Data-driven probabilistic machine learning in sustainable smart energy/smart energy systems: Key developments, challenges, and future research opportunities in the context of smart grid paradigm
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Lack of explainability impedes debugging and validation.
“impedes debugging and validation [27]”
From Addressing Challenges for the Effective Adoption of Artificial Intelligence in the Energy Sector (2025) · cites this paper as [27]
AI systems may underperform in real-world conditions due to data/concept drift or poor generalization, impacting ROI.
“AI systems may underperform in real-world conditions due to data/concept drift or poor generalization (Section 3.1.5;[27,39]), impacting ROI (Section 3.2.2).”
From Addressing Challenges for the Effective Adoption of Artificial Intelligence in the Energy Sector (2025) · cites this paper as [27]
Overfitting to noisy or unrepresentative data limits real-world utility.
“Overfitting to noisy or unrepresentative data (Section 3.1.1)limits realworld utility [4,27].”
From Addressing Challenges for the Effective Adoption of Artificial Intelligence in the Energy Sector (2025) · cites this paper as [27]
Robustness against data/concept drift requires ongoing MLOps, an operational and strategic undertaking.
“Robustness against data/concept drift requires ongoing MLOps, an operational and strategic undertaking (Section 3.3; [27]).”
From Addressing Challenges for the Effective Adoption of Artificial Intelligence in the Energy Sector (2025) · cites this paper as [27]
AI-driven automation raises concerns about job displacement, necessitating large-scale reskilling/upskilling initiatives which have economic and strategic implications.
“AI-driven automation raises concerns about job displacement, necessitating large-scale reskilling/upskilling initiatives which have economic and strategic implications (Sections 3.2 and 3.3; [13,27,60]).”
From Addressing Challenges for the Effective Adoption of Artificial Intelligence in the Energy Sector (2025) · cites this paper as [27]
Cites
Commentary: The Materials Project: A materials genome approach to accelerating materials innovation
Jain, A., Ong, S. P., Hautier, G., Chen, W., Richards, W. D., Dacek, S., Cholia, S., Gunter, D., Skinner, D., Ceder, G., et al. (2013) · APL Materials · doi:10.1063/1.4812323 · Cited by 13,094 · see citing works
Industry 4.0, digitization, and opportunities for sustainability
Ghobakhloo, M. (2019) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2019.119869 · Cited by 2,298 · see citing works
Artificial intelligence in sustainable energy industry: Status Quo, challenges and opportunities
Ahmad, T., Zhang, D., Huang, C., Zhang, H., Dai, N., Song, Y., & Chen, H. (2021) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2021.125834 · Cited by 1,258 · see citing works
Self-citationMachine learning on big data: Opportunities and challenges
Zhou, L., Pan, S., Wang, J., & Vasilakos, A. V. (2017) · Neurocomputing · doi:10.1016/j.neucom.2017.01.026 · Cited by 1,235 · 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
Nižetić, S., Šolić, P., López–de–Ipiña, D., & Patrono, L. (2020) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2020.122877 · Cited by 1,063 · see citing works
Sensfuß, F., Ragwitz, M., & Genoese, M. (2008) · Energy Policy · doi:10.1016/j.enpol.2008.03.035 · Cited by 972 · see citing works
Reinforcement learning for demand response: A review of algorithms and modeling techniques
Vázquez-Canteli, J. R., & Nagy, Z. (2019) · Applied Energy · doi:10.1016/j.apenergy.2018.11.002 · Cited by 883 · see citing works
Industrial artificial intelligence for industry 4.0-based manufacturing systems
Lee, J., Davari, H., Singh, J., & Pandhare, V. (2018) · Manufacturing Letters · doi:10.1016/j.mfglet.2018.09.002 · Cited by 870 · see citing works
Machine Learning for Predictive Maintenance: A Multiple Classifier Approach
Susto, G. A., Schirru, A., Pampuri, S., McLoone, S., & Beghi, A. (2015) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2014.2349359 · Cited by 763 · see citing works
Machine learning and data mining in manufacturing
Doğan, A., & Birant, D. (2020) · Expert Systems with Applications · doi:10.1016/j.eswa.2020.114060 · Cited by 685 · see citing works
A review of machine learning in building load prediction
Zhang, L., Wen, J., Li, Y., Chen, J., Ye, Y., Fu, Y., & Livingood, W. (2021) · Applied Energy · doi:10.1016/j.apenergy.2021.116452 · Cited by 684 · see citing works
Antonopoulos, I., Robu, V., Couraud, B., Kirli, D., Norbu, S., Kiprakis, A., Flynn, D., Elizondo-Gonzalez, S., & Wattam, S. (2020) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2020.109899 · Cited by 656 · see citing works
Benefits and challenges of electrical demand response: a critical review
O׳Connell, N., Pinson, P., Madsen, H., & O׳Malley, M. (2014) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2014.07.098 · Cited by 636 · 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
Artificial intelligence: the next digital frontier?
