The DOI matches its Crossref record; 190 of the 190 references it cites were found in a registry or an index.
Potential of Explainable Artificial Intelligence in Advancing Renewable Energy: Challenges and Prospects
Abstract
Modern machine learning (ML) techniques are making inroads in every aspect of renewable energy for optimization and model prediction. The effective utilization of ML techniques for the development and scaling up of renewable energy systems needs a high degree of accountability. However, most of the ML approaches currently in use are termed black box since their work is difficult to comprehend. Explainable artificial intelligence (XAI) is an attractive option to solve the issue of poor interoperability in black-box methods. This review investigates the relationship between renewable energy (RE) and XAI. It emphasizes the potential advantages of XAI in improving the performance and efficacy of RE systems. It is realized that although the integration of XAI with RE has enormous potential to alter how energy is produced and consumed, possible hazards and barriers remain to be overcome, particularly concerning transparency, accountability, and fairness. Thus, extensive research is required to address the societal and ethical implications of using XAI in RE and to create standardized data sets and evaluation metrics. In summary, this paper shows the potential, perspectives, opportunities, and challenges of XAI application to RE system management and operation aiming to target the efficient energy-use goals for a more sustainable and trustworthy future.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
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?
In the citing paper
“The 'black box' nature of complex AI models hinders understanding of their decisionmaking, a critical barrier in high-stakes energy applications [25,26].”
From Addressing Challenges for the Effective Adoption of Artificial Intelligence in the Energy Sector (2025) · cites this paper as [26]
In the citing paper
“The focus has been shifting from initial implementation hurdles toward ensuring the Trustworthiness and transparency of deployed systems, as seen in the growing body of work on XAI [25,26].”
From Addressing Challenges for the Effective Adoption of Artificial Intelligence in the Energy Sector (2025) · cites this paper as [26]
In the citing paper
“Lack of transparency (XAI, Ethics; Sections 3.1.4and 3.4.2),fears of job displacement (Section 3.4.1),data privacy concerns (Sections 3.1.3and 3.4.2) [22,65], perceptions of unfairness/bias [26], and concerns about AI's environmental/safety impacts (Section 3.2.4)can fuel opposition.”
From Addressing Challenges for the Effective Adoption of Artificial Intelligence in the Energy Sector (2025) · cites this paper as [26]
In the citing paper
“Biased AI, trained on historical data reflecting societal inequities [26,63], could lead to discriminatory outcomes in resource allocation or pricing, eroding public trust (Section 3.4.3).”
From Addressing Challenges for the Effective Adoption of Artificial Intelligence in the Energy Sector (2025) · cites this paper as [26]
In the citing paper
“a practice increasingly called for in energy justice research [26,64]”
From Addressing Challenges for the Effective Adoption of Artificial Intelligence in the Energy Sector (2025) · cites this paper as [26]
Citations
Cites
Rudin, C. (2019) · Nature Machine Intelligence · doi:10.1038/s42256-019-0048-x · Cited by 10,561
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
A comprehensive study of renewable energy sources: Classifications, challenges and suggestions
Ang, T.-Z., Salem, M., Kamarol, M., Das, H. S., Nazari, M. A., & Prabaharan, N. (2022) · Energy Strategy Reviews · doi:10.1016/j.esr.2022.100939 · Cited by 1,151
Rahman, A., Farrok, O., & Haque, M. M. (2022) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2022.112279 · Cited by 1,105
Yang, G., Ye, Q., & Xia, J. (2022) · Information Fusion · doi:10.1016/j.inffus.2021.07.016 · Cited by 717
Adebayo, T. S., & Kırıkkaleli, D. (2021) · Environment Development and Sustainability · doi:10.1007/s10668-021-01322-2 · Cited by 599
Prediction of solar energy guided by pearson correlation using machine learning
Jebli, I., Belouadha, F.-Z., Kabbaj, M. I., & Tilioua, A. (2021) · Energy · doi:10.1016/j.energy.2021.120109 · Cited by 590
Olu-Ajayi, R., Alaka, H., Sulaimon, I. A., Sunmola, F. T., & Ajayi, S. O. (2021) · Journal of Building Engineering · doi:10.1016/j.jobe.2021.103406 · Cited by 550
Artificial Intelligence Applied to Battery Research: Hype or Reality?
