the Atkinson cycle gasoline engine through NSGA Ⅱ algorithm using machine learning

Tong, J., Li, Y., Liu, J., Cheng, R., Guan, J., Wang, S., Liu, S., Hu, S., & Guo, T. (2021) · Energy Conversion and Management · doi:10.1016/j.enconman.2021.113871 · Cited by 55 · see citing works

An investigation on deep learning and wavelet transform to nowcast wind power and wind power ramp: A case study in Brazil and Uruguay

Zucatelli, P. J., Nascimento, E. G. S., Santos, A. Á. B., Arce, A. M. G., & Moreira, D. M. (2021) · Energy · doi:10.1016/j.energy.2021.120842 · Cited by 55 · see citing works

A renewable energies-assisted sustainable development plan for Iran using techno-econo-socio-environmental multivariate analysis and big data

Ifaei, P., Karbassi, A., Lee, S., & Yoo, C. (2017) · Energy Conversion and Management · doi:10.1016/j.enconman.2017.10.014 · Cited by 54 · see citing works

A proactive energy-efficient optimal ventilation system using artificial intelligent techniques under outdoor air quality conditions

Nam, K., Heo, S., Li, Q., Loy-Benitez, J., Kim, M. J., Park, D., & Yoo, C. (2020) · Applied Energy · doi:10.1016/j.apenergy.2020.114893 · Cited by 54 · 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

Multiobjective Optimization of Multi-Carrier Energy System Using a Combination of ANFIS and Genetic Algorithms

Kampouropoulos, K., Andrade, F., Sala, E., Espinosa, A. G., & Romeral, L. (2016) · IEEE Transactions on Smart Grid · doi:10.1109/tsg.2016.2609740 · Cited by 52 · see citing works

A novel clustering algorithm based on mathematical morphology for wind power generation prediction

Hao, Y., Dong, L., Liao, X., Liang, J., Wang, L., & Wang, B. (2019) · Renewable Energy · doi:10.1016/j.renene.2019.01.018 · Cited by 51 · see citing works

A short-term solar radiation forecasting system for the Iberian Peninsula. Part 2: Model blending approaches based on machine learning

Huertas‐Tato, J., Aler, R., Galván, I. M., Rodríguez-Benítez, F. J., Arbizu‐Barrena, C., & Pozo‐Vázquez, D. (2019) · Solar Energy · doi:10.1016/j.solener.2019.11.091 · Cited by 51 · see citing works

Nationwide sustainable renewable energy and Power-to-X deployment planning in South Korea assisted with forecasting model

Lim, J. Y., Safder, U., How, B. S., Ifaei, P., & Yoo, C. (2020) · Applied Energy · doi:10.1016/j.apenergy.2020.116302 · Cited by 51 · see citing works

Imputation of missing data from offshore wind farms using spatio-temporal correlation and feature correlation

Sun, C., Chen, Y., & Cheng, C. (2021) · Energy · doi:10.1016/j.energy.2021.120777 · Cited by 51 · see citing works

Dynamic real–time abnormal energy consumption detection and energy efficiency optimization analysis considering uncertainty

Yin, S., Yang, H., Xu, K., Zhu, C., Zhang, S., & Liu, G. (2021) · Applied Energy · doi:10.1016/j.apenergy.2021.118314 · Cited by 47 · see citing works

Day-Ahead Optimization of Prosumer Considering Battery Depreciation and Weather Prediction for Renewable Energy Sources

Faraji, J., Abazari, A., Babaei, M., Muyeen, S. M., & Benbouzid, M. (2020) · Applied Sciences · doi:10.3390/app10082774 · Cited by 46 · see citing works

Review of Clustering Techniques in Control System

Singh, S., & Srivastava, S. (2020) · Procedia Computer Science · doi:10.1016/j.procs.2020.06.032 · Cited by 45 · see citing works

Forming clusters based on strategic partnerships and circular economy for biogas production: A GIS analysis for optimal location

de Jesus, R. H. G., Barros, M. V., Salvador, R., de Souza, J. T., Piekarski, C. M., & de Francisco, A. C. (2021) · Biomass and Bioenergy · doi:10.1016/j.biombioe.2021.106097 · Cited by 45 · see citing works

Smart supply-side management of optimal hydro reservoirs using the water/energy nexus concept: A hydropower pinch analysis

Charmchi, A. S. T., Ifaei, P., & Yoo, C. (2020) · Applied Energy · doi:10.1016/j.apenergy.2020.116136 · Cited by 44 · see citing works

Spatio-temporal wind speed prediction of multiple wind farms using capsule network

Zheng, L., Zhou, B., Or, S. W., Cao, Y., Wang, H., Li, Y., & Chan, K. W. (2021) · Renewable Energy · doi:10.1016/j.renene.2021.05.023 · Cited by 43 · see citing works

A novel asexual-reproduction evolutionary neural network for wind power prediction based on generative adversarial networks

Yin, H., Ou, Z., Zhu, Z., Xu, X., Fan, J., & Meng, A. (2021) · Energy Conversion and Management · doi:10.1016/j.enconman.2021.114714 · Cited by 43 · see citing works

Hybrid model-driven and data-driven control method based on machine learning algorithm in energy hub and application

Cai, Q., Luo, X., Peng, W., Gao, C., & Zhao, P. (2021) · Applied Energy · doi:10.1016/j.apenergy.2021.117913 · Cited by 43 · see citing works

