Artificial intelligence powered large-scale renewable integrations in multi-energy systems for carbon neutrality transition: Challenges and future perspectives
Open accessAbstract
The vigorous expansion of renewable energy as a substitute for fossil energy is the predominant route of action to achieve worldwide carbon neutrality. However, clean energy supplies in multi-energy building districts are still at the preliminary stages for energy paradigm transitions. In particular, technologies and methodologies for large-scale renewable energy integrations are still not sufficiently sophisticated, in terms of intelligent control management. Artificial intelligent (AI) techniques powered renewable energy systems can learn from bio-inspired lessons and provide power systems with intelligence. However, there are few in-depth dissections and deliberations on the roles of AI techniques for large-scale integrations of renewable energy and decarbonisation in multi-energy systems. This study summarizes the commonly used AI-related approaches and discusses their functional advantages when being applied in various renewable energy sectors, as well as their functional contribution to optimizing the operational control modalities of renewable energy and improving the overall operational effectiveness. This study also presents practical applications of various AI techniques in large-scale renewable energy integration systems, and analyzes their effectiveness through theoretical explanations and diverse case studies. In addition, this study introduces limitations and challenges associated with the large-scale renewable energy integrations for carbon neutrality transition using relevant AI techniques, and proposes further promising research perspectives and recommendations. This comprehensive review ignites advanced AI techniques for large-scale renewable integrations and provides valuable informational instructions and guidelines to different stakeholders (e.g., engineers, designers and scientists) for carbon neutrality transition.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
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?
As AI applications mature within specific domains, the challenge of coordinating across multiple energy vectors (e.g., electricity, gas, heat) has emerged as a key frontier.
“Concurrently, as AI applications mature within specific domains, the challenge of coordinating across multiple energy vectors (e.g., electricity, gas, heat) has emerged as a key frontier [36,38].”
From Addressing Challenges for the Effective Adoption of Artificial Intelligence in the Energy Sector (2025) · cites this paper as [38]
Multi-energy coordination involves high modeling complexity due to diverse constraints and dynamics, overcoming data fragmentation from historically siloed operations, addressing interoperability deficits, and navigating regulatory/market designs often ill-suited for cross-sector optimization.
“However, this involves high modeling complexity due to diverse constraints and dynamics [35], overcoming data fragmentation from historically siloed operations ( [23]; linking to policy/governance, Section 3.2.3),addressing interoperability deficits [33], and navigating regulatory/market designs often ill-suited for cross-sector optimization [38].”
From Addressing Challenges for the Effective Adoption of Artificial Intelligence in the Energy Sector (2025) · cites this paper as [38]
AI offers potential for optimizing increasingly integrated multi-energy systems (electricity, heat, gas, hydrogen).
“AI offers potential for optimizing increasingly integrated multi-energy systems (electricity, heat, gas, hydrogen) [37,38].”
From Addressing Challenges for the Effective Adoption of Artificial Intelligence in the Energy Sector (2025) · cites this paper as [38]
AI in critical energy decisions raises profound ethical concerns regarding algorithmic bias, data privacy, and accountability.
“AI in critical energy decisions raises profound ethical concerns regarding algorithmic bias, data privacy, and accountability [38,62].”
From Addressing Challenges for the Effective Adoption of Artificial Intelligence in the Energy Sector (2025) · cites this paper as [38]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
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
Kebede, A. A., Kalogiannis, T., Mierlo, J. V., & Berecibar, M. (2022) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2022.112213 · Cited by 1,249 · see citing works
Artificial neural networks in renewable energy systems applications: a review
Kalogirou, S. A. (2001) · Renewable and Sustainable Energy Reviews · doi:10.1016/s1364-0321(01)00006-5 · Cited by 1,165 · see citing works
Grid-connected photovoltaic power systems: Technical and potential problems—A review
Eltawil, M., & Zhao, Z.-M. (2010) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2009.07.015 · Cited by 1,081 · see citing works
A review on time series forecasting techniques for building energy consumption
Deb, C., Zhang, F., Yang, J., Lee, S. E., & Shah, K. W. (2017) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2017.02.085 · Cited by 1,032 · see citing works
A review of wind speed and wind power forecasting with deep neural networks
Wang, Y., Zou, R., Liu, F., Zhang, L., & Liu, Q. (2021) · Applied Energy · doi:10.1016/j.apenergy.2021.117766 · Cited by 974 · see citing works
Tan, K. M., Ramachandaramurthy, V. K., & Yong, J. Y. (2015) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2015.09.012 · Cited by 882 · see citing works
Modeling and forecasting building energy consumption: A review of data-driven techniques
Bourdeau, M., Zhai, X. Q., Nefzaoui, E., Guo, X., & Chatellier, P. (2019) · Sustainable Cities and Society · doi:10.1016/j.scs.2019.101533 · Cited by 819 · see citing works
A review of computational optimisation methods applied to sustainable building design
Evins, R. (2013) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2013.02.004 · Cited by 786 · see citing works
Wang, Z., & Srinivasan, R. S. (2016) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2016.10.079 · Cited by 694 · see citing works
Al-Falahi, M. D. A., Jayasinghe, S. G., & Enshaei, H. (2017) · Energy Conversion and Management · doi:10.1016/j.enconman.2017.04.019 · Cited by 601 · see citing works
Applications of fuzzy logic in renewable energy systems – A review
Suganthi, L., Iniyan, S., & Samuel, A. A. (2015) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2015.04.037 · Cited by 539 · see citing works
A review of the-state-of-the-art in data-driven approaches for building energy prediction
Sun, Y., Haghighat, F., & Fung, B. C. M. (2020) · Energy and Buildings · doi:10.1016/j.enbuild.2020.110022 · Cited by 511 · see citing works
Self-citationBasak, P., Chowdhury, S., Dey, S. H. N., & Chowdhury, S. (2012) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2012.05.043 · Cited by 508 · 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
Mohanraj, M. P., Jayaraj, S., & Muraleedharan, C. (2011) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2011.10.015 · Cited by 422 · see citing works