Lombardo, T., Duquesnoy, M., El-Bouysidy, H., Årén, F., Gallo‐Bueno, A., Jørgensen, P. B., Bhowmik, A., Demortière, A., Ayerbe, E., Alcaide, F., et al. (2021) · Chemical Reviews · doi:10.1021/acs.chemrev.1c00108 · Cited by 536
Efficiency of geothermal power plants: A worldwide review
Zarrouk, S. J., & Moon, H. (2014) · Geothermics · doi:10.1016/j.geothermics.2013.11.001 · Cited by 535
Ağbulut, Ü., Gürel, A. E., & Biçen, Y. (2020) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2020.110114 · Cited by 456
Solving the Black Box Problem: A Normative Framework for Explainable Artificial Intelligence
Zednik, C. (2021) · Philosophy & Technology · doi:10.1007/s13347-019-00382-7 · Cited by 419
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
Sharifzadeh, M., Sikinioti-Lock, A., & Shah, N. (2019) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2019.03.040 · Cited by 397
Khan, W. A., Walker, S. S. W., & Zeiler, W. (2021) · Energy · doi:10.1016/j.energy.2021.122812 · Cited by 367
Interpretable machine learning for building energy management: A state-of-the-art review
Chen, Z., Xiao, F., Guo, F., & Yan, J. (2023) · Advances in Applied Energy · doi:10.1016/j.adapen.2023.100123 · Cited by 357
Füller, J., Hutter, K., Wahl, J., Bilgram, V., & Tekic, Z. (2022) · Technological Forecasting and Social Change · doi:10.1016/j.techfore.2022.121598 · Cited by 335
Determinants of renewable energy production in transition economies: A panel data approach
Przychodzeń, W., & Przychodzeń, J. (2019) · Energy · doi:10.1016/j.energy.2019.116583 · Cited by 300
Ağbulut, Ü. (2021) · Sustainable Production and Consumption · doi:10.1016/j.spc.2021.10.001 · Cited by 280
Peter, A. P., Koyande, A. K., Chew, K. W., Ho, S., Chen, W., Chang, J., Rambabu, K., BANAT, F. A., & Show, P. L. (2021) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2021.111852 · Cited by 260
Self-citationA comprehensive study of geothermal heating and cooling systems
Soltani, M., Kashkooli, F. M., Dehghani-Sanij, A., Kazemi, A., Bordbar, N., Farshchi-Heydari, M.-J., Elmi, M., Gharali, K., & Dusseault, M. B. (2018) · Sustainable Cities and Society · doi:10.1016/j.scs.2018.09.036 · Cited by 258
Potential for hydrogen production from sustainable biomass with carbon capture and storage
Rosa, L., & Mazzotti, M. (2022) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2022.112123 · Cited by 257
Ren, L., Zhou, S., & Ou, X. (2020) · Energy · doi:10.1016/j.energy.2020.118482 · Cited by 256
Kuzlu, M., Cali, Ü., Sharma, V., & Güler, Ö. (2020) · IEEE Access · doi:10.1109/access.2020.3031477 · Cited by 246
Park, J., Lee, W. H., Kim, K. T., Park, C. Y., Lee, S., & Heo, T.-Y. (2022) · Science of The Total Environment · doi:10.1016/j.scitotenv.2022.155070 · Cited by 243
An overview of water desalination systems integrated with renewable energy sources
Ghazi, Z. M., Rizvi, S. W. F., Shahid, W. M., Abdulhameed, A. M., Saleem, H., & Zaidi, S. M. J. (2022) · Desalination · doi:10.1016/j.desal.2022.116063 · Cited by 238
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
Dell’Anna, F. (2020) · Energy Policy · doi:10.1016/j.enpol.2020.112031 · Cited by 214