Forecasting the biomass-based energy potential using artificial intelligence and geographic information systems: A case study

Şenocak, A. A., & Gören, H. G. (2021) · Engineering Science and Technology an International Journal · doi:10.1016/j.jestch.2021.04.011 · Cited by 42 · see citing works

A Q-learning based transient power optimization method for organic Rankine cycle waste heat recovery system in heavy duty diesel engine applications

Xu, B., & Li, X. (2021) · Applied Energy · doi:10.1016/j.apenergy.2021.116532 · Cited by 41 · see citing works

Understanding spatio-temporal electricity demand at different urban scales: A data-driven approach

Voulis, N., Warnier, M., & Brazier, F. M. T. (2018) · Applied Energy · doi:10.1016/j.apenergy.2018.08.121 · Cited by 40 · see citing works

An optimal model identification for solid oxide fuel cell based on extreme learning machines optimized by improved Red Fox Optimization algorithm

Zhang, M., Xu, Z., Lu, X., Liu, Y., Xiao, Q., & Taheri, B. (2021) · International Journal of Hydrogen Energy · doi:10.1016/j.ijhydene.2021.06.046 · Cited by 40 · see citing works

Machine learning methods to assist structure design and optimization of Dual Darrieus Wind Turbines

Cheng, B., Du, J., & Yao, Y. (2021) · Energy · doi:10.1016/j.energy.2021.122643 · Cited by 40 · see citing works

Multi-scale smart management of integrated energy systems, Part 2: Weighted multi-objective optimization, multi-criteria decision making, and multi-scale management (3M) methodology

Safder, U., Ifaei, P., & Yoo, C. (2019) · Energy Conversion and Management · doi:10.1016/j.enconman.2019.111830 · Cited by 38 · see citing works

Sustainability insights on emerging solar district heating technologies to boost the nearly zero energy building concept

Abokersh, M. H., Gangwar, S., Spiekman, M., Vallès, M., Jiménez, L., & Boer, D. T. (2021) · Renewable Energy · doi:10.1016/j.renene.2021.08.091 · Cited by 38 · see citing works

A hybrid spatio-temporal forecasting of solar generating resources for grid integration

Nam, S., & Hur, J. (2019) · Energy · doi:10.1016/j.energy.2019.04.127 · Cited by 36 · see citing works

A reinforcement learning based blade twist angle distribution searching method for optimizing wind turbine energy power

Jia, L., Hao, J., Hall, J., Nejadkhaki, H. K., Wang, G., Yan, Y., & Sun, M. (2020) · Energy · doi:10.1016/j.energy.2020.119148 · Cited by 36 · see citing works

Solar farm voltage anomaly detection using high-resolution μ PMU data-driven unsupervised machine learning

Dey, M., Rana, S. P., Simmons, C. V., & Dudley, S. (2021) · Applied Energy · doi:10.1016/j.apenergy.2021.117656 · Cited by 35 · see citing works

A systematic approach of bottom-up assessment methodology for an optimal design of hybrid solar/wind energy resources – Case study at middle east region

Ifaei, P., Karbassi, A., Jácome-Aguirre, G., & Yoo, C. (2017) · Energy Conversion and Management · doi:10.1016/j.enconman.2017.04.097 · Cited by 34 · see citing works

Spatio-temporal modelling of solar photovoltaic adoption: An integrated neural networks and agent-based modelling approach

Peralta, A. A., Balta‐Ozkan, N., & Longhurst, P. (2021) · Applied Energy · doi:10.1016/j.apenergy.2021.117949 · Cited by 34 · see citing works

A data-driven analytical roadmap to a sustainable 2030 in South Korea based on optimal renewable microgrids

Ifaei, P., Tayerani Charmchi, A. S., Loy-Benitez, J., Yang, R. J., & Yoo, C. (2022) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2022.112752 · Cited by 33 · see citing works

A new utility-free circular integration approach for optimal multigeneration from biowaste streams

Ifaei, P., Charmchi, A. S. T., Vilela, P., Safder, U., & Yoo, C. (2022) · Energy Conversion and Management · doi:10.1016/j.enconman.2022.115269 · Cited by 32 · see citing works

An Advanced Multicarrier Residential Energy Hub System Based on Mixed Integer Linear Programming

Yuan, Y., Bayod-Rújula, Á. A., Chen, H., Martínez-Gracia, A., Wang, J., & Pinnarelli, A. (2019) · International Journal of Photoenergy · doi:10.1155/2019/1384985 · Cited by 29 · see citing works

Battery Scheduling in a Residential Multi-Carrier Energy System Using Reinforcement Learning

Mbuwir, B. V., Kaffash, M., & Deconinck, G. (2018) · IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm) · doi:10.1109/smartgridcomm.2018.8587412 · Cited by 27 · see citing works

Adequacy of time-series reduction for renewable energy systems

Göke, L., & Kendziorski, M. (2021) · Energy · doi:10.1016/j.energy.2021.121701 · Cited by 27 · see citing works

Using clustering algorithms to characterise uncertain long-term decarbonisation pathways

Li, P., Pye, S., & Keppo, I. (2020) · Applied Energy · doi:10.1016/j.apenergy.2020.114947 · Cited by 26 · see citing works

A machine learning-driven spatio-temporal vulnerability appraisal based on socio-economic data for COVID-19 impact prevention in the U.S. counties