Abdalla, A. N., Nazir, M. S., Hai, T., Cao, S., Ji, R., Jiang, M., & Liu, Y. (2021) · Journal of Energy Storage · doi:10.1016/j.est.2021.102811 · Cited by 421 · see citing works
Xia, M., Shao, H., Ma, X., & de Silva, C. W. (2021) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2021.3056867 · Cited by 420 · see citing works
Cubric, M. (2020) · Technology in Society · doi:10.1016/j.techsoc.2020.101257 · Cited by 416 · see citing works
Mayer, M. J., Szilágyi, A., & Gróf, G. (2020) · Applied Energy · doi:10.1016/j.apenergy.2020.115058 · Cited by 409 · see citing works
Energetics Systems and artificial intelligence: Applications of industry 4.0
Ahmad, T., Zhu, H., Zhang, D., Tariq, R., Bassam, A., Ullah, F., AlGhamdi, A. S., & Alshamrani, S. S. (2021) · Energy Reports · doi:10.1016/j.egyr.2021.11.256 · Cited by 399 · see citing works
Zhang, W., Maleki, A., Rosen, M. A., & Liu, J. (2018) · Energy · doi:10.1016/j.energy.2018.08.112 · Cited by 394 · see citing works
Self-citationCheng, L., & Yu, T. (2019) · International Journal of Energy Research · doi:10.1002/er.4333 · Cited by 380 · see citing works
A review of particle swarm optimization and its applications in Solar Photovoltaic system
Khare, A., & Rangnekar, S. (2012) · Applied Soft Computing · doi:10.1016/j.asoc.2012.11.033 · Cited by 367 · see citing works
Brouwer, A. S., van den Broek, M. A., Seebregts, A. J., & Faaij, A. P. C. (2014) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2014.01.076 · Cited by 352 · see citing works
Shi, Z., Yao, W., Li, Z., Zeng, L., Zhao, Y., Zhang, R., Tang, Y., & Wen, J. (2020) · Applied Energy · doi:10.1016/j.apenergy.2020.115733 · Cited by 327 · see citing works
Application of Artificial Intelligence Methods for Hybrid Energy System Optimization
Zahraee, S. M., Assadi, M. K., & Saidur, R. (2016) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2016.08.028 · Cited by 318 · see citing works
Cai, Y., Huang, G., Yang, Z. F., & Tan, Q. (2008) · Applied Energy · doi:10.1016/j.apenergy.2008.09.025 · Cited by 303 · see citing works
A review of strategic charging–discharging control of grid-connected electric vehicles
Solanke, T. U., Ramachandaramurthy, V. K., Yong, J. Y., Pasupuleti, J., Kasinathan, P., & Rajagopalan, A. (2020) · Journal of Energy Storage · doi:10.1016/j.est.2020.101193 · Cited by 302 · see citing works
Serban, A. C., & Lytras, M. D. (2020) · IEEE Access · doi:10.1109/access.2020.2990123 · Cited by 300 · see citing works
Review and prospect of data-driven techniques for load forecasting in integrated energy systems
Zhu, J., Dong, H., Zheng, W., Li, S., Huang, Y., & Xi, L. (2022) · Applied Energy · doi:10.1016/j.apenergy.2022.119269 · Cited by 300 · see citing works
Choudhury, S. (2022) · Journal of Energy Storage · doi:10.1016/j.est.2022.103966 · Cited by 288 · see citing works
Prediction of short-term PV power output and uncertainty analysis
Liu, L., Zhao, Y., Chang, D., Xie, J., Ma, Z., Sun, Q., Yin, H., & Wennersten, R. (2018) · Applied Energy · doi:10.1016/j.apenergy.2018.06.112 · Cited by 281 · see citing works
Al–Othman, A., Tawalbeh, M., Martis, R., Dhou, S., Orhan, M. F., Qasim, M., & Olabi, A. G. (2021) · Energy Conversion and Management · doi:10.1016/j.enconman.2021.115154 · Cited by 269 · see citing works
Güven, A. F., Yörükeren, N., & Samy, M. M. (2022) · Energy · doi:10.1016/j.energy.2022.124089 · Cited by 267 · see citing works
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
Wei, N., Li, C., Peng, X., Zeng, F. B., & Lu, X. (2019) · Journal of Petroleum Science and Engineering · doi:10.1016/j.petrol.2019.106187 · Cited by 260 · see citing works
Greener energy: Issues and challenges for Pakistan—Solar energy prospective
Bhutto, A. W., Bazmi, A. A., & Zahedi, G. (2012) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2012.02.043 · Cited by 250 · see citing works
Design of wind farm layout using ant colony algorithm
Eroğlu, Y., & Seçkiner, S. U. (2012) · Renewable Energy · doi:10.1016/j.renene.2011.12.013 · Cited by 244 · see citing works
Alam, S., Dobbie, G., Koh, Y. S., Riddle, P., & Rehman, S. U. (2014) · Swarm and Evolutionary Computation · doi:10.1016/j.swevo.2014.02.001 · Cited by 243 · see citing works
Potential challenges of integrating large-scale wind energy into the power grid–A review
Shafiullah, G., Oo, A. M. T., Ali, A. B. M. S., & Wolfs, P. (2013) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2012.11.057 · Cited by 239 · see citing works
A Survey of Machine Learning Models in Renewable Energy Predictions
Lai, J.-P., Chang, Y.-M., Chen, C.-H., & Pai, P. (2020) · Applied Sciences · doi:10.3390/app10175975 · Cited by 223 · see citing works
A review on the complementarity between grid-connected solar and wind power systems
Weschenfelder, F., Leite, G. D. N. P., da Costa, A. C. A., Vilela, O. D. C., Ribeiro, C. M., Ochoa, Á. A. V., & Araújo, A. M. (2020) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2020.120617 · Cited by 223 · see citing works
Boza, P., & Evgeniou, T. (2021) · Applied Energy · doi:10.1016/j.apenergy.2021.116754 · Cited by 221 · see citing works
Nadjemi, O., Nacer, T., Hamidat, A., & Salhi, H. (2016) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2016.12.038 · Cited by 220 · see citing works
Kermani, M., Adelmanesh, B., Shirdare, E., Sima, C. A., Carnì, D. L., & Martirano, L. (2021) · Renewable Energy · doi:10.1016/j.renene.2021.03.008 · Cited by 211 · see citing works
Hannan, M. A., Al-Shetwi, A. Q., Ker, P. J., Begum, R. A., Mansor, M., Rahman, M. S. A., Dong, Z. Y., Kiong, T. S., Mahlia, T. M. I., & Muttaqi, K. M. (2021) · Energy Reports · doi:10.1016/j.egyr.2021.08.172 · Cited by 208 · see citing works
Fetanat, A., & Khorasaninejad, E. (2015) · Applied Soft Computing · doi:10.1016/j.asoc.2015.02.047 · Cited by 201 · see citing works
Zhou, 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
Self-citationNasim, G., Gheibi, M., Zahra, K., Faramarz, M. G., Naghedi, R., Eftekhari, M., Fathollahi‐Fard, A. M., Dulebenets, M. A., & Tian, G. (2021) · Sustainable Cities and Society · doi:10.1016/j.scs.2021.103149 · Cited by 194 · see citing works
Yu, F., Wang, L., & Li, X. (2020) · Energy Policy · doi:10.1016/j.enpol.2020.111463 · Cited by 192 · see citing works
An Intelligent Load Management System With Renewable Energy Integration for Smart Homes
Javaid, N., Ullah, I., Akbar, M., Iqbal, Z., Khan, F. A., Alrajeh, N. A., & Alabed, M. S. (2017) · IEEE Access · doi:10.1109/access.2017.2715225 · Cited by 190 · see citing works
Renewable energy prediction: A novel short-term prediction model of photovoltaic output power
Li, L.-L., Wen, S.-Y., Tseng, M., & Wang, C. (2019) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2019.04.331 · Cited by 190 · see citing works
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
Memon, M. A., Mekhilef, S., Mubin, M. B., & Aamir, M. (2017) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2017.08.068 · Cited by 186 · see citing works
The role of artificial intelligence in the mass adoption of electric vehicles