Bughin, J., Hazan, E., Ramaswamy, S., Chui, M., Allas, T., Dahlström, P., Henke, N., & Trench, M. (2017) · Cited by 569 · see citing works
Solar photovoltaic energy optimization methods, challenges and issues: A comprehensive review
Al-Shahri, O. A., Ismail, F. B., Hannan, M. A., Lipu, M. S. H., Al-Shetwi, A. Q., Begum, R. A., Al-Muhsen, N. F. O., & Soujeri, E. (2020) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2020.125465 · Cited by 539 · see citing works
Ahmad, T., Zhang, H., & Yan, B. (2020) · Sustainable Cities and Society · doi:10.1016/j.scs.2020.102052 · Cited by 504 · see citing works
Self-citationMohanta, B. K., Jena, D., Satapathy, U., & Patnaik, S. (2020) · Internet of Things · doi:10.1016/j.iot.2020.100227 · Cited by 500 · see citing works
Machine learning assisted materials design and discovery for rechargeable batteries
Liu, Y., Guo, B., Zou, X., Li, Y., & Shi, S. (2020) · Energy storage materials · doi:10.1016/j.ensm.2020.06.033 · Cited by 472 · see citing works
A Robust Regularization Path Algorithm for $\nu $ -Support Vector Classification
Gu, B., & Sheng, V. S. (2016) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2016.2527796 · Cited by 449 · see citing works
Machine learning in energy economics and finance: A review
Ghoddusi, H., Creamer, G. G., & Rafizadeh, N. (2019) · Energy Economics · doi:10.1016/j.eneco.2019.05.006 · Cited by 443 · see citing works
Machine learning driven smart electric power systems: Current trends and new perspectives
Ibrahim, M. S., Dong, W., & Yang, Q. (2020) · Applied Energy · doi:10.1016/j.apenergy.2020.115237 · Cited by 437 · see citing works
Mutezo, G., & Mulopo, J. L. (2020) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2020.110609 · Cited by 433 · see citing works
A Multi-Agent Reinforcement Learning-Based Data-Driven Method for Home Energy Management
Xu, X., Jia, Y., Xu, Y., Xu, Z., Chai, S., & Lai, C. S. (2020) · IEEE Transactions on Smart Grid · doi:10.1109/tsg.2020.2971427 · Cited by 420 · see citing works
Big data analytics in smart grids: a review
Zhang, Y., Huang, T., & Bompard, E. F. (2018) · Energy Informatics · doi:10.1186/s42162-018-0007-5 · Cited by 404 · see citing works
Ferrando, M., Causone, F., Hong, T., & Chen, Y. (2020) · Sustainable Cities and Society · doi:10.1016/j.scs.2020.102408 · Cited by 404 · 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
Gorjian, S., Sharon, H., Ebadi, H., Kant, K., Scavo, F. B., & Tina, G. M. (2020) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2020.124285 · Cited by 386 · see citing works
Fonseca, J. A., Nguyen, T.-A., Schlueter, A., et al. (2015) · Energy and Buildings · doi:10.1016/j.enbuild.2015.11.055 · Cited by 384 · see citing works
Magazzino, C., Mele, M., & Schneider, N. (2020) · Renewable Energy · doi:10.1016/j.renene.2020.11.050 · Cited by 381 · see citing works
Cheng, L., & Yu, T. (2019) · International Journal of Energy Research · doi:10.1002/er.4333 · Cited by 380 · see citing works
Machine Learning and Deep Learning in smart manufacturing: The Smart Grid paradigm
Kotsiopoulos, T., Sarigiannidis, P. G., Ioannidis, D., & Tzovaras, D. K. (2021) · Computer Science Review · doi:10.1016/j.cosrev.2020.100341 · Cited by 360 · see citing works
State-of-the-Art Artificial Intelligence Techniques for Distributed Smart Grids: A Review
Ali, S. S., & Choi, B. J. (2020) · Electronics · doi:10.3390/electronics9061030 · Cited by 359 · see citing works
Using the internet of things in smart energy systems and networks
Ahmad, T., & Zhang, D. (2021) · Sustainable Cities and Society · doi:10.1016/j.scs.2021.102783 · Cited by 332 · see citing works
Self-citationCITYSIM: Comprehensive Micro-Simulation of Resource Flows for Sustainable Urban Planning
Robinson, D., Haldi, F., Leroux, P., Perez, D., Rasheed, A., & Wilke, U. (2009) · Infoscience (Ecole Polytechnique Fédérale de Lausanne) · doi:10.26868/25222708.2009.1083-1090 · Cited by 329 · see citing works