Moosazadeh, M., Ifaei, P., Charmchi, A. S. T., Asadi, S., & Yoo, C. (2022) · Sustainable Cities and Society · doi:10.1016/j.scs.2022.103990 · Cited by 26 · see citing works

Ensemble solar forecasting and post-processing using dropout neural network and information from neighboring satellite pixels

Yagli, G. M., Yang, D., & Srinivasan, D. (2021) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2021.111909 · Cited by 25 · see citing works

Interval prediction method based on Long-Short Term Memory networks for system integrated of hydro, wind and solar power

Zhang, Z., Qin, H., Yao, L., Lu, J., & Cheng, L. (2019) · Energy Procedia · doi:10.1016/j.egypro.2019.01.491 · Cited by 24 · see citing works

An integrated steam jet ejector power plant for drought adaptation considering water-exergy nexus in an optimal platform

Ifaei, P., Safder, U., Charmchi, A. S. T., & Yoo, C. (2020) · Energy Conversion and Management · doi:10.1016/j.enconman.2020.113622 · Cited by 24 · see citing works

Applying reinforcement learning method to optimize an Energy Hub operation in the smart grid

Rayati, M., Sheikhi, A., & Ranjbar, A. M. (2015) · IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT) · doi:10.1109/isgt.2015.7131906 · Cited by 22 · see citing works

Techno-econo-environmental feasibility of retrofitting urban transportation system with optimal solar panels for climate change mitigation – A case study

Ifaei, P., Khiabani, H., Piran, M. J., & Yoo, C. (2019) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2019.119639 · Cited by 22 · see citing works

Hydrogen consumption prediction of a fuel cell based system with a hybrid intelligent approach

Montero-Sousa, J. A., Aláiz‐Moretón, H., Quintián, H., González-Ayuso, T., Nováis, P., & ROLLE, J. L. C. (2020) · Energy · doi:10.1016/j.energy.2020.117986 · Cited by 22 · see citing works

A novel wind farm equivalent model for high voltage ride through analysis based on multi-view incremental transfer clustering

Han, J., Miao, S., Chen, Z., Liu, Z., Li, Y., Yang, W., Yin, H., & Zhang, D. (2021) · International Journal of Electrical Power & Energy Systems · doi:10.1016/j.ijepes.2021.107527 · Cited by 22 · see citing works

An online driver behavior adaptive shift strategy for two-speed AMT electric vehicle based on dynamic corrected factor

Lin, X., Li, Y., & Xia, B. (2021) · Sustainable Energy Technologies and Assessments · doi:10.1016/j.seta.2021.101598 · Cited by 22 · see citing works

Clustering distributed Energy Storage units for the aggregation of optimized local solar energy

Silva, C., Faria, P., Fernandes, A. M. R., & Vale, Z. (2022) · Energy Reports · doi:10.1016/j.egyr.2022.01.043 · Cited by 21 · see citing works

Revisiting agglomerative clustering

Tokuda, E. K., Comin, C. H., & Costa, L. D. F. (2021) · Physica A Statistical Mechanics and its Applications · doi:10.1016/j.physa.2021.126433 · Cited by 21 · see citing works

Spatio-temporal analysis of wind resource in the Iberian Peninsula with data-coupled clustering

Chidean, M. I., Caamaño, A. J., Ramiro-Bargueno, J., Casanova-Mateo, C., & Salcedo‐Sanz, S. (2017) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2017.06.075 · Cited by 19 · see citing works

Deep learning-based forecasting of aggregated CSP production

Segarra-Tamarit, J., Pérez, E., Moya, E., Ayuso, P., & Beltrán, H. (2020) · Mathematics and Computers in Simulation · doi:10.1016/j.matcom.2020.02.007 · Cited by 19 · see citing works

A review and proposal of (fuzzy) clustering for nonlinearly separable data

Ferraro, M. B., & Giordani, P. (2019) · International Journal of Approximate Reasoning · doi:10.1016/j.ijar.2019.09.004 · Cited by 17 · see citing works

Duckweed growth model for large-scale applications: Optimizing harvesting regime and intrinsic growth rate via machine learning to maximize biomass yields

Calicioglu, Ö., Sengul, M. Y., Femeena, P. V., & Brennan, R. A. (2021) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2021.129120 · Cited by 17 · see citing works

Machine learning aided methods for reducing the dimensionality of the comprehensive energy economy optimization of the fuel cell powertrains

Zhou, X., Sun, F., & Sun, C. (2021) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2021.129250 · Cited by 17 · see citing works

Joint modelling wind speed and power via Bayesian Dynamical models

Duca, V. E. L. A., Fonseca, T. C. O., & Oliveira, F. L. C. (2022) · Energy · doi:10.1016/j.energy.2022.123431 · Cited by 17 · see citing works

Sustainability assessment of energy services using complex multi-layer system models

Coss, S., Clement, R., Verda, V., & Corre, O. L. (2016) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2016.08.134 · Cited by 16 · see citing works

Three-objective optimization of boiler combustion process based on multi-objective teaching–learning based optimization algorithm and ameliorated extreme learning machine

Ma, Y., Wang, H., Zhang, X., Hou, L., & Song, J. (2021) · Machine Learning with Applications · doi:10.1016/j.mlwa.2021.100082 · Cited by 16 · see citing works

Wind power conversion system model identification using adaptive neuro-fuzzy inference systems: A case study