Ahmed, M., Zheng, Y. H., Amine, A., Fathiannasab, H., & Chen, Z. (2021) · Joule · doi:10.1016/j.joule.2021.07.012 · Cited by 181 · see citing works
Intelligent disassembly of electric-vehicle batteries: a forward-looking overview
Meng, K., Xu, G., Peng, X., Youcef‐Toumi, K., & Li, J. (2022) · Resources Conservation and Recycling · doi:10.1016/j.resconrec.2022.106207 · Cited by 174 · see citing works
Izadi, A., Shahafve, M., & Ahmadi, P. (2022) · Energy Conversion and Management · doi:10.1016/j.enconman.2022.115593 · Cited by 170 · see citing works
Caballero, F. I., Sauma, E. E., & Yanine, F. F. (2013) · Energy · doi:10.1016/j.energy.2013.08.030 · Cited by 165 · see citing works
Bala, B., & Siddique, S. A. (2009) · Energy Sustainable Development/Energy for sustainable development · doi:10.1016/j.esd.2009.07.002 · Cited by 163 · see citing works
Multiobjective optimization of building energy consumption based on BIM-DB and LSSVM-NSGA-II
Chen, B., Liu, Q., Chen, H., Wang, L., Deng, T., Zhang, L., & Wu, X. (2021) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2021.126153 · Cited by 160 · see citing works
Cox, S. J., Kim, D., Cho, H., & Mago, P. (2019) · Applied Energy · doi:10.1016/j.apenergy.2019.01.093 · Cited by 157 · see citing works
Wolsink, M. (2020) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2020.109841 · Cited by 156 · see citing works
Ajagekar, A., & You, F. (2022) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2022.112493 · Cited by 155 · see citing works
Adaptive intelligent techniques for microgrid control systems: A survey
Mahmoud, M. S., Alyazidi, N. M., & Abouheaf, M. (2017) · International Journal of Electrical Power & Energy Systems · doi:10.1016/j.ijepes.2017.02.008 · Cited by 154 · see citing works
Zhou, Y., & Zheng, S. (2020) · Applied Energy · doi:10.1016/j.apenergy.2019.114416 · Cited by 151 · see citing works
Self-citationIslam, S., Iqbal, A., Marzband, M., Khan, I., & Wahedi, A. A. (2022) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2022.112574 · Cited by 150 · see citing works
Power electronics - the key technology for renewable energy system integration
Blaabjerg, F., Yang, Y., Ma, K., & Wang, X. (2015) · 2015 International Conference on Renewable Energy Research and Applications (ICRERA) · doi:10.1109/icrera.2015.7418680 · Cited by 148 · see citing works
Ali, A. O., Elmarghany, M. R., Abdelsalam, M. M., Sabry, M. N., & Hamed, A. M. (2022) · Journal of Energy Storage · doi:10.1016/j.est.2022.104609 · Cited by 144 · see citing works
Athari, M. H., & Ardehali, M. M. (2015) · Renewable Energy · doi:10.1016/j.renene.2015.07.055 · Cited by 143 · see citing works
Ram, J. P., Rajasekar, N., & Miyatake, M. (2017) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2017.02.009 · Cited by 141 · see citing works
Zhang, G., Wu, B., Maleki, A., & Zhang, W. (2018) · Solar Energy · doi:10.1016/j.solener.2018.07.094 · Cited by 141 · see citing works
Self-citationKow, K. W., Wong, Y. W., Rajkumar, R. K., & Rajkumar, R. K. (2015) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2015.11.064 · Cited by 139 · see citing works
Particle swarm optimisation in designing parameters of manufacturing processes: A review (2008–2018)
Sibalija, T. V. (2019) · Applied Soft Computing · doi:10.1016/j.asoc.2019.105743 · Cited by 136 · see citing works
He, Y., Zhou, Y., Yuan, J. P., Liu, Z., Wang, Z., & Zhang, G. (2021) · Energy Conversion and Management · doi:10.1016/j.enconman.2021.114834 · Cited by 134 · see citing works
Self-citationEnergy integration and interaction between buildings and vehicles: A state-of-the-art review
Zhou, Y., Cao, S., Hensen, J. J., & Lund, P. D. (2019) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2019.109337 · Cited by 133 · see citing works
Self-citationZhou, Y. (2022) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2022.112444 · Cited by 130 · see citing works
Self-citationLorestani, A., & Ardehali, M. M. (2018) · Energy · doi:10.1016/j.energy.2017.12.155 · Cited by 129 · see citing works
Liu, G., Jiang, T., Ollis, B., Zhang, X., & Tomsovic, K. L. (2019) · Applied Energy · doi:10.1016/j.apenergy.2019.01.210 · Cited by 128 · see citing works
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
He, Q., Zheng, H., Ma, X., Wang, L., Kong, H., & Zhu, Z. (2021) · Energy and AI · doi:10.1016/j.egyai.2021.100123 · Cited by 121 · see citing works
Zhou, Y., & Cao, S. (2019) · Energy Conversion and Management · doi:10.1016/j.enconman.2019.111888 · Cited by 120 · see citing works
Self-citationMachine learning methods to assist energy system optimization
Perera, A. T. D., Wickramasinghe, P. U., Nik, V. M., & Scartezzini, J. (2019) · Applied Energy · doi:10.1016/j.apenergy.2019.03.202 · Cited by 114 · see citing works
AI-assistance for predictive maintenance of renewable energy systems
Shin, W., Han, J., & Rhee, W. (2021) · Energy · doi:10.1016/j.energy.2021.119775 · Cited by 112 · see citing works
Zhou, Y., Cao, S., Kosonen, R., & Hamdy, M. (2020) · Energy Conversion and Management · doi:10.1016/j.enconman.2020.113017 · Cited by 112 · see citing works
Self-citationGreener energy: Issues and challenges for Pakistan—wind power prospective
Bhutto, A. W., Bazmi, A. A., & Zahedi, G. (2013) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2012.12.010 · Cited by 111 · 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
Neto, R. F. T., & Filho, M. G. (2012) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2012.03.011 · Cited by 106 · see citing works
Large scale integration of renewable energy sources (RES) in the future Colombian energy system
Pupo-Roncallo, O., Campillo, J., Ingham, D. B., Hughes, K. J., & Pourkashanian, M. M. (2019) · Energy · doi:10.1016/j.energy.2019.07.135 · Cited by 104 · see citing works
Integration of PCM in domestic hot water tanks: Optimization for shifting peak demand
Najafian, A., Haghighat, F., & Moreau, A. (2015) · Energy and Buildings · doi:10.1016/j.enbuild.2015.05.036 · Cited by 102 · see citing works
PANIGRAHI, B. K., Bhuyan, A., Shukla, J., Ray, P. K., & Pati, S. (2021) · International Journal of Energy Research · doi:10.1002/er.6641 · Cited by 102 · see citing works
Zhou, Y., & Cao, S. (2020) · Sustainable Energy Grids and Networks · doi:10.1016/j.segan.2020.100304 · Cited by 100 · see citing works
Self-citationZhou, D., Al‐Durra, A., Zhang, K., Ravey, A., & Gao, F. (2019) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2019.2893867 · Cited by 99 · see citing works
Zhou, Y., Cao, S., & Hensen, J. J. (2021) · Applied Energy · doi:10.1016/j.apenergy.2021.116606 · Cited by 98 · see citing works
Self-citationZhou, Y., Zheng, S., & Zhang, G. (2019) · Renewable Energy · doi:10.1016/j.renene.2019.11.037 · Cited by 97 · see citing works
Self-citationAktaş, A., Erhan, K., Özdemir, Ş., & Özdemir, E. (2018) · Energy · doi:10.1016/j.energy.2018.08.016 · Cited by 96 · see citing works
Algorithm for Demand Response to Maximize the Penetration of Renewable Energy
Hadi, A. A., Silva, C., Hossain, E., & Challoo, R. (2020) · IEEE Access · doi:10.1109/access.2020.2981877 · Cited by 91 · see citing works