TEASER: an open tool for urban energy modelling of building stocks
Remmen, P., Lauster, M., Mans, M., et al. (2017) · Journal of Building Performance Simulation · doi:10.1080/19401493.2017.1283539 · Cited by 329 · see citing works
Machine learning applications in urban building energy performance forecasting: A systematic review
Fathi, S., Srinivasan, R. S., Fenner, A. E., & Fathi, S. (2020) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2020.110287 · Cited by 317 · see citing works
Machine learning approaches to IoT security: A systematic literature review
Ahmad, R., & Alsmadi, I. M. (2021) · Internet of Things · doi:10.1016/j.iot.2021.100365 · Cited by 303 · see citing works
Bhattarai, B. P., Paudyal, S., Luo, Y., Mohanpurkar, M. U., Cheung, K. W., Tonkoski, R., Hovsapian, R. O., Myers, K. S., Zhang, R., Zhao, P. Q., et al. (2019) · IET Smart Grid · doi:10.1049/iet-stg.2018.0261 · Cited by 292 · see citing works
Challenges and progresses of energy storage technology and its application in power systems
Yao, L., Yang, B., Cui, H., ZHUANG, J., Ye, J., & Xue, J. (2016) · Journal of Modern Power Systems and Clean Energy · doi:10.1007/s40565-016-0248-x · Cited by 281 · see citing works
Mahmud, K., Khan, B., Ravishankar, J., Ahmadi, A., & Siano, P. (2020) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2020.109840 · Cited by 281 · see citing works
Using Bayesian Deep Learning to Capture Uncertainty for Residential Net Load Forecasting
Sun, M., Zhang, T., Wang, Y., Štrbac, G., & Kang, C. (2019) · IEEE Transactions on Power Systems · doi:10.1109/tpwrs.2019.2924294 · Cited by 266 · see citing works
An Optimal and Learning-Based Demand Response and Home Energy Management System
Zhang, D., Li, S., Sun, M., et al. (2016) · IEEE Transactions on Smart Grid · doi:10.1109/tsg.2016.2552169 · Cited by 264 · see citing works
Self-citationSaid, Z., Hachicha, A. A., Aberoumand, S., Yousef, B. A. A., Sayed, E. T., & Bellos, E. (2021) · Progress in Energy and Combustion Science · doi:10.1016/j.pecs.2020.100898 · Cited by 253 · see citing works
Smart manufacturing systems: state of the art and future trends
Qu, Y., Ming, X., Liu, Z., Zhang, X., & Hou, Z. T. (2019) · The International Journal of Advanced Manufacturing Technology · doi:10.1007/s00170-019-03754-7 · Cited by 251 · see citing works
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
A Solar Time Based Analog Ensemble Method for Regional Solar Power Forecasting
Zhang, X., Li, Y., Lu, S., et al. (2018) · IEEE Transactions on Sustainable Energy · doi:10.1109/tste.2018.2832634 · Cited by 228 · see citing works
Machine Learning in Catalysis, From Proposal to Practicing
Yang, W., Fidelis, T. T., & Sun, W. (2019) · ACS Omega · doi:10.1021/acsomega.9b03673 · Cited by 226 · see citing works
Application of machine learning tools for energy efficiency in industry: A review
Narciso, D. A. C., & Martins, F. G. (2020) · Energy Reports · doi:10.1016/j.egyr.2020.04.035 · Cited by 226 · see citing works
Renewable Electricity Futures for the United States
Mai, T., Hand, M. M., Baldwin, S. F., et al. (2014) · IEEE Transactions on Sustainable Energy · doi:10.1109/tste.2013.2290472 · Cited by 223 · see citing works
Machine learning for advanced energy materials
Yun, L., Esan, O. C., Pan, Z., & An, L. (2021) · Energy and AI · doi:10.1016/j.egyai.2021.100049 · Cited by 215 · see citing works
Heffron, R. J., Körner, M.-F., Wagner, J., Weibelzahl, M., & Fridgen, G. (2020) · Applied Energy · doi:10.1016/j.apenergy.2020.115026 · Cited by 212 · see citing works
Demand-Side Management Using Deep Learning for Smart Charging of Electric Vehicles
López, K. L., Gagné, C., & Gardner, M.-A. (2018) · IEEE Transactions on Smart Grid · doi:10.1109/tsg.2018.2808247 · Cited by 211 · see citing works
Dai, X., Liu, J., & Zhang, X. (2020) · Energy and Buildings · doi:10.1016/j.enbuild.2020.110159 · Cited by 204 · see citing works