Bilal, B., Adjallah, K. H., Sava, A., Yetilmezsoy, K., & Kıyan, E. (2021) · Energy · doi:10.1016/j.energy.2021.122089 · Cited by 16 · see citing works

The nexus between water, exergy, and economics in an optimal integrated desalination system with two configurations and four operation modes

Charmchi, A. S. T., Ifaei, P., & Yoo, C. (2021) · Energy Conversion and Management · doi:10.1016/j.enconman.2021.114966 · Cited by 16 · see citing works

Battery aging-aware energy management strategy with dual-state feedback for improving life cycle economy by using multi-neural networks learning algorithm

Lin, X., & Zhang, J. (2022) · Journal of Energy Storage · doi:10.1016/j.est.2021.103890 · Cited by 14 · see citing works

Modeling storage systems in electricity markets with high shares of renewable generation: A daily clustering approach

Orgaz, A., Bello, A., & Reneses, J. (2021) · International Journal of Electrical Power & Energy Systems · doi:10.1016/j.ijepes.2021.107706 · Cited by 13 · see citing works

Model-Free Real-Time Autonomous Energy Management for a Residential Multi-Carrier Energy System: A Deep Reinforcement Learning Approach

Ye, Y., Qiu, D., Ward, J., & Abram, M. (2020) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2020/48 · Cited by 12 · see citing works

Efficient prediction of concentrating solar power plant productivity using data clustering

Martinek, J. G., & Wagner, M. J. (2021) · Solar Energy · doi:10.1016/j.solener.2021.06.002 · Cited by 12 · see citing works

Holistic Analysis of Multi-source, Multi-feature Data: Modeling and Computation Challenges

Santra, A., & Bhowmick, S. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-72413-3_4 · Cited by 11 · see citing works

Non-Gaussian multivariate statistical monitoring of spatio-temporal wind speed frequencies to improve wind power quality in South Korea

Heo, S., Lim, J. Y., Chang, R., Shim, Y., Ifaei, P., & Yoo, C. (2022) · Journal of Environmental Management · doi:10.1016/j.jenvman.2022.115516 · Cited by 11 · see citing works

Multi‐objective optimization of integrated gas–electricity energy system based on improved multi‐object cuckoo algorithm

Yang, B. (2021) · Energy Science & Engineering · doi:10.1002/ese3.954 · Cited by 9 · see citing works

Optimal configuration and control strategy in a multi-carrier-energy system using reinforcement learning methods

Bollenbacher, J., & Rhein, B. (2017) · International Energy and Sustainability Conference (IESC) · doi:10.1109/iesc.2017.8167476 · Cited by 8 · see citing works

Predicting Higher Education Outcomes with Hyperbox Machine Learning: What Factors Influence Graduate Employability?

Aviso, K. B., Janairo, J. I. B., Lucas, R. I. G., Promentilla, M. A. B., Yu, D. E. C., & Tan, R. R. (2020) · Chemical engineering transactions · doi:10.3303/cet2081114 · Cited by 7 · see citing works

An Energy Management Strategy of More-Electric Aircraft Based on Fuzzy Neural Network Trained by Dynamic Programming

Huangfu, Y., Shi, W., Xu, L., Zhang, Z., Ren, Z., & Zhuo, S. (2021) · Annual Conference of the IEEE Industrial Electronics Society (IECON) · doi:10.1109/iecon48115.2021.9589360 · Cited by 2 · see citing works

A review of the applications of artificial intelligence in renewable energy systems: An approach-based study

Shoaei, M., Noorollahi, Y., Hajinezhad, A., & Moosavian, S. F. (2024) · Energy Conversion and Management · doi:10.1016/j.enconman.2024.118207 · Cited by 169 · see citing works

Deep reinforcement learning based optimal scheduling of active distribution system considering distributed generation, energy storage and flexible load

Lu, Y., Xiang, Y., Huang, Y., Yu, B., Weng, L., & Liu, J. (2023) · Energy · doi:10.1016/j.energy.2023.127087 · Cited by 105 · see citing works

Computational applications using data driven modeling in process Systems: A review

Bishnu, S. K., Alnouri, S. Y., & Al‐Mohannadi, D. M. (2023) · Digital Chemical Engineering · doi:10.1016/j.dche.2023.100111 · Cited by 78 · see citing works

Photocatalytic degradation of drugs and dyes using a maching learning approach

Anandhi, G., & Iyapparaja, M. (2024) · RSC Advances · doi:10.1039/d4ra00711e · Cited by 76 · see citing works

Darrieus vertical-axis wind turbine performance enhancement approach and optimized design: A review

Shen, Z., Gong, S., Zuo, Z., Chen, Y., & Guo, W. (2024) · Ocean Engineering · doi:10.1016/j.oceaneng.2024.118965 · Cited by 64 · see citing works

Machine learning-driven optimization of biphasic pretreatment conditions for enhanced lignocellulosic biomass fractionation

Madadi, M., Kargaran, E., Azad, S. A., Saleknezhad, M., Zhang, E., & Sun, F. F. (2025) · Energy · doi:10.1016/j.energy.2025.136241 · Cited by 62 · see citing works

Integrating machine learning for the sustainable development of smart cities

Mrabet, M., & Sliti, M. (2024) · Frontiers in Sustainable Cities · doi:10.3389/frsc.2024.1449404 · Cited by 51 · see citing works