Wang, C., Raza, S. A., Adebayo, T. S., Sun, Y., & Shah, M. I. (2022) · Energy · doi:10.1016/j.energy.2022.125303 · Cited by 214
Ryo, M., Angelov, B., Mammola, S., Kass, J. M., Benito, B. M., & Hartig, F. (2020) · Ecography · doi:10.1111/ecog.05360 · Cited by 200
Wu, Y., & Zhou, Y. (2022) · Construction and Building Materials · doi:10.1016/j.conbuildmat.2022.127298 · Cited by 200
Singh, N. K., Yadav, M. K., Singh, V., Padhiyar, H., Kumar, V., Bhatia, S. K., & Show, P. L. (2022) · Bioresource Technology · doi:10.1016/j.biortech.2022.128486 · Cited by 195
Mitrentsis, G., & Lens, H. (2022) · Applied Energy · doi:10.1016/j.apenergy.2021.118473 · Cited by 192
Sharma, M., Luthra, S., Joshi, S., & Kumar, A., V (2021) · Government Information Quarterly · doi:10.1016/j.giq.2021.101624 · Cited by 184
Granovskii, M., Dinçer, İ., & Rosen, M. A. (2006) · International Journal of Hydrogen Energy · doi:10.1016/j.ijhydene.2006.09.029 · Cited by 183
Energy investigation of glazed windows containing Nano-PCM in different seasons
Li, D., Wu, Y., Liu, C., Zhang, G., & Arıcı, M. (2018) · Energy Conversion and Management · doi:10.1016/j.enconman.2018.07.015 · Cited by 182
Repurposing abandoned wells for geothermal energy: Current status and future prospects
Santos, L. Y., Taleghani, A. D., & Elsworth, D. J. (2022) · Renewable Energy · doi:10.1016/j.renene.2022.05.138 · Cited by 180
Fan, J., Wu, L., Ma, X., Zhou, H., & Zhang, F. (2019) · Renewable Energy · doi:10.1016/j.renene.2019.07.104 · Cited by 176
Aghahosseini, A., Solomon, A. A., Breyer, C., Pregger, T., Simon, S., Strachan, P. A., & Jäger‐Waldau, A. (2022) · Applied Energy · doi:10.1016/j.apenergy.2022.120401 · Cited by 176
The development of carbon capture and storage (CCS) in India: A critical review
Shaw, R., & Mukherjee, S. (2022) · Carbon Capture Science & Technology · doi:10.1016/j.ccst.2022.100036 · Cited by 169
Wang, G., Sadiq, M., Bashir, T., Jain, V., Ali, S. A., & Shabbir, M. S. (2022) · Energy Strategy Reviews · doi:10.1016/j.esr.2022.100886 · Cited by 166
Tang, Q., Chen, Y., Yang, H., Liu, M., Xiao, H., Wu, Z., Chen, H., & Naqvi, S. R. (2020) · Energy & Fuels · doi:10.1021/acs.energyfuels.0c01893 · Cited by 164
Artificial intelligence for sustainable energy: A contextual topic modeling and content analysis
Saheb, T., Dehghani, M., & Saheb, T. (2022) · Sustainable Computing Informatics and Systems · doi:10.1016/j.suscom.2022.100699 · Cited by 162
Salem, H., Kabeel, A. E., El‐Said, E. M. S., & Elzeki, O. M. (2021) · Desalination · doi:10.1016/j.desal.2021.115411 · Cited by 160
Lu, W. (2017) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph14111436 · Cited by 154
Mehrenjani, J. R., Gharehghani, A., & Sangesaraki, A. G. (2022) · Energy Conversion and Management · doi:10.1016/j.enconman.2022.115266 · Cited by 154
Shahid, F., Wood David, A., Humaira, N., Zameer, A., & Eger, S. (2022) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2022.112700 · Cited by 153
A review of solar-driven organic Rankine cycles: Recent challenges and future outlook