Amasyali, K., & El-Gohary, N. (2021) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2021.110714 · Cited by 203 · see citing works
Ahmad, T., & Chen, H. (2019) · Sustainable Cities and Society · doi:10.1016/j.scs.2019.102010 · Cited by 202 · see citing works
Self-citationZhou, Y., Zheng, S., Liu, Z., Wen, T., Ding, Z., Yan, J. F., & Zhang, G. (2020) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2020.109889 · Cited by 201 · see citing works
Machine learning toward advanced energy storage devices and systems
Gao, T., & Lu, W. D. (2020) · iScience · doi:10.1016/j.isci.2020.101936 · Cited by 199 · see citing works
Onile, A. E., Machlev, R., Petlenkov, E., Levron, Y., & Belikov, J. (2021) · Energy Reports · doi:10.1016/j.egyr.2021.01.090 · Cited by 191 · see citing works
Hossain Lipu, M. S., Hannan, M. A., Hussain, A., Saad, M. H., Ayob, A., & Uddin, M. N. (2019) · IEEE Transactions on Industry Applications · doi:10.1109/tia.2019.2902532 · Cited by 188 · see citing works
Support-Vector-Machine-Enhanced Markov Model for Short-Term Wind Power Forecast
Yang, L., He, M., Zhang, J., & Vittal, V. (2015) · IEEE Transactions on Sustainable Energy · doi:10.1109/tste.2015.2406814 · Cited by 186 · see citing works
Khodayar, M., Mohammadi, S., Khodayar, M. E., et al. (2019) · IEEE Transactions on Sustainable Energy · doi:10.1109/tste.2019.2897688 · Cited by 185 · see citing works
Smart grid encounters edge computing: opportunities and applications
Feng, C., Wang, Y., Chen, Q., Ding, Y., Štrbac, G., & Kang, C. (2020) · Advances in Applied Energy · doi:10.1016/j.adapen.2020.100006 · Cited by 185 · see citing works
Vasudevan, R. K., Choudhary, K. K., Mehta, A., Smith, R. P., Kusne, A. G., Tavazza, F., Vlček, L., Ziatdinov, M. A., Kalinin, S. V., & Hattrick-Simpers, J. R. (2019) · MRS Communications · doi:10.1557/mrc.2019.95 · Cited by 184 · see citing works
Toyao, T., Suzuki, K., Kikuchi, S., Takakusagi, S., Shimizu, K., & Takigawa, I. (2018) · The Journal of Physical Chemistry C · doi:10.1021/acs.jpcc.7b12670 · Cited by 182 · see citing works
Sun, W., & Zhang, C. (2018) · Applied Energy · doi:10.1016/j.apenergy.2018.09.118 · Cited by 181 · see citing works
Shaukat, N., Ali, S. M., Mehmood, C. A., Khan, M. B., Jawad, M., Farid, U., Ullah, Z., Anwar, S. M., & Majid, M. (2017) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2017.05.208 · Cited by 176 · see citing works
On the assessment and control optimisation of demand response programs in residential buildings
Pallonetto, F., Роса, М. Д., D’Ettorre, F., & Finn, D. (2020) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2020.109861 · Cited by 172 · see citing works
Seyedzadeh, S., Rahimian, F. P., Oliver, S., Rodríguez, S., & Glesk, I. (2020) · Applied Energy · doi:10.1016/j.apenergy.2020.115908 · Cited by 163 · see citing works
Ray, P. K., Mohanty, S. R., Kishor, N., & Catalào, J. P. S. (2013) · IEEE Transactions on Sustainable Energy · doi:10.1109/tste.2013.2278865 · Cited by 159 · see citing works
Li, C., Conejo, A. J., Liu, P., et al. (2021) · European Journal of Operational Research · doi:10.1016/j.ejor.2021.06.024 · Cited by 159 · see citing works
Shi, Z., Yang, W., Deng, X., Cai, C., Yan, Y., Liang, H., Liu, Z., & Qiao, Z. (2020) · Molecular Systems Design & Engineering · doi:10.1039/d0me00005a · Cited by 157 · see citing works
Saraswat, S. K., & Digalwar, A. K. (2021) · Renewable Energy · doi:10.1016/j.renene.2021.02.068 · Cited by 157 · see citing works
India’s potential for integrating solar and on- and offshore wind power into its energy system
Lü, T., Sherman, P. J., Chen, X., Chen, S., Lu, X., & McElroy, M. B. (2020) · Nature Communications · doi:10.1038/s41467-020-18318-7 · Cited by 156 · see citing works
Zhang, W., Maleki, A., & Rosen, M. A. (2019) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2019.117920 · Cited by 151 · see citing works