Intelligent energy management of microgrids using machine learning: Leveraging random forest models for solar and wind power

Anonto, H. Z., Hossain, M. I., Shufian, A., Sayem, M. S., Shovon, S. M. T. H., Sheikh, P. P., & Alam, S. S. (2025) · Results in Engineering · doi:10.1016/j.rineng.2025.106539 · Cited by 49 · see citing works

Performance prediction and optimization of a hybrid renewable-energy-based multigeneration system using machine learning

Ghandehariun, S., Ghandehariun, A. M., & Ziabari, N. B. (2023) · Energy · doi:10.1016/j.energy.2023.128908 · Cited by 47 · see citing works

Sub-surface geospatial intelligence in carbon capture, utilization and storage: A machine learning approach for offshore storage site selection

Nassabeh, M., You, Z., Keshavarz, A., & Iglauer, S. (2024) · Energy · doi:10.1016/j.energy.2024.132086 · Cited by 47 · see citing works

Breaking new ground: Opportunities and challenges in tunnel boring machine operations with integrated management systems and artificial intelligence

Loy-Benitez, J., Song, M. K., Choi, Y.-H., Lee, J.-K., & Lee, S. S. (2023) · Automation in Construction · doi:10.1016/j.autcon.2023.105199 · Cited by 47 · see citing works

Modeling challenges for Industry 4.0 implementation in new energy systems towards carbon neutrality: Implications for impact assessment policy and practice in emerging economies

Moktadir, M. A., & Ren, J. (2023) · Resources Conservation and Recycling · doi:10.1016/j.resconrec.2023.107246 · Cited by 45 · see citing works

Recent advances in data mining and machine learning for enhanced building energy management

Zhou, X., Du, H., Xue, S., & Ma, Z. (2024) · Energy · doi:10.1016/j.energy.2024.132636 · Cited by 47 · see citing works

Review of Energy-Related Machine Learning Applications in Drying Processes

Đaković, D., Kljajić, M. V., Milivojević, N., Doder, Đ. D., & Anđelković, A. S. (2023) · Energies · doi:10.3390/en17010224 · Cited by 43 · see citing works

An efficient federated transfer learning framework for collaborative monitoring of wind turbines in IoE-enabled wind farms

Wang, L., Fan, W., Jiang, G., & Xie, P. (2023) · Energy · doi:10.1016/j.energy.2023.128518 · Cited by 36 · see citing works

Towards specific cutting energy analysis in the machining of Inconel 601 alloy under sustainable cooling conditions

Korkmaz, M. E., Gupta, M. K., Yılmaz, H., Ross, N. S., Boy, M., Sivalingam, V., Chan, C. K., & Raman, J. (2023) · Journal of Materials Research and Technology · doi:10.1016/j.jmrt.2023.10.192 · Cited by 30 · see citing works

Data-driven enabling technologies in soft sensors of modern internal combustion engines: Perspectives

Li, J., Zhou, Q., He, X., Chen, W., & Xu, H. (2023) · Energy · doi:10.1016/j.energy.2023.127067 · Cited by 28 · see citing works

Hybrid Energy Systems for Buildings: A Techno-Economic-Enviro Systematic Review

Nazari‐Heris, M., Esfehankalateh, A. T., & Ifaei, P. (2023) · Energies · doi:10.3390/en16124725 · Cited by 28 · see citing works

Artificial Intelligence and machine learning in renewable and sustainable energy strategies: a critical review and future perspectivesPreprint

Rane, N. L., Choudhary, S., & Rane, J. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4838761 · Cited by 28 · see citing works

Prediction of the rate of penetration in offshore large-scale cluster extended reach wells drilling based on machine learning and big-data techniques

Chen, X., Weng, C., Du, X., Yang, J., Gao, D., & Wang, R. (2023) · Ocean Engineering · doi:10.1016/j.oceaneng.2023.115404 · Cited by 27 · see citing works

Advances in AI-Driven Biomass Processing: A Review of Conversion Technologies, Optimization Strategies, and Smart Energy Integration

Garg, R., Rajput, P., Vibhandik, J., Ali, A., & Abrar, I. (2025) · ACS Omega · doi:10.1021/acsomega.5c05427 · Cited by 24 · see citing works

Systematic review and cutting-edge applications of prominent heuristic optimizers in sustainable energies

Ifaei, P., Esfehankalateh, A. T., Ghobadi, F., Mohammadi‐Ivatloo, B., & Yoo, C. (2023) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2023.137632 · Cited by 21 · see citing works

Hydrogen-Incorporated Sector-Coupled Smart Grids: A Systematic Review and Future Concepts

Hayati, M. M., Safari, A., Nazari‐Heris, M., & Oshnoei, A. (2024) · Green energy and technology · doi:10.1007/978-3-031-52429-5_2 · Cited by 20 · see citing works

Decoding intersectionality: A systematic review of gender and energy dynamics under the structural and situational effects of contexts

Erten, E. Ü., Güzeloğlu, E. B., Ifaei, P., Khalilpour, K. R., Ifaei, P., & Yoo, C. (2024) · Energy Research & Social Science · doi:10.1016/j.erss.2024.103432 · Cited by 19 · see citing works

Multi-objective optimization of photovoltaic facades in prefabricated academic buildings using transfer learning and genetic algorithms

Chen, Z., Cui, Y., Cai, H., Zheng, H., Ning, Q., & Ding, X. (2025) · Energy · doi:10.1016/j.energy.2025.136470 · Cited by 18 · see citing works