Chen, X., Wu, X., & Lee, K. Y. (2021) · Energy Conversion and Management · doi:10.1016/j.enconman.2021.113856 · Cited by 91 · see citing works
He, Y., Zhou, Y., Wang, Z., Liu, J. P., Liu, Z., & Zhang, G. (2021) · Applied Energy · doi:10.1016/j.apenergy.2021.117444 · Cited by 90 · see citing works
Self-citationZhou, Y., Cao, S., Hensen, J. J., & Hasan, A. (2020) · Energy Conversion and Management · doi:10.1016/j.enconman.2020.112891 · Cited by 83 · see citing works
Self-citationEnergy storage system-based power control for grid-connected wind power farm
Ge, B., Wang, W., Bi, D., Rogers, C. B., Peng, F. Z., de Almeida, A. T., & Abu‐Rub, H. (2012) · International Journal of Electrical Power & Energy Systems · doi:10.1016/j.ijepes.2012.07.021 · Cited by 77 · see citing works
Zhou, Y. (2022) · Applied Energy · doi:10.1016/j.apenergy.2022.119131 · Cited by 77 · see citing works
Self-citationLoßner, M., Böttger, D., & Brückner, T. (2016) · Energy Economics · doi:10.1016/j.eneco.2016.12.008 · Cited by 76 · see citing works
Zhang, H., Zhang, X., & Yuan, J. (2020) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2020.110102 · Cited by 76 · see citing works
2015 International conference on renewable energy research and applications (ICRERA)
(2015) · International Conference on Renewable Energy Research and Applications (ICRERA) · doi:10.1109/icrera.2015.7418148 · Cited by 75 · see citing works
Feng, C., Liu, Y., & Zhang, J. (2021) · International Journal of Electrical Power & Energy Systems · doi:10.1016/j.ijepes.2021.107176 · Cited by 73 · see citing works
Mitigation strategies to minimize potential technical challenges of renewable energy integration
Shafiullah, G., Arif, M. T., & Oo, A. M. T. (2017) · Sustainable Energy Technologies and Assessments · doi:10.1016/j.seta.2017.10.008 · Cited by 71 · see citing works
García–Triviño, P., Mena, A. J. G., Llorens-Iborra, F., García‐Vázquez, C. A., RAMIREZ, L. M. F., & Jurado, F. (2014) · Energy Conversion and Management · doi:10.1016/j.enconman.2014.11.051 · Cited by 70 · see citing works
A new ultra-short-term photovoltaic power prediction model based on ground-based cloud images
Hu, K., Cao, S., Wang, L., Li, W., & Lv, M. (2018) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2018.07.311 · Cited by 70 · see citing works
Nuvvula, R. S. S., Elangovan, D., Elavarasan, R. M., Taheri, S. I., Irfan, M. F., & Kishore, T. S. (2021) · Sustainable Energy Technologies and Assessments · doi:10.1016/j.seta.2021.101689 · Cited by 70 · see citing works
Hassan, Z. E., Amir, A., Selvaraj, J. A.-L., & Rahim, N. A. (2020) · Solar Energy · doi:10.1016/j.solener.2020.06.085 · Cited by 69 · see citing works
Liu, J., Zhou, Y., Yang, H., & Wu, H. (2022) · Applied Energy · doi:10.1016/j.apenergy.2022.119394 · Cited by 69 · see citing works
Self-citationGarshasbi, S., Kurnitski, J., & Mohammadi, Y. (2016) · Applied Energy · doi:10.1016/j.apenergy.2016.07.033 · Cited by 68 · see citing works
Review of application of AI techniques to Solar Tower Systems
Milidonis, K. F., Blanco, M., Grigoriev, V., Panagiotou, C. F., Bonanos, A. M., Constantinou, M., Pye, J., & Asselineau, C.-A. (2021) · Solar Energy · doi:10.1016/j.solener.2021.06.009 · Cited by 68 · see citing works
Ser, J. D., Casillas-Pérez, D., Cornejo-Bueno, L. M., Prieto-Godino, L., Sanz, J., Casanova-Mateo, C., & Salcedo‐Sanz, S. (2022) · Applied Soft Computing · doi:10.1016/j.asoc.2022.108526 · Cited by 67 · see citing works
Design of integrated energy market cloud service platform based on blockchain smart contract
Wang, L., Ma, Y., Zhu, L., Wang, X., Cong, H., & Shi, T. (2021) · International Journal of Electrical Power & Energy Systems · doi:10.1016/j.ijepes.2021.107515 · Cited by 66 · see citing works
Liu, Z., Xie, M., Zhou, Y., He, Y., Zhang, L., Zhang, G., & Chen, D. (2021) · Energy and Built Environment · doi:10.1016/j.enbenv.2021.10.003 · Cited by 66 · see citing works
Self-citationMatschoss, P., Bayer, B., Thomas, H., & Marian, A. (2018) · Renewable Energy · doi:10.1016/j.renene.2018.10.103 · Cited by 65 · see citing works
Application of machine learning for wind energy from design to energy-Water nexus: A Survey
Elyasichamazkoti, F., & Khajehpoor, A. (2021) · Energy Nexus · doi:10.1016/j.nexus.2021.100011 · Cited by 62 · see citing works
Vijayalakshmi, K., Vijayakumar, K., & Nandhakumar, K. (2022) · Electric Power Systems Research · doi:10.1016/j.epsr.2022.107879 · Cited by 60 · see citing works
Zhang, R., Yan, K., Li, G., Jiang, T., Li, X., & Chen, H. (2020) · International Journal of Electrical Power & Energy Systems · doi:10.1016/j.ijepes.2020.106094 · Cited by 57 · see citing works
Zhou, Y., & Cao, S. (2020) · Energy Conversion and Management · doi:10.1016/j.enconman.2020.113473 · Cited by 57 · see citing works
Self-citationZhou, F., Li, Y., Wang, W., & Pan, C. (2022) · Energy and Buildings · doi:10.1016/j.enbuild.2022.111916 · Cited by 56 · see citing works
Namilakonda, S., & Guduri, Y. (2021) · International Journal of Electrical Power & Energy Systems · doi:10.1016/j.ijepes.2020.106632 · Cited by 56 · see citing works
Li, X., Wang, W., & Wang, H. (2021) · Energy · doi:10.1016/j.energy.2021.120513 · Cited by 56 · see citing works
Fikiin, K., Stankov, B., Evans, J., Maidment, G., Foster, A., Brown, T., Radcliffe, J., Youbi-Idrissi, M., Alford, A., Varga, L., et al. (2017) · Trends in Food Science & Technology · doi:10.1016/j.tifs.2016.11.011 · Cited by 55 · see citing works
Tümay, M., Demirdelen, T., Bal, S., Kayaalp, R. İ., Doğru, B., & Aksoy, M. (2017) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2017.04.008 · Cited by 55 · see citing works
Vigneysh, T., & Kumarappan, N. (2017) · Electric Power Systems Research · doi:10.1016/j.epsr.2017.06.010 · Cited by 55 · see citing works
Zhou, Y., & Zheng, S. (2020) · Energy · doi:10.1016/j.energy.2020.117747 · Cited by 53 · see citing works
Self-citationData-driven modelling of energy demand response behaviour based on a large-scale residential trial
Antonopoulos, I., Robu, V., Couraud, B., & Flynn, D. (2021) · Energy and AI · doi:10.1016/j.egyai.2021.100071 · Cited by 53 · see citing works
Han, G., Lee, S., Lee, J., Lee, K., & Bae, J. (2021) · Journal of Energy Storage · doi:10.1016/j.est.2021.102868 · Cited by 53 · see citing works
Ren, H., Ma, Z., Lin, W., Wang, S., & Li, W. (2018) · Energy Conversion and Management · doi:10.1016/j.enconman.2018.11.020 · Cited by 50 · see citing works
Rao, G. N., Raju, P. S., & Sekhar, K. C. (2014) · International Journal of Electrical Power & Energy Systems · doi:10.1016/j.ijepes.2014.02.023 · Cited by 49 · see citing works
Co-optimization generation and transmission planning for maximizing large-scale solar PV integration
Alanazi, M. S., Mahoor, M., & Khodaei, A. (2019) · International Journal of Electrical Power & Energy Systems · doi:10.1016/j.ijepes.2019.105723 · Cited by 48 · see citing works
Mahmoudi, S. M., Maleki, A., & Ochbelagh, D. R. (2022) · Journal of Energy Storage · doi:10.1016/j.est.2022.104015 · Cited by 47 · see citing works
Mohamed, A. A. S., Metwally, H. M. B., El-Sayed, A., & Selem, S. I. (2019) · Solar Energy · doi:10.1016/j.solener.2019.09.018 · Cited by 46 · see citing works