Energy efficiency of manufacturing systems: A review of energy assessment methods and tools
Menghi, R., Papetti, A., Germani, M., & Marconi, M. (2019) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2019.118276 · Cited by 146 · see citing works
Focus on low carbon technologies: The positive solution
Omer, A. M. (2007) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2007.04.015 · Cited by 144 · see citing works
Big Data Systems Meet Machine Learning Challenges: Towards Big Data Science as a Service
Elshawi, R., Sakr, S., Talia, D., & Paolo Trunfio, A. (2018) · Big Data Research · doi:10.1016/j.bdr.2018.04.004 · Cited by 142 · see citing works
Critical review of machine learning applications in perovskite solar research
Yılmaz, B., & Yıldırım, R. (2020) · Nano Energy · doi:10.1016/j.nanoen.2020.105546 · Cited by 140 · see citing works
Coupling small batteries and PV generation: A review
Gomes, I. S. F., Pérez, Y., & Suomalainen, E. (2020) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2020.109835 · Cited by 136 · see citing works
Rahnama, A., Zepon, G., & Sridhar, S. (2019) · International Journal of Hydrogen Energy · doi:10.1016/j.ijhydene.2019.01.261 · Cited by 135 · see citing works
Machine learning for halide perovskite materials
Zhang, L., He, M., & Shao, S. (2020) · Nano Energy · doi:10.1016/j.nanoen.2020.105380 · Cited by 135 · see citing works
Insights from machine learning of carbon electrodes for electric double layer capacitors
Zhou, M., Gallegos, A., Liu, K., et al. (2019) · Carbon · doi:10.1016/j.carbon.2019.08.090 · Cited by 132 · see citing works
Machine learning analysis on stability of perovskite solar cells
Odabaşı, Ç., & Yıldırım, R. (2019) · Solar Energy Materials and Solar Cells · doi:10.1016/j.solmat.2019.110284 · Cited by 127 · see citing works
Huang, C., Zhang, H., Song, Y., et al. (2021) · IEEE Transactions on Smart Grid · doi:10.1109/tsg.2021.3052515 · Cited by 127 · see citing works
Self-citationHe, Y., Cubuk, E. D., Allendorf, M. D., & Reed, E. J. (2018) · The Journal of Physical Chemistry Letters · doi:10.1021/acs.jpclett.8b01707 · Cited by 123 · 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
Agent-Based Modeling of Retail Electrical Energy Markets With Demand Response
Dehghanpour, K., Nehrir, M. H., Sheppard, J. W., et al. (2016) · IEEE Transactions on Smart Grid · doi:10.1109/tsg.2016.2631453 · Cited by 122 · see citing works
Use machine learning to find energy materials
Luna, P. D., Wei, J. N., Bengio, Y., Aspuru‐Guzik, A., & Sargent, E. H. (2017) · Nature · doi:10.1038/d41586-017-07820-6 · Cited by 121 · see citing works
Agugiaro, G., Benner, J., Cipriano, P., et al. (2018) · Open Geospatial Data Software and Standards · doi:10.1186/s40965-018-0042-y · Cited by 120 · see citing works
Improved Deep Belief Network for Short-Term Load Forecasting Considering Demand-Side Management
Kong, X., Li, C., Zheng, F., & Wang, C. (2019) · IEEE Transactions on Power Systems · doi:10.1109/tpwrs.2019.2943972 · Cited by 118 · see citing works
Assessment of tools for urban energy planning
Ferrari, S., Zagarella, F., Caputo, P., & Bonomolo, M. (2019) · Energy · doi:10.1016/j.energy.2019.04.054 · Cited by 117 · see citing works
Du, P. (2018) · IEEE Transactions on Sustainable Energy · doi:10.1109/tste.2018.2880615 · Cited by 108 · see citing works
Modified deep learning and reinforcement learning for an incentive-based demand response model
Wen, L., Zhou, K., Li, J., & Wang, S. (2020) · Energy · doi:10.1016/j.energy.2020.118019 · Cited by 107 · see citing works
Sustainable design, integration, and operation for energy high-performance process systems
Seferlis, P., Varbanov, P. S., Παπαδόπουλος, Α. Ι., Chin, H. H., & Klemeš, J. J. (2021) · Energy · doi:10.1016/j.energy.2021.120158 · Cited by 103 · see citing works
Battery production design using multi-output machine learning models