Real-time data assimilation for the thermodynamic modeling of cryogenic storage tanks

Marques, P. A. D. M., Ahizi, S., & Mendez, M. A. (2024) · Energy · doi:10.1016/j.energy.2024.131739 · Cited by 18 · see citing works

A novel wind model downscaling with statistical regression and forecast for the cleaner energy

Chen, H. (2023) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2023.140217 · Cited by 16 · see citing works

A Novel Methodology for Developing an Advanced Energy-Management System

Gheorghiu, C., Scripcariu, M., Tanasiev, G. N., Gheorghe, Ș., & Duong, M. Q. (2024) · Energies · doi:10.3390/en17071605 · Cited by 15 · see citing works

Mobileception-ResNet for transient stability prediction of novel power systems

Yin, L., & Ge, W. (2024) · Energy · doi:10.1016/j.energy.2024.133163 · Cited by 15 · see citing works

Dynamic fusion LSTM-Transformer for prediction in energy harvesting from human motions

Gong, Y., Wang, Y., Xie, Y., Peng, X., Peng, Y., & Zhang, W. (2025) · Energy · doi:10.1016/j.energy.2025.136192 · 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

Evaluating regional sustainable energy potential through hierarchical clustering and machine learning

Azkeskin, S. A., & Aladağ, Z. (2024) · Environmental Research Communications · doi:10.1088/2515-7620/ada2e5 · Cited by 13 · see citing works

Emerging trends in sustainable energy system assessments: integration of machine learning with techno-economic analysis and lifecycle assessment

Zahra, E., Mosalpuri, M., Baltrušaitis, J., Dubey, P., & Wright, M. M. (2025) · Sustainability Science and Technology · doi:10.1088/2977-3504/ada77b · Cited by 13 · see citing works

Prolonged Power Outages and Air Quality: Insights from Quito’s 2023–2024 Energy Crisis

Vallejo, F., Villacrés, P., Yánez, D., Espinoza, L., Poveda, E. M. B., Díaz‐Robles, L. A., Oyaneder, M., Campos, V., Palmay, P., Cordovilla-Pérez, A., et al. (2025) · Atmosphere · doi:10.3390/atmos16030274 · Cited by 13 · see citing works

Reliable operation of an optimal self-scheduling multicarrier system with two alternatives: Multiple renewable utilities or hydrogen facilities

Ba-Alawi, A. H., Ifaei, P., & Yoo, C. (2023) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2023.138463 · Cited by 12 · see citing works

Hybridizing Machine Learning Algorithms With Numerical Models for Accurate Wind Power Forecasting

Abad‐Santjago, Á., Peláez-Rodríguez, C., Pérez‐Aracil, J., Sanz, J., Casanova-Mateo, C., & Salcedo‐Sanz, S. (2025) · Expert Systems · doi:10.1111/exsy.13830 · Cited by 12 · see citing works

The identification of gas–liquid two-phase flow patterns in a centrifugal pump with an imbalanced sample and variable rotational speeds

Li, S., Li, R.-L., He, D., Huang, R., & Sun, S. (2024) · Physics of Fluids · doi:10.1063/5.0197688 · Cited by 10 · see citing works

Golden eagle optimization algorithm embedded in gated Kolmogorov-Arnold network for transient stability preventive control of power systems

Yin, L., Ge, W., & Liu, R. (2025) · Energy · doi:10.1016/j.energy.2025.134931 · Cited by 10 · see citing works

Reinforcement Learning-Driven Control Strategies for DC Flexible Microgrids: Challenges and Future

Shi, J., Xue, W., & Li, K. (2026) · Energies · doi:10.3390/en19030648 · Cited by 7 · see citing works

Enhanced Spatio-Temporal Modeling for Rainfall Forecasting: A High-Resolution Grid Analysis

Alam, N. M., Mitra, S., Pandey, S. K., Jana, C., Ray, M., Ghosh, S., Mazumdar, S. P., Shankar, S. V., Saha, R., & Kar, G. (2024) · Water · doi:10.3390/w16131891 · Cited by 6 · see citing works

Dwelling, Habits, and Possessions: Clustering Turkey’s Household Energy Choices through Responsible Consumption and Poverty

Erten, E. Ü., Esfahani, I. J., Ifaei, P., & Güzeloğlu, E. B. (2023) · Energies · doi:10.3390/en16247983 · Cited by 6 · see citing works

Effect of injection strategies and EGR on combustion characteristics of GDI Atkinson cycle engine

He, T., Fu, J., Shen, Y., Qin, B., & Wei, C. (2025) · Thermal Science and Engineering Progress · doi:10.1016/j.tsep.2025.103544 · Cited by 5 · see citing works

Optimization of biogas production through co-digestion of cafeteria food waste and cow dung using the response surface methodology

Bidiko, G. B., Sangib, E. B., & Gnaro, M. A. (2025) · Frontiers in Energy Research · doi:10.3389/fenrg.2025.1568478 · Cited by 5 · see citing works

Energy forecast for a cogeneration system using dynamic factor models

Alonso, A. M., Sipols, A. E., & Santos-Martín, M. T. (2024) · Computers & Industrial Engineering · doi:10.1016/j.cie.2024.110525 · Cited by 5 · see citing works

Leveraging Information Systems, Big Data Analytics, and AI for Energy-Efficient Design of Rural Residences