Hybridized Intelligent Home Renewable Energy Management System for Smart Grids
Ma, Y.-H., & Li, B. (2020) · Sustainability · doi:10.3390/su12052117 · Cited by 43 · see citing works
Hybrid renewable energy system based on intelligent optimization techniques
Mercado, K. D., Mares, J. R. J., & M., C. G. Q. (2016) · International Conference on Renewable Energy Research and Applications (ICRERA) · doi:10.1109/icrera.2016.7884417 · Cited by 42 · see citing works
Luo, X., Liu, Y., & Liu, X. (2021) · Journal of Building Engineering · doi:10.1016/j.jobe.2021.102744 · Cited by 41 · see citing works
Comparison of Small-Scale Wind Energy Conversion Systems: Economic Indexes
Nazir, M. S., Wang, Y., Bilal, M., Sohail, H. M., Kadhem, A. A., Nazir, H. M. R., Abdalla, A. N., & Ma, Y. (2020) · Clean Technologies · doi:10.3390/cleantechnol2020010 · Cited by 39 · see citing works
Zhou, Y. (2021) · iScience · doi:10.1016/j.isci.2021.103420 · Cited by 36 · see citing works
Self-citationArdehali, M. M., Saboori, M., & Teshnelab, M. (2003) · Energy Conversion and Management · doi:10.1016/j.enconman.2003.11.003 · Cited by 35 · see citing works
Makhloufi, S., Khennas, S., Bouchaib, S., & Arab, A. H. (2021) · Renewable Energy · doi:10.1016/j.renene.2021.10.088 · Cited by 31 · see citing works
2019 8th International Conference on Modern Power Systems (MPS)
(2019) · doi:10.1109/mps46825.2019 · Cited by 31 · see citing works
The intelligent solution: automation, the skills shortage and cyber-security
Smith, G. (2018) · Computer Fraud & Security · doi:10.1016/s1361-3723(18)30073-3 · Cited by 26 · see citing works
Banu, J. F., Mahajan, R. A., Sakthi, U., Nassa, V. K., Lakshmi, D., & Nadanakumar, V. (2022) · Sustainable Energy Technologies and Assessments · doi:10.1016/j.seta.2022.102334 · Cited by 25 · see citing works
Sustainable Developments by Artificial Intelligence and Machine Learning for Renewable Energies
(2022) · Elsevier eBooks · doi:10.1016/c2020-0-04074-0 · Cited by 25 · see citing works
State of Energy Prediction in Renewable Energy-driven Mobile Edge Computing using CNN-LSTM Networks
Ku, Y.-J., Sapra, S., Baidya, S., & Dey, S. (2020) · IEEE Green Energy and Smart Systems Conference (IGESSC) · doi:10.1109/igessc50231.2020.9285102 · Cited by 25 · see citing works
Garlík, B., & Křivan, M. (2013) · Energy and Buildings · doi:10.1016/j.enbuild.2013.07.062 · Cited by 24 · see citing works
Large scale integration of intermittent renewable energy sources in the Greek power sector
Voumvoulakis, E. M., Asimakopoulou, G. E., Danchev, S., Maniatis, G., & Τσακανίκας, Ά. (2012) · Energy Policy · doi:10.1016/j.enpol.2012.05.056 · Cited by 23 · see citing works
Intelligent Optimization And Management System For Renewable Energy Systems Using Multi-Agent
Ameur, C., Faquir, S., & Yahyaouy, A. (2019) · IAES International Journal of Artificial Intelligence · doi:10.11591/ijai.v8.i4.pp352-359 · Cited by 23 · see citing works
Dărab, C. P., Tarnovan, R., Turcu, A., & Martineac, C. (2019) · International Conference on Modern Power Systems (MPS) · doi:10.1109/mps.2019.8759662 · Cited by 23 · see citing works
Mahfouz, M. M. A. (2020) · Electric Power Systems Research · doi:10.1016/j.epsr.2020.106405 · Cited by 21 · see citing works
Prediction Markets for Probabilistic Forecasting of Renewable Energy Sources
Shamsi, M., & Cuffe, P. (2021) · IEEE Transactions on Sustainable Energy · doi:10.1109/tste.2021.3112916 · Cited by 20 · see citing works
Anjaiah, K., Dash, P. K., & Sahani, M. (2022) · ISA Transactions · doi:10.1016/j.isatra.2022.05.037 · Cited by 20 · see citing works
Shah, T., & Ansari, Z. A. (2018) · IEEE International Conference on Industrial and Information Systems (ICIIS) · doi:10.1109/iciinfs.2018.8721384 · Cited by 20 · see citing works
Application of Smart Grid Technologies
(2018) · Elsevier eBooks · doi:10.1016/c2014-0-03960-3 · Cited by 19 · see citing works
Zangeneh, M., Aghajari, E., & Forouzanfar, M. (2021) · Soft Computing · doi:10.1007/s00500-021-06288-x · Cited by 16 · see citing works
Nandkeolyar, S., & Ray, P. K. (2022) · Journal of Energy Storage · doi:10.1016/j.est.2022.104439 · Cited by 16 · see citing works
Rostami, A. A., Jalilian, A., Naderi, S. B., Negnevitsky, M., Davari, P., & Blaabjerg, F. (2017) · Australasian Universities Power Engineering Conference · doi:10.1109/aupec.2017.8282394 · Cited by 14 · see citing works
The fault detection method of islanded microgrid with the V/f controlled distributed generation
Liang, Z., Mu, L., Zhang, F., Zhou, H., & Zhang, X. (2019) · International Journal of Electrical Power & Energy Systems · doi:10.1016/j.ijepes.2019.04.030 · Cited by 14 · see citing works
Ju, L., Huang, L., Lin, H., Li, H., & Tan, Z. (2020) · Sustainable Cities and Society · doi:10.1016/j.scs.2020.102504 · Cited by 14 · see citing works
Detection and diagnosis of islanding using artificial intelligence in distributed generation systems
Mohapatra, S. S., Maharana, M. K., Pradhan, A., Panigrahi, P. K., & Prusty, R. C. (2021) · Sustainable Energy Grids and Networks · doi:10.1016/j.segan.2021.100576 · Cited by 13 · see citing works
Li, J., & Samavatian, V. (2020) · Computers & Electrical Engineering · doi:10.1016/j.compeleceng.2020.106769 · Cited by 12 · see citing works
Aragón, G., Pandian, V. P. S., Krauß, V., Werner-Kytölä, O., Thybo, G. W., & Pautasso, E. (2022) · Energy · doi:10.1016/j.energy.2022.123889 · Cited by 12 · see citing works
(2019) · International Journal of Renewable Energy Research · doi:10.20508/ijrer.v9i1.8917.g7582 · Cited by 9 · see citing works
Chandio, A. A., Laghari, J. A., Khokhar, S., & Almani, S. A. (2019) · Journal of Intelligent & Fuzzy Systems · doi:10.3233/jifs-181849 · Cited by 9 · see citing works
Utilization of Thermal Potential of Abandoned Wells
(2022) · Elsevier eBooks · doi:10.1016/c2020-0-04095-8 · Cited by 9 · see citing works
Liu, Z., Zeng, C., Zhou, Y., & Xing, C. (2022) · Elsevier eBooks · doi:10.1016/b978-0-323-90616-6.00009-9 · Cited by 9 · see citing works
Self-citationResearch on resilience assessment and disaster prevention strategy of active distribution network
Li, Z., Liu, Z., Zhang, Y., Zhang, H. F., & Fu, D. (2021) · Energy Reports · doi:10.1016/j.egyr.2021.09.198 · Cited by 7 · see citing works
Self-citationCeseña, E. A. M., & Mancarella, P. (2018) · Elsevier eBooks · doi:10.1016/b978-0-12-803128-5.00008-8 · Cited by 6 · see citing works
Flagged first, then by citations
Research works printed but not linked: 3
- Zhou Y. Chapter 8 -advanced renewable dispatch with machine learning-based hybrid demand-side controller: the state of the art and a novel approach (Eds.). In:
- Zhou Y. Chapter 9 -a machine learning-based design approach on PCMs-PV systems with multilevel scenario uncertainty (Eds.). In: Kumar K, Rao RS, Kaiwartya O, Kaiser MS, Padmanaban S, editors. Sustainable developments by artificial intelligence and machine learning for renewable energies. Elsevier; 2022. p. 257-72.
- Casini M, Casini M. Chapter 12 -smart buildings and smart cities (Ed.). Construction 4.0. Woodhead Publishing; 2022. p. 607-60.