Turetskyy, A., Wessel, J., Herrmann, C. A., et al. (2021) · Energy storage materials · doi:10.1016/j.ensm.2021.03.002 · Cited by 99 · see citing works
Forecasting of customer demands for production planning by local k -nearest neighbor models
Kück, M., & Freitag, M. (2020) · International Journal of Production Economics · doi:10.1016/j.ijpe.2020.107837 · Cited by 98 · see citing works
Artificial Intelligence in Materials Modeling and Design
Huang, J., Liew, J. X., Ademiloye, A. S., & Liew, K. M. (2020) · Archives of Computational Methods in Engineering · doi:10.1007/s11831-020-09506-1 · Cited by 98 · see citing works
Bayesian networks in renewable energy systems: A bibliographical survey
Borunda, M., Jaramillo-Salgado, O. A., Reyes, A., & Ibargüengoytia, P. H. (2016) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2016.04.030 · Cited by 94 · see citing works
du Plessis, A. A., Strauss, J. M., & Rix, A. J. (2021) · Applied Energy · doi:10.1016/j.apenergy.2020.116395 · Cited by 89 · see citing works
A Hybrid Ensemble Model for Interval Prediction of Solar Power Output in Ship Onboard Power Systems
Wen, S., Zhang, C., Lan, H. Y., et al. (2019) · IEEE Transactions on Sustainable Energy · doi:10.1109/tste.2019.2963270 · Cited by 88 · see citing works
Patnam, B. S. K., & Pindoriya, N. M. (2020) · Electric Power Systems Research · doi:10.1016/j.epsr.2020.106923 · Cited by 88 · see citing works
Tayal, A., Solanki, A., & Singh, S. P. (2020) · Sustainable Cities and Society · doi:10.1016/j.scs.2020.102383 · Cited by 87 · see citing works
Zhao, N. B., & You, F. (2021) · Advances in Applied Energy · doi:10.1016/j.adapen.2021.100019 · Cited by 84 · see citing works
Guevara, E., Babonneau, F., Homem‐de‐Mello, T., et al. (2020) · Applied Energy · doi:10.1016/j.apenergy.2020.115005 · Cited by 81 · see citing works
Ikeda, S., & Nagai, T. (2021) · Applied Energy · doi:10.1016/j.apenergy.2021.116716 · Cited by 80 · see citing works
Zhao, W. M., Zhang, H., Zheng, J., Dai, Y., Huang, L., Shang, W., & Liang, Y. (2021) · Energy · doi:10.1016/j.energy.2021.120026 · Cited by 77 · see citing works
Machine Learning from Schools about Energy Efficiency
Burlig, F., Knittel, C. R., Rapson, D. S., Reguant, M., & Wolfram, C. (2020) · Journal of the Association of Environmental and Resource Economists · doi:10.1086/710606 · Cited by 76 · see citing works
Donnal, J. S., & Leeb, S. B. (2014) · IEEE Sensors Journal · doi:10.1109/jsen.2014.2359619 · Cited by 71 · see citing works
Enhancing the stability of organic photovoltaics through machine learning
David, T. W., Anizelli, H. S., Jacobsson, T. J., Gray, C., Teahan, W. J., & Kettle, J. (2020) · Nano Energy · doi:10.1016/j.nanoen.2020.105342 · Cited by 68 · see citing works
Larsson, M. R. (2009) · Palgrave Macmillan UK eBooks · doi:10.1057/9780230244092 · Cited by 65 · see citing works
Generation, analysis, and applications of high resolution electricity load profiles in Qatar
Bayram, I. S., Saffouri, F., & Koç, M. (2018) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2018.02.084 · Cited by 63 · see citing works
Kang, Y., Li, L., & Li, B. (2020) · Journal of Energy Chemistry · doi:10.1016/j.jechem.2020.05.044 · Cited by 63 · see citing works
Review of the changing electricity industry value chain in the ICT convergence era
Park, C., & Heo, W. (2020) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2020.120743 · Cited by 62 · see citing works
Haq, E. U., Lyu, X., Jia, Y., et al. (2020) · Energy Reports · doi:10.1016/j.egyr.2020.11.071 · Cited by 61 · see citing works
A Machine Learning-based DSS for mid and long-term company crisis prediction
Perboli, G., & Arabnezhad, E. (2021) · Expert Systems with Applications · doi:10.1016/j.eswa.2021.114758 · Cited by 61 · see citing works
Cyber-enabled grids: Shaping future energy systems
Pong, P. W. T., Annaswamy, A. M., Kroposki, B., Zhang, Y. Y. C., Rajagopal, R., Zussman, G., & Poor, H. V. (2020) · Advances in Applied Energy · doi:10.1016/j.adapen.2020.100003 · Cited by 59 · see citing works