Hu, J., & Xu, Z. (2023) · Journal of Information Systems Engineering & Management · doi:10.55267/iadt.07.14037 · Cited by 5 · see citing works

Reinforcement Learning Approaches to PV System Maximum Power Point Tracking Control Under Partial Shading Conditions: A Review

Nwachukwu, S. E., Folly, K. A., Awodele, K. O., & Chen, Q. (2025) · IEEJ Transactions on Power and Energy · doi:10.1541/ieejpes.145.508 · Cited by 4 · see citing works

Entire co-combustion lifecycle of sewage sludge and water lettuce (Pistia stratiotes L.) and its controls over energetic and environmental benefits

Liang, J., Liu, J., Evrendilek, F., Chen, Z., Li, L., Lin, S., Lin, Z., Li, L., Wu, X., Li, W., et al. (2024) · Chemical Engineering Journal · doi:10.1016/j.cej.2024.153955 · Cited by 4 · see citing works

Monitor the energy and carbon emissions of process-based models: ProcessC

Li, Z., Qi, Z., Li, B., Xu, J., Wu, R., Liu, Y., & Smith, W. (2024) · Resources Conservation and Recycling · doi:10.1016/j.resconrec.2024.108101 · Cited by 4 · see citing works

Energy, Exergy, and Economic Analysis and Optimization of a Novel Geothermal Energy-Based Multigeneration System for Liquid Hydrogen, Hot Water, Cooling, and Power Production

Eyvazi, A., Ameri, M., Dehaj, M. S., & Ghaebi, H. (2025) · Process Integration and Optimization for Sustainability · doi:10.1007/s41660-025-00565-0 · Cited by 3 · see citing works

A comparative study of machine learning methods to predict the thermal and energy performance of a small-scale solar chimney

Soares, M. A. F., Patrocínio, Z. K. G., & Maia, C. B. (2025) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2025.113430 · Cited by 3 · see citing works

Thermodynamic and economic analysis of a novel geothermal based multigeneration system integrated with cascade Claude cycle for power, heat, cooling, and liquid hydrogen production

Eyvazi, A., Ameri, M., Dehaj, M. S., & Ghaebi, H. (2025) · Advances in Mechanical Engineering · doi:10.1177/16878132251365041 · Cited by 2 · see citing works

Digital Twin Technology with Machine Learning Algorithms for Reducing Wastage: A Comprehensive Review

Aggarwal, A., Gola, K. K., & Kanauzia, R. (2023) · International Conference on Contemporary Computing and Informatics (IC3I) · doi:10.1109/ic3i59117.2023.10398005 · Cited by 2 · see citing works

Intelligent learning approaches for demand-side controller for BIPV-integrated buildings

Liu, Z., Zhang, L., & Wang, S. (2024) · Elsevier eBooks · doi:10.1016/b978-0-443-15806-3.00007-3 · Cited by 3 · see citing works

Intelligent Speed Advisory System for Optimal Energy Efficiency Based on Ambient Temperature Leveraging Communication Technology and Fuzzy Logic

Othmani, T., Boubaker, S., Rehimi, F., & Alimi, S. E. (2024) · ASU International Conference in Emerging Technologies for Sustainability and Intelligent Systems (ICETSIS) · doi:10.1109/icetsis61505.2024.10459507 · Cited by 2 · see citing works

Characterisation and simulated evaluation of electricity management strategies for Prosumers, Prosumagers, and other end-users

Jiménez, V., González, J., Jímenez, M., Escudero-Atehortúa, A., & Nieto‐Londoño, C. (2024) · Technology Analysis and Strategic Management · doi:10.1080/09537325.2024.2405138 · Cited by 2 · see citing works

Spatiotemporal Renewable Energy Techniques and Applications

Vyas, A., Abimannan, S., Lin, P., & Hwang, R. (2024) · Big data management · doi:10.1007/978-981-99-9651-3_10 · Cited by 2 · see citing works

Enhancing Solar Energy Production Forecasting with Ensemble-based Learning Techniques

Abdelsalam, H. A., Souri, A., & İnanç, N. (2024) · International Conference on Cybernetics and Intelligent System (ICORIS) · doi:10.1109/icoris63540.2024.10903813 · Cited by 1 · see citing works

Real-time prediction of the rate of penetration via computational intelligence: a comparative study on complex lithology in Southwest Iran

Najafi, M., & Shiri, Y. (2025) · Journal of Petroleum Exploration and Production Technology · doi:10.1007/s13202-025-02022-7 · Cited by 1 · see citing works

A novel method for modeling renewable power production using ERA5: Spanish wind energy

Jiménez-Garrote, A., Sánchez-Hernández, G., López-Cuesta, M., Galván, I. M., Aler, R., & Pozo‐Vázquez, D. (2025) · Sustainable Energy Technologies and Assessments · doi:10.1016/j.seta.2025.104397 · Cited by 1 · see citing works

Energy efficiency measures and technologies for net zero

Munguía, N., Esquer, J., Guzman, H. M., Ochoa, M. Y., & Velazquez, L. E. (2025) · Elsevier eBooks · doi:10.1016/b978-0-443-36426-6.00004-6 · Cited by 1 · see citing works

Wind power forecasting using ensemble learning based on coupled multiscale decomposition and regression enhancement

Ma, Z., Zhang, T. A., Zhang, K., Qian, J., & Sun, H. (2026) · Knowledge-Based Systems · doi:10.1016/j.knosys.2026.116655 · Cited by 1 · see citing works

Genetic programming modeling for Cr-contaminated soil remediation by composite cathode displacement

Yu, Q. S., Zhang, L., Zhang, L., Jiang, Z., & Yu, B. (2025) · Process Safety and Environmental Protection · doi:10.1016/j.psep.2025.107540 · Cited by 1 · see citing works

Role of Emerging Technologies in Smart Grids and Power Systems

Kumari, S., & Tyagi, A. K. (2024) · doi:10.1002/9781394303564.ch19 · Cited by 1 · see citing works

MACHINE LEARNING - METHOD OR SOLUTION?