Cited by
Sorted by citations
RETRACTED ARTICLE: Artificial intelligence-based solutions for climate change: a review
Chen, L., Chen, Z., Zhang, Y., Liu, Y., Osman, A. I., Farghali, M. R. F., Hua, J., Al-Fatesh, A. S., Ihara, I., Rooney, D. W., et al. (2023) · Environmental Chemistry Letters · doi:10.1007/s10311-023-01617-y · Cited by 432 · see citing works
RetractedWang, Q., Zhang, F., Li, R., & Sun, J. (2024) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2024.141298 · Cited by 358 · see citing works
Cavus, M. (2025) · Electronics · doi:10.3390/electronics14061159 · Cited by 204 · see citing works
Hydrate Technologies for CO 2 Capture and Sequestration: Status and Perspectives
Wang, P., Li, Y., Sun, N., Han, S., Wang, X., Su, Q., Li, Y., He, J., Yu, X., Du, S., et al. (2024) · Chemical Reviews · doi:10.1021/acs.chemrev.2c00777 · Cited by 150 · see citing works
Mahmood, M., Chowdhury, P., Yeassin, R., Hasan, M., Ahmad, T., & Chowdhury, N.-U.-R. (2024) · Energy Conversion and Management X · doi:10.1016/j.ecmx.2024.100790 · Cited by 151 · see citing works
Jiang, Y., Li, Y., Ding, Y., Hu, S., Dang, J., Yang, F., & Ouyang, M. (2022) · Journal of Power Sources · doi:10.1016/j.jpowsour.2022.232303 · Cited by 146 · see citing works
Chen, Y., & Jin, S. (2023) · Processes · doi:10.3390/pr11092705 · Cited by 137 · see citing works
Liu, Z., Zhang, X., Sun, Y., & Zhou, Y. (2023) · Energy and Buildings · doi:10.1016/j.enbuild.2023.113436 · Cited by 136 · see citing works
Self-citationZhu, Y., Wu, S., Li, J., Jia, Q., Zhang, T., Zhang, X., Han, D., & Tan, Y. (2024) · Journal of Energy Storage · doi:10.1016/j.est.2024.110663 · Cited by 134 · see citing works
Yousef, L. A., Yousef, H., & Rocha‐Meneses, L. (2023) · Energies · doi:10.3390/en16248057 · Cited by 122 · see citing works
Um-e-Habiba, Ahmed, I., Asif, M., Alhelou, H. H., & Khalid, M. (2024) · Journal of Building Engineering · doi:10.1016/j.jobe.2024.109354 · Cited by 119 · see citing works
Solar and Wind Energy Integrated System Frequency Control: A Critical Review on Recent Developments
Alam, M. S., Chowdhury, T. A., Dhar, A., Al–Ismail, F. S., Choudhury, M., Shafiullah, M., Hossain, M. A., Hossain, M. A., Ullah, A., & Rahman, S. M. (2023) · Energies · doi:10.3390/en16020812 · Cited by 110 · see citing works
A Review of Solar Forecasting Techniques and the Role of Artificial Intelligence
Barhmi, K., Heynen, C., Golroodbari, S., & van Sark, W. G. J. H. M. (2024) · Solar · doi:10.3390/solar4010005 · Cited by 109 · see citing works
Liu, Z., Yu, C., Qian, Q. K., Huang, R., You, K., Visscher, H. J., & Zhang, G. (2023) · Energy and Buildings · doi:10.1016/j.enbuild.2023.113343 · Cited by 104 · see citing works
Self-citationCan artificial intelligence help accelerate the transition to renewable energy?
Zhao, Q., Wang, L., Stan, S. E., & Mirza, N. (2024) · Energy Economics · doi:10.1016/j.eneco.2024.107584 · Cited by 101 · see citing works
Antonesi, G., Cioara, T., Anghel, I. M., Michalakopoulos, V., Sarmas, E., & Toderean, L. (2025) · Applied Energy · doi:10.1016/j.apenergy.2025.126670 · Cited by 102 · see citing works
The smart green tide: A bibliometric analysis of AI and renewable energy transition
Gao, D., Cai, J., & Wu, K. M. (2025) · Energy Reports · doi:10.1016/j.egyr.2025.04.052 · Cited by 80 · see citing works
Aghamolaei, R., & Fallahpour, M. (2023) · Journal of Building Engineering · doi:10.1016/j.jobe.2023.107183 · Cited by 79 · see citing works
Alam, M. M., Hossain, M. J., Habib, M. A., Arafat, M. Y., & Hannan, M. A. (2025) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2024.115251 · Cited by 76 · see citing works
Zhou, Y., Zheng, S., Lei, J., & Zi, Y. (2023) · Energy Conversion and Management · doi:10.1016/j.enconman.2023.117733 · Cited by 73 · see citing works
Self-citationArtificial intelligence for calculating and predicting building carbon emissions: a review
Hua, J., Wang, R., Hu, Y. C., Chen, Z., Chen, L., Osman, A. I., Farghali, M. R. F., Huang, L., Feng, J., Wang, J., et al. (2025) · Environmental Chemistry Letters · doi:10.1007/s10311-024-01799-z · Cited by 72 · see citing works
Artificial intelligence for hydrogen-enabled integrated energy systems: A systematic review
Mullanu, S., Chua, C., Molnar, A., & Yavari, A. (2024) · International Journal of Hydrogen Energy · doi:10.1016/j.ijhydene.2024.08.013 · Cited by 71 · see citing works
Shayan, M. E., Najafi, G., Ghobadian, B., Gorjian, S., Mamat, R., & Ghazali, M. F. (2022) · Renewable Energy · doi:10.1016/j.renene.2022.11.006 · Cited by 68 · see citing works
Reviewing 40 years of artificial intelligence applied to power systems – A taxonomic perspective
Heymann, F., Quest, H., Garcia, T., Ballif, C., & Galus, M. D. (2023) · Energy and AI · doi:10.1016/j.egyai.2023.100322 · Cited by 66 · see citing works
Perspectives for artificial intelligence in sustainable energy systems
Chen, D., Lin, X., & Qiao, Y. (2025) · Energy · doi:10.1016/j.energy.2025.134711 · Cited by 62 · see citing works
Precision to plate: AI-driven innovations in fermentation and hyper-personalized diets
Priyadharshini, D., Muthuvel, I., Saraswathy, S., Kavitha, P. S., & Jegadeeswari, V. (2025) · Frontiers in Nutrition · doi:10.3389/fnut.2025.1659511 · Cited by 58 · see citing works
Loza, B., Minchala, L. I., Ochoa-Correa, D., & Martínez, S. (2024) · Sustainability · doi:10.3390/su16219535 · Cited by 57 · see citing works
The Role of Smart Grid Technologies in Urban and Sustainable Energy Planning
Almihat, M. G. M., & Munda, J. L. (2025) · Energies · doi:10.3390/en18071618 · Cited by 57 · see citing works
A review on digital twin application in photovoltaic energy systems: challenges and opportunities
Alao, K. T., Gilani, S. I. U.-H., Sopian, K., & Alao, T. O. (2024) · JMST Advances · doi:10.1007/s42791-024-00083-z · Cited by 56 · see citing works
Wang, B., Yang, Z., Ji, M., Shan, J., Ni, M., Hou, Z., Cai, J., Gu, X., Yuan, X., Gong, Z., et al. (2023) · Energy and AI · doi:10.1016/j.egyai.2023.100278 · Cited by 55 · see citing works
Elkadeem, M. R., Kotb, K. M., Abido, M. A., Hasanien, H. M., Atiya, E. G., Almakhles, D. J., & Elmorshedy, M. F. (2023) · Energy · doi:10.1016/j.energy.2023.129816 · Cited by 55 · see citing works
Zhou, L., Song, A., & Zhou, Y. (2023) · Energy Conversion and Management · doi:10.1016/j.enconman.2023.117984 · Cited by 54 · see citing works
Self-citationGiannuzzo, L., Minuto, F. D., Schiera, D. S., & Lanzini, A. (2023) · Energy and AI · doi:10.1016/j.egyai.2023.100329 · Cited by 49 · see citing works
Ai in Energy: Overcoming Unforeseen Obstacles
Danish, M. S. S. (2023) · AI · doi:10.3390/ai4020022 · Cited by 48 · see citing works
Yudhistira, B., Adi, P., Mulyani, R., Chang, C., Gavahian, M., & Hsieh, C. (2024) · Comprehensive Reviews in Food Science and Food Safety · doi:10.1111/1541-4337.13413 · Cited by 48 · see citing works