Loni, R., Mahian, O., Markides, C. N., Bellos, E., Roux, W. G. L., Kasaeian, A., Najafi, G., & Rajaee, F. (2021) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2021.111410 · Cited by 147
Forecasting of solar radiation using different machine learning approaches
Demir, V., & Çıtakoğlu, H. (2022) · Neural Computing and Applications · doi:10.1007/s00521-022-07841-x · Cited by 144
A review of the use of artificial intelligence methods in infrastructure systems
McMillan, L., & Varga, L. (2022) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2022.105472 · Cited by 144
Salah, S. I., Eltaweel, M., & Abeykoon, C. (2022) · Cleaner Engineering and Technology · doi:10.1016/j.clet.2022.100497 · Cited by 143
He, Z., Guo, W., & Zhang, P. (2021) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2021.111977 · Cited by 139
Ullah, I., Liu, K., Yamamoto, T., Zahid, M., & Jamal, A. (2022) · International Journal of Energy Research · doi:10.1002/er.8219 · Cited by 136
A novel hybrid decision making approach for the strategic selection of wind energy projects
Dong, W., Zhao, G., Yüksel, S., Dınçer, H., & Ubay, G. G. (2021) · Renewable Energy · doi:10.1016/j.renene.2021.12.077 · Cited by 134
Carneiro, T. C., Rocha, P. A. C., de Carvalho, P. C. M., & RAMIREZ, L. M. F. (2022) · Applied Energy · doi:10.1016/j.apenergy.2022.118936 · Cited by 134
Franco, A., & Salza, P. (2010) · Renewable Energy · doi:10.1016/j.renene.2010.07.022 · Cited by 132
Al‐Ghussain, L., Ahmad, A. D., Abubaker, A. M., & Hassan, M. A. (2022) · International Journal of Hydrogen Energy · doi:10.1016/j.ijhydene.2022.04.289 · Cited by 126
Wu, Q., Zheng, H., Guo, X., & Liu, G. (2022) · Renewable Energy · doi:10.1016/j.renene.2022.09.036 · Cited by 118
Amir, M., & Khan, S. Z. (2021) · Energy and Built Environment · doi:10.1016/j.enbenv.2021.03.002 · Cited by 115
Analysis of a clean hydrogen liquefaction plant integrated with a geothermal system
Seyam, S., Dinçer, İ., & Agelin‐Chaab, M. (2019) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2019.118562 · Cited by 113
Extreme Learning Machines for Solar Photovoltaic Power Predictions
Al‐Dahidi, S., Ayadi, O., Adeeb, J., Alrbai, M., & Qawasmeh, B. R. (2018) · Energies · doi:10.3390/en11102725 · Cited by 111
Shokri, A., & Fard, M. S. (2022) · Environmental Advances · doi:10.1016/j.envadv.2022.100281 · Cited by 111
Shyu, C.-W. (2021) · Energy Research & Social Science · doi:10.1016/j.erss.2021.102199 · Cited by 106
Adedeji, P. A., Akinlabi, S. A., Madushele, N., & Olatunji, O. O. (2020) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2020.122104 · Cited by 106
Ashraf, M., Ayaz, M., Khan, M., Adil, S. F., Farooq, W., Ullah, N., & Tahir, M. N. (2023) · Energy & Fuels · doi:10.1021/acs.energyfuels.2c04077 · Cited by 105
Membrane distillation driven by solar energy: A review
Shalaby, S. M., Kabeel, A. E., Abosheiasha, H. F., Elfakharany, M. K., El-Bialy, E., Shama, A., & Vidić, R. D. (2022) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2022.132949 · Cited by 104
Solar irradiance forecasting based on direct explainable neural network
Wang, H., Ren, C., Zhou, B., Aziz, S., Qin, B., Voropai, N., Gan, L., & Алексеевич, Б. Е. (2020) · Energy Conversion and Management · doi:10.1016/j.enconman.2020.113487 · Cited by 103