Tymoteusz, M. (2023) · SCIENTIFIC PRACTICE: MODERN AND CLASSICAL RESEARCH METHODS · doi:10.36074/logos-22.12.2023.058

Optimal design of hybrid renewable energy conversion systems using two configurations and four different algorithms for a rural village in Egypt

Torad, M. M., Elbanna, S. H. A., El‐Dabah, M. A., & Diab, A. A. Z. (2026) · Discover Applied Sciences · doi:10.1007/s42452-026-09425-z

A Method for Potential Analysis to Identify Application Scenarios for Machine Learning

Fuchs-Kittowski, F., Schulze, P., Abecker, A., Lachowitzer, J., Loßow, S., Rudolf, H., & Rodner, E. (2025) · Progress in IS · doi:10.1007/978-3-031-85284-8_1

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

Analysis of economic, exergy and energy characteristics of a CaO-absorption-enhanced biomass gasification multi-generation system via machine learning and multi-objective optimizationPreprint

Li, C., Wang, X., Zhao, Y., Guo, R., & Zhang, W. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.6025677

Analysis of economic, exergy and energy characteristics of a CaO‐absorption‐enhanced biomass gasification multi‐generation system via machine learning and multi‐objective optimizationPreprint

Li, C., Wang, X.-H., Zhao, Y., Guo, R., & Zhang, W. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.6485681

Impact of global technological turn and innovations for humanitarian aid and development: A systematic review on the pathways to sustainable development

OKOYE, K. O. (2026) · Social Sciences & Humanities Open · doi:10.1016/j.ssaho.2026.102743 · Cited by 1 · see citing works

Energy Consumption and CO 2 Emissions Forecasting of Transport Sector Using Machine Learning

Akbari, A. H., Hashemi, S. S., & Farajollahi, M. (2026) · Energy Science & Engineering · doi:10.1002/ese3.70545

Machine learning in the renewable energy sector

Azhar, B., Taipabu, M. I., & Avian, C. (2026) · Elsevier eBooks · doi:10.1016/b978-0-443-40312-5.00007-4

Techno-economic assessment and optimization of an environmentally friendly calcium oxide CO2 capture enhanced biomass gasification polygeneration systemPreprint

Li, C., Wang, X.-H., Zhao, Y., Guo, R., & Zhang, W. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.6990050

Cluster-informed forecasting diagnostics for renewable energy communities:Behavioural segmentation and forecast-error concentrationPreprint

islam, M. T. U., & Pereira, M. A. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.7181305

A dynamic deposition–removal-mechanism-constrained neural network for heat exchanger fouling prediction

Guan, X.-T., Gan, S.-K., & Lv, X.-F. (2026) · International Journal of Heat and Mass Transfer · doi:10.1016/j.ijheatmasstransfer.2026.129481

Current density performance prediction for a microbial electrolysis cell using machine learning methods

Rivero-Cacho, A., Botejara-Antúnez, M., Santiago, Ó. B., Kerzenmacher, S., & García‐Sanz‐Calcedo, J. (2026) · Journal of Water Process Engineering · doi:10.1016/j.jwpe.2026.110840

A review of artificial intelligence data-driven methods in building energy management: A comprehensive application-based of recent advances

Ahmadi, A., Saifoddin, A., & Pourfayaz, F. (2026) · Energy Reports · doi:10.1016/j.egyr.2026.109719

Chance-constrained model predictive control with online gaussian process learning reduces battery degradation in vehicle-to-grid service

Cai, K., Ma, Q., & Zhong, C. (2026) · Discover Vehicles · doi:10.1007/s44465-026-00043-y

Machine Learning for Sustainable Energy Systems

Donti, P. L., & Kolter, J. Z. (2021) · Annual Review of Environment and Resources · doi:10.1146/annurev-environ-020220-061831 · Cited by 123 · see citing works

How Sustainable is Machine Learning in Energy Applications? - The Sustainable Machine Learning Balance Sheet

Wenninger, S., Kaymakci, C., Wiethe, C., Römmelt, J., Baur, L., Häckel, B. S., & Sauer, A. (2022) · ERef Bayreuth (University of Bayreuth) · Cited by 6 · see citing works

Machine learning for a sustainable energy future

Oral, B., Çoşgun, A., Kilic, A., Eroğlu, D., Günay, M. E., & Yıldırım, R. (2024) · Chemical Communications · doi:10.1039/d4cc05148c · Cited by 16 · 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

MACHINE LEARNING BASED CONTROL FOR SUSTAINABLE ENERGY MANAGEMENT

Ibragimova, K., & Yuldashev, D. (2024) · Zenodo (CERN European Organization for Nuclear Research) · doi:10.5281/zenodo.10497933