Leveraging artificial intelligence to meet the sustainable development goals
Ziemba, E. W., Duong, C. D., Ejdys, J., González-Pérez, M. A., Universidad EAFIT, C., Kazlauskaitė, R., Korzyński, P., Mazurek, G., Paliszkiewicz, J. O., Stankevičienė, J., et al. (2024) · Journal of Economics and Management · doi:10.22367/jem.2024.46.19 · Cited by 49 · see citing works
A Solar and Wind Energy Evaluation Methodology Using Artificial Intelligence Technologies
Симанков, В. С., Buchatskiy, P. Y., Kazak, A., Васильевич, Т. С., Onishchenko, S. V., Kuzmin, K. A., & Chetyrbok, P. V. (2024) · Energies · doi:10.3390/en17020416 · Cited by 47 · see citing works
Does artificial intelligence improve energy efficiency? Evidence from provincial data in China
Li, X., Li, S., Cao, J., & Spulbar, A. C. (2024) · Energy Economics · doi:10.1016/j.eneco.2024.108149 · Cited by 47 · see citing works
Necula, S.-C. (2023) · Energies · doi:10.3390/en16227633 · Cited by 45 · see citing works
Li, X., Wang, Q., & Tang, Y. (2024) · Sustainability · doi:10.3390/su16083200 · Cited by 45 · see citing works
Frontier ocean thermal/power and solar PV systems for transformation towards net-zero communities
Liu, Z., Zhou, Y., Yan, J. F., & Tostado‐Véliz, M. (2023) · Energy · doi:10.1016/j.energy.2023.128362 · Cited by 42 · see citing works
Self-citationDeterminants of environmental pollution: Evidence from Indonesia
Xuan, V. N. (2024) · Journal of Open Innovation Technology Market and Complexity · doi:10.1016/j.joitmc.2024.100386 · Cited by 42 · see citing works
Xuan, V. N., Huong, L. M., Thu, N. T. P., & Hoa, P. X. (2024) · Energy Reports · doi:10.1016/j.egyr.2024.04.045 · Cited by 41 · see citing works
Lipu, M. S. H., Rahman, M. S. A., Islam, Z. U., Rahman, T., Rahman, M. S., Meraj, S. T., Hossain, M. S. B., & Mansor, M. (2025) · Journal of Energy Storage · doi:10.1016/j.est.2025.116629 · Cited by 41 · see citing works
Pineda, V. G., Valencia-Arías, A., Giraldo, F. E. L., & Ochoa, E. A. Z. (2025) · International Journal of Cognitive Computing in Engineering · doi:10.1016/j.ijcce.2025.08.002 · Cited by 40 · see citing works
AI in power systems: a systematic review of key matters of concern
Henao, F., Edgell, R., Sharma, A., & Olney, J. R. (2025) · Energy Informatics · doi:10.1186/s42162-025-00529-1 · Cited by 39 · see citing works
Hien, P. T., Nong, D., Simshauser, P., Nguyen, G., & Duong, K. T. (2024) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2024.140692 · Cited by 38 · see citing works
Modularized neural network incorporating physical priors for future building energy modeling
Jiang, Z., & Dong, B. (2024) · Patterns · doi:10.1016/j.patter.2024.101029 · Cited by 38 · see citing works
Hoa, P. X., Xuan, V. N., & Thu, N. T. P. (2024) · Heliyon · doi:10.1016/j.heliyon.2024.e39943 · Cited by 38 · see citing works
Zhou, Y. (2024) · Renewable Energy · doi:10.1016/j.renene.2024.120280 · Cited by 37 · see citing works
Self-citationDoes artificial intelligence affect the ecological footprint? –Evidence from 30 provinces in China
Wang, Y., Zhang, R., Yao, K., & Ma, X. (2024) · Journal of Environmental Management · doi:10.1016/j.jenvman.2024.122458 · Cited by 36 · see citing works
Transitioning the energy landscape: AI's role in shifting from fossil fuels to renewable energy
Li, Z., Xing, Y., Shao, X., Zhong, Y., & Su, Y. H. (2025) · Energy Economics · doi:10.1016/j.eneco.2025.108729 · Cited by 36 · see citing works
A Comprehensive Review of Integrated Energy Management for Future Smart Energy System
Ali, M. S., Sharma, A., Joy, T. A., & Halim, M. A. (2024) · Control Systems and Optimization Letters · doi:10.59247/csol.v2i1.77 · Cited by 35 · see citing works
Emerging role of generative AI in renewable energy forecasting and system optimization
Erdiwansyah, E., Mamat, R., Syafrizal, S., Ghazali, M. F., Basrawi, F., & Rosdi, S. M. (2025) · Sustainable Chemistry for Climate Action · doi:10.1016/j.scca.2025.100099 · Cited by 33 · see citing works
Liang, X., Liu, Z., Wang, J., Jin, X., & Du, Z. (2023) · Applied Energy · doi:10.1016/j.apenergy.2023.120889 · Cited by 32 · see citing works
Self-citationLin, R., Huo, J., Cai, X. L., Lan, S., & Hao, Z. (2024) · Journal of Power Sources · doi:10.1016/j.jpowsour.2024.235030 · Cited by 32 · see citing works
Xu, Z. (2025) · Applied Ocean Research · doi:10.1016/j.apor.2025.104796 · Cited by 32 · see citing works
Wolsink, M. (2024) · Energy Research & Social Science · doi:10.1016/j.erss.2023.103392 · Cited by 31 · see citing works
Petchimuthu, S., Fathima Banu, M., Mahendiran, C., & Premala, T. (2025) · Spectrum of Operational Research. · doi:10.31181/sor21202525 · Cited by 31 · see citing works
Asl, M. G., Jabeur, S. B., Nammouri, H., & Miled, K. B. H. (2024) · Energy Economics · doi:10.1016/j.eneco.2024.108017 · Cited by 29 · see citing works
Alhasnawi, B. N., Hashim, H. K., Zanker, M., & Bureš, V. (2025) · Energy Conversion and Management X · doi:10.1016/j.ecmx.2025.101162 · Cited by 27 · see citing works
Factors Affecting Renewable Energy for Sustainable Development: The Case of the Philippines
Xuan, V. N. (2024) · Environmental Health Insights · doi:10.1177/11786302241288856 · Cited by 26 · see citing works
Data-driven prediction models of photovoltaic energy for smart grid applications
Souabi, S., Chakir, A., & Tabaa, M. (2023) · Energy Reports · doi:10.1016/j.egyr.2023.05.237 · Cited by 26 · see citing works
Qiu, Y., Hui, Y., Zhao, P., Wang, M., Guo, S., Dai, B., Dou, J., Bhattacharya, S., & Yu, J. (2024) · Fuel · doi:10.1016/j.fuel.2024.132162 · Cited by 26 · see citing works
Towards a just AI-assisted energy transitions for vulnerable communities
Delina, L. L., & Tung, Y. S. M. (2024) · Energy Research & Social Science · doi:10.1016/j.erss.2024.103752 · Cited by 25 · see citing works
Artificial Intelligence-driven regional energy transition:Evidence from China
Zhao, Z., Zhao, Q., Li, S., & Yan, J. (2024) · Economic Analysis and Policy · doi:10.1016/j.eap.2024.10.004 · Cited by 25 · see citing works
Hofmeister, M., Lee, K. F., Tsai, Y., Müller, M., Nagarajan, K. V., Mosbach, S., Akroyd, J., & Kraft, M. (2024) · Energy and AI · doi:10.1016/j.egyai.2024.100376 · Cited by 25 · see citing works
Freitas, R. S. M., & Jiang, X. (2024) · Energy and AI · doi:10.1016/j.egyai.2024.100385 · Cited by 24 · see citing works
Bag, S., Rahman, M. S., Routray, S., Roubaud, D., & Mangla, S. K. (2025) · Business Strategy and the Environment · doi:10.1002/bse.70052 · Cited by 24 · see citing works
Kim, J., Kim, J., Sovacool, B. K., Griffiths, S., Bazilian, M., Creutzig, F., Rio, D. D. F. D., Agarwala, M., Choi, M., & Debnath, R. (2026) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2026.116861 · Cited by 24 · see citing works
The impact of China’s artificial intelligence development on urban energy efficiency
Zeng, J., & Wang, T. Z. (2025) · Scientific Reports · doi:10.1038/s41598-025-09319-x · Cited by 23 · see citing works