A Novel IoT-Based Explainable Deep Learning Framework for Intrusion Detection Systems
Houda, Z. A. E., Brik, B., & Senouci, S. (2022) · IEEE Internet of Things Magazine · doi:10.1109/iotm.005.2200028 · Cited by 102
Hu, X., Yang, B., Ke, S., Liu, Y., Fang, M., Huang, Z., & Min, X. (2023) · Energy & Fuels · doi:10.1021/acs.energyfuels.3c01265 · Cited by 102
Three-dimensional spatiotemporal wind field reconstruction based on physics-informed deep learning
Zhang, J., & Zhao, X. (2021) · Applied Energy · doi:10.1016/j.apenergy.2021.117390 · Cited by 101
Eyerer, S., Schifflechner, C., Hofbauer, S., Bauer, W., Wieland, C. M., & Spliethoff, H. (2019) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2019.109661 · Cited by 99
Weather Impact on Solar Farm Performance: A Comparative Analysis of Machine Learning Techniques
Gopi, A., Sharma, P., Sudhakar, K., Ngui, W. K., Kirpichnikova, I., & Cüce, E. (2022) · Sustainability · doi:10.3390/su15010439 · Cited by 98
Self-citationPhysics-informed deep learning model in wind turbine response prediction
Li, X., & Zhang, W. (2021) · Renewable Energy · doi:10.1016/j.renene.2021.12.058 · Cited by 92
Sinha, P., Paul, K., Deb, S., & Sachan, S. (2023) · Energies · doi:10.3390/en16062924 · Cited by 91
Sohani, A., Hoseinzadeh, S., Samiezadeh, S., & Verhaert, I. (2021) · Journal of Thermal Analysis and Calorimetry · doi:10.1007/s10973-021-10744-z · Cited by 90
Hu, G., & You, F. (2022) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2022.112790 · Cited by 90
Nwokolo, S. C., Obiwulu, A. U., & Ogbulezie, J. C. (2023) · Physics and Chemistry of the Earth Parts A/B/C · doi:10.1016/j.pce.2023.103389 · Cited by 89
Directed graph deep neural network for multi-step daily streamflow forecasting
Liu, Y., Hou, G., Huang, F., Qin, H., Wang, B., & Yi, L. (2022) · Journal of Hydrology · doi:10.1016/j.jhydrol.2022.127515 · Cited by 87
A state of art review on estimation of solar radiation with various models
Gürel, A. E., Ağbulut, Ü., Bakır, H., Ergün, A., & Yıldız, G. (2023) · Heliyon · doi:10.1016/j.heliyon.2023.e13167 · Cited by 83
Said, Z., Sharma, P., Elavarasan, R. M., Tiwari, A. K., & Rathod, M. K. (2022) · Journal of Energy Storage · doi:10.1016/j.est.2022.105230 · Cited by 82
Self-citationAl-Buraiki, A. S., & Al‐Sharafi, A. (2021) · Energy Conversion and Management · doi:10.1016/j.enconman.2021.114899 · Cited by 79
Tuấn, L. A., Pandey, A., Sirohi, R., Sharma, P., Chen, W., Pham, N. D. K., Trần, V. D., Nguyễn, X. P., & Hoang, A. T. (2023) · Energy & Fuels · doi:10.1021/acs.energyfuels.3c02868 · Cited by 79
Self-citationPredicting biodiesel properties and its optimal fatty acid profile via explainable machine learning
Suvarna, M., Jahirul, M. I., Aaron-Yeap, W. H., Augustine, C. V., Umesh, A., Rasul, M. G., Günay, M. E., Yıldırım, R., & Janaun, J. (2022) · Renewable Energy · doi:10.1016/j.renene.2022.02.124 · Cited by 78
Explaining CNN and RNN Using Selective Layer-Wise Relevance Propagation
Jung, Y., Han, S. H., & Choi, H. (2021) · IEEE Access · doi:10.1109/access.2021.3051171 · Cited by 77
Toward Totally Flexible Dye-Sensitized