Zhou, Y., Zheng, S., & Zhang, G. (2020) · Energy and Buildings · doi:10.1016/j.enbuild.2020.110013 · Cited by 57 · see citing works
Using Bayesian deep learning approaches for uncertainty-aware building energy surrogate models
Westermann, P., & Evins, R. (2021) · Energy and AI · doi:10.1016/j.egyai.2020.100039 · Cited by 54 · see citing works
Legal framework of marine renewable energy: A review for the Atlantic region of Europe
Ramos, V., Giannini, G., Calheiros-Cabral, T., Rosa-Santos, P., & Pinto, F. T. (2020) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2020.110608 · Cited by 54 · see citing works
Orupattur, N. V., Mushrif, S. H., & Prasad, V. (2020) · Computational Materials Science · doi:10.1016/j.commatsci.2019.109474 · Cited by 53 · see citing works
Publics׳ knowledge, attitudes and behavioral toward the use of solar energy in Yemen power sector
Baharoon, D. A., Rahman, H. A., & Fadhl, S. O. (2016) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2015.12.110 · Cited by 48 · see citing works
Hu, M., Chen, H., Shen, L., et al. (2017) · Energy and Buildings · doi:10.1016/j.enbuild.2017.10.012 · Cited by 47 · see citing works
Wang, Y., Liu, J., & Han, Y. M. (2020) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2020.122824 · Cited by 47 · see citing works
The role of policy in shielding, nurturing and enabling offshore wind in The Netherlands (1973–2013)
Verhees, B., Raven, R. P. J. M., Kern, F., et al. (2015) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2015.02.036 · Cited by 45 · see citing works
Owoyele, O., & Pal, P. (2021) · Applied Energy · doi:10.1016/j.apenergy.2021.116455 · Cited by 45 · see citing works
Zhou, Y., & Zheng, S. (2020) · Renewable Energy · doi:10.1016/j.renene.2020.01.133 · Cited by 42 · see citing works
Tayal, D. (2017) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2017.07.040 · Cited by 38 · see citing works
Power and energy measurement devices: A review, comparison, discussion, and the future of research
Babuta, A., Gupta, B., Kumar, A., et al. (2021) · Measurement · doi:10.1016/j.measurement.2020.108961 · Cited by 37 · see citing works
Pu, B., Nan, F., Zhu, N., et al. (2021) · Energy Reports · doi:10.1016/j.egyr.2021.09.105 · Cited by 31 · see citing works
Online System for Grid Resource Monitoring and Machine Learning-Based Prediction
Hu, L. Q., Che, X., & Zheng, S.-Q. (2011) · IEEE Transactions on Parallel and Distributed Systems · doi:10.1109/tpds.2011.108 · Cited by 29 · see citing works
Sharmila, P., Baskaran, J., Nayanatara, C., & Maheswari, R. U. (2019) · Procedia Computer Science · doi:10.1016/j.procs.2020.01.076 · Cited by 28 · see citing works
Tavana, M., Shaabani, A., Santos‐Arteaga, F. J., et al. (2020) · Energies · doi:10.3390/en13153947 · Cited by 28 · see citing works
Germany’s renewable revolution awaits energy forecast
Schiermeier, Q. (2016) · Nature · doi:10.1038/535212a · Cited by 27 · see citing works
Network State-Based Algorithm Selection for Power Flow Management Using Machine Learning
King, J. E., Jupe, S. C. E., & Taylor, P. (2015) · IEEE Transactions on Power Systems · doi:10.1109/tpwrs.2014.2361792 · Cited by 26 · see citing works
Machine learning for energy projections
McCollum, D. L. (2021) · Nature Energy · doi:10.1038/s41560-021-00779-9 · Cited by 26 · see citing works
Terrén-Serrano, G., & Martínez‐Ramón, M. (2021) · Applied Energy · doi:10.1016/j.apenergy.2021.116656 · Cited by 26 · see citing works
Nisar, A., Ruiz, F., & Palacios, M. (2013) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2013.01.039 · Cited by 22 · see citing works
Artificial neural networks controlled fast valving in a power generation plant
Han, Y., Xiu, L., Wang, Z., et al. (1997) · IEEE Transactions on Neural Networks · doi:10.1109/72.557689 · Cited by 19 · see citing works
Xu, S., Wu, W., Yang, Y., et al. (2021) · IET Renewable Power Generation · doi:10.1049/rpg2.12163 · Cited by 18 · see citing works