Integrated Energy System Systems and Game Theory A Review
Ramadhan, A., Bhima, B., & Nurtino, T. (2022) · International Transactions on Artificial Intelligence (ITALIC) · doi:10.33050/italic.v1i1.203 · Cited by 23 · see citing works
Abouelaziz, I., & Jouane, Y. (2023) · Building Simulation · doi:10.1007/s12273-023-1089-y · Cited by 23 · see citing works
Du, S., Ding, T., Xiao, Y., Wan, J., Liu, J. P., & Meng, F. (2024) · IEEE Transactions on Power Systems · doi:10.1109/tpwrs.2024.3423381 · Cited by 22 · see citing works
Self-citationZeng, Q., Yang, X., Bu, E., Liu, Z., Ji, F., Yuan, F., Jin, Y., Gao, X., & Li, C. (2025) · International Journal of Hydrogen Energy · doi:10.1016/j.ijhydene.2025.01.243 · Cited by 22 · see citing works
Self-citationWang, X. (2025) · Journal of Environmental Management · doi:10.1016/j.jenvman.2025.126397 · Cited by 22 · see citing works
Hussein, J. B., Workneh, T. S., Kassim, A., Mugodo, K., Melesse, S. F., El‐Mesery, H. S., & Zicheng, H. (2025) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2025.116076 · Cited by 22 · see citing works
Bârã, A., & Oprea, S. (2024) · International Journal of Computational Intelligence Systems · doi:10.1007/s44196-024-00464-1 · Cited by 22 · see citing works
Kong, J., Dong, Y., Zhang, Z. F., Yap, P., & Zhou, Y. (2024) · Sustainable Energy Technologies and Assessments · doi:10.1016/j.seta.2024.104012 · Cited by 22 · see citing works
Self-citationOptimization of modeling and temperature control of air-cooled PEMFC based on TLBO-DE
He, P., Chen, J., Zhang, C.-Z., Yu, Z., Wang, M., Chen, J., Song, J.-L., Mu, Y., Gong, K., & Tao, W. (2024) · Energy and AI · doi:10.1016/j.egyai.2024.100430 · Cited by 21 · see citing works
Turyasingura, B., Ayiga, N., Kayusi, F., & Tumuhimbise, M. (2024) · Babylonian Journal of Machine Learning · doi:10.58496/bjml/2024/018 · Cited by 21 · see citing works
AI carbon footprint in China sets to double post-2030 carbon peaking
Chen, Z., Xiong, Q., Duan, J., Ma, J., Chen, Z., & Guo, S. (2025) · Energy Economics · doi:10.1016/j.eneco.2025.108880 · Cited by 22 · see citing works
Satpathy, P. R., & Ramachandaramurthy, V. K. (2025) · Applied Energy · doi:10.1016/j.apenergy.2025.127109 · Cited by 21 · see citing works
Prianka, Y. M., Sharma, A., & Biswas, C. (2024) · Control Systems and Optimization Letters · doi:10.59247/csol.v2i3.142 · Cited by 20 · see citing works
Liu, L., Aljibori, H. S. S., Abdulrahman, A., Samad, S., Zhang, L., Osman, H., Abdujabbarov, A., & Mahariq, I. (2025) · Applied Thermal Engineering · doi:10.1016/j.applthermaleng.2025.128232 · Cited by 20 · see citing works
Zhang, T., Wang, K., Xiao, F., Xu, C., & Ye, F. (2024) · Renewable Energy · doi:10.1016/j.renene.2024.119949 · Cited by 20 · see citing works
Zhang, L., Han, H., Li, W., Guo, K., Yuan, M., & Liu, Z. (2024) · Applied Thermal Engineering · doi:10.1016/j.applthermaleng.2024.122605 · Cited by 20 · see citing works
Self-citationIcaza, D., González-Ladrón-de-Guevara, F., Espinoza, J. L. R., Borge-Díez, D., Galindo, S. P., & Flores-Vázquez, C. (2025) · Energies · doi:10.3390/en18061523 · Cited by 19 · see citing works
Xuan, V. N. (2025) · Energy & Environment · doi:10.1177/0958305x251322893 · Cited by 18 · see citing works
Prajapati, A., Paraye, P., Ahirwar, B. K., & Tam, C. F. (2025) · Journal of Thermal Analysis and Calorimetry · doi:10.1007/s10973-025-14558-1 · Cited by 18 · see citing works
Arsad, S. R., Hadi, M. H. H., Afandi, N. A. M., Ker, P. J., Tang, S. G. H., Afzal, M. M., Ramanathan, S., Chen, C. P., Krishnan, P. S., & Kiong, T. S. (2023) · Energies · doi:10.3390/en16186510 · Cited by 18 · see citing works
Ramzani, S. R., Konhäusner, P., Olaniregun, O. A., Abu-Alkheil, A. M., & Alsharari, N. M. (2024) · European Journal of Business Management and Research · doi:10.24018/ejbmr.2024.9.2.2277 · Cited by 18 · 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
Renewable energy for a sustainable future
Allegretti, G., Montoya, M. A., & Talamini, E. (2023) · Elsevier eBooks · doi:10.1016/b978-0-443-13439-5.00001-6 · Cited by 17 · see citing works
Zhang, L., Yang, Z., Xiao, Q., Guo, Y., Ying, Z., Hu, T., Xu, X., Khan, S., & Li, K. (2024) · Energy and AI · doi:10.1016/j.egyai.2024.100340 · Cited by 17 · see citing works
The potential for sustainable waste management and energy recovery in Bangladesh: A review
Shovon, S. M., Akash, F. A., Rahman, M. A., Rahman, W., Chakraborty, P., Monir, M. U., Karim, K. M. R., Habib, M. A., & Khan, M. F. H. (2024) · Sustainable Energy Technologies and Assessments · doi:10.1016/j.seta.2024.103705 · Cited by 17 · see citing works
Poursaeid, M. (2024) · Journal of Hydrology · doi:10.1016/j.jhydrol.2024.132373 · Cited by 17 · see citing works
Ding, Z., Li, R., & Luo, K. H. (2025) · Journal of Power Sources · doi:10.1016/j.jpowsour.2025.236959 · Cited by 16 · see citing works
Mendonça, M., & Santos, V. (2025) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2025.145564 · Cited by 16 · see citing works
Gavorník, A., Podroužek, J., Oresko, S., Slosiarová, N., & Grmanová, G. (2024) · Telematics and Informatics · doi:10.1016/j.tele.2024.102132 · Cited by 16 · see citing works
- Authors
- Liu, Z., Sun, Y., Xing, C., Liu, J., He, Y., Zhou, Y., & Zhang, G.
- Affiliations
- Hunan University (CN)
- Concordia University (CA)
- Hong Kong Polytechnic University (HK)
- Hong Kong University of Science and Technology (HK)
- Corresponding author
- Liu, Zhengxuan
- Sun, Ying
- Zhou, Yuekuan
- Venue
- Energy and AI
- Publisher
- Elsevier BV
- Published
- 2022-11
- Volume
- 10
- Pages
- 100195
- ISSN
- 2666-5468 (ISSN-L)
- OpenAlex
- W4289861147
- Cited by
- 288
- References
- 174
- Citations by year
- 2026: 60 · 2025: 99 · 2024: 91 · 2023: 34 · 2022: 4
- FWCI
- 15.60
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Energy and AI (publishedVersion, cc-by)
- PolyU Institutional Research Archive (Hong Kong Polytechnic University) (submittedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Rare & Special e-Zone (The Hong Kong University of Science and Technology) (submittedVersion)
- Research Repository (Delft University of Technology) (submittedVersion, cc-by)
- Data Archiving and Networked Services (DANS) (submittedVersion, other-oa)
- Topics
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Integrated Energy Systems Optimization
- Physical Sciences › Energy › Energy Engineering and Power Technology › Power Systems and Renewable Energy
- Physical Sciences › Energy › Energy Engineering and Power Technology › Hybrid Renewable Energy Systems
- Keywords
- artificial intelligence, large-scale renewable energy integration, integrated energy systems, carbon neutrality, bio-inspired optimization, operational control, decarbonization, renewable energy forecasting, energy management
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