Liukkonen, M., & Hiltunen, T. (2014) · Energy · doi:10.1016/j.energy.2014.06.034 · Cited by 17 · see citing works
Kawase, H., Mori, Y., Hasegawa, H., et al. (2016) · IEEE Transactions on Network and Service Management · doi:10.1109/tnsm.2016.2605640 · Cited by 10 · see citing works
Kell, A., McGough, A. S., & Forshaw, M. (2021) · Sustainable Computing Informatics and Systems · doi:10.1016/j.suscom.2021.100532 · Cited by 10 · see citing works
Power outage prediction by using logistic regression and decision tree
Saidi, A. Y. N., Ramli, N. A., Muhammad, N., et al. (2021) · Journal of Physics Conference Series · doi:10.1088/1742-6596/1988/1/012039 · Cited by 10 · see citing works
Zhu, K., Liu, X., & Pong, P. W. T. (2018) · Proceedings of IEEE Sensors/Proceedings of IEEE Sensors ... · doi:10.1109/icsens.2018.8589880 · Cited by 9 · see citing works
Multiscale Modeling in Systems Biology: Methods and Perspectives
Coulier, A. (2021) · Digital comprehensive summaries of Uppsala dissertations from the Faculty of Science and Technology · doi:10.33063/diva-442412 · Cited by 6 · see citing works
Traditional power markets and an evolution to smart grids
Striełkowski, W. (2019) · Elsevier eBooks · doi:10.1016/b978-0-12-817770-9.00002-x · Cited by 3 · see citing works
Consumers, prosumers, and the smart grids
Striełkowski, W. (2019) · Elsevier eBooks · doi:10.1016/b978-0-12-817770-9.00006-7 · Cited by 2 · see citing works
Artificial Intelligence in the Energy World—Getting the Act Together
Gupta, V., & Bali, R. (2021) · Lecture notes in electrical engineering · doi:10.1007/978-981-15-8297-4_63 · Cited by 1 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Bin Abu Sofian, A. D. A., Lim, H. R., Siti Halimatul Munawaroh, H., Ma, Z., Chew, K. W., & Show, P. L. (2024) · Sustainable Development · doi:10.1002/sd.2885 · Cited by 270 · see citing works
Strielkowski, W., Vlasov, A., Selivanov, K., Muraviev, K., & Shakhnov, V. (2023) · Energies · doi:10.3390/en16104025 · Cited by 237 · see citing works
Alshdaifat, S. M., Aziz, N. H. A., Alhasnawi, M. Y., Alharasis, E. E., Al Qadi, F., & Al Amosh, H. (2024) · Journal of Risk and Financial Management · doi:10.3390/jrfm17110509 · Cited by 68 · see citing works
A systematic review of current AI techniques used in the context of the SDGs
Greif, L., Röckel, F., Kimmig, A., & Ovtcharova, J. (2025) · International Journal of Environmental Research · doi:10.1007/s41742-024-00668-5 · Cited by 55 · see citing works
- Authors
- Ahmad, T., Madonski, R., Zhang, D., Huang, C., & Mujeeb, A.
- Affiliations
- Jinan University (CN)
- Guangxi University (CN)
- University of Science and Technology Beijing (CN)
- Tsinghua University (CN)
- Corresponding author
- Ahmad, Tanveer
- Venue
- Renewable and Sustainable Energy Reviews
- Publisher
- Elsevier BV
- Published
- 2022-05
- Volume
- 160
- Pages
- 112128
- ISSN
- 1364-0321 (ISSN-L)
- OpenAlex
- W4220842087
- Cited by
- 711
- References
- 150
- Citations by year
- 2026: 104 · 2025: 280 · 2024: 212 · 2023: 92 · 2022: 23
- FWCI
- 41.57
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- Renewable and Sustainable Energy Reviews (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Topics
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Smart Grid and Power Systems
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Smart Grid Energy Management
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Energy Load and Power Forecasting
- Keywords
- probabilistic machine learning, smart grid, energy distribution networks, energy forecasting, energy price forecasting, merit order, renewable energy integration, energy storage, energy efficiency, energy materials, big data analytics, cybersecurity, distributed energy resources, power transmission, energy planning, load forecasting
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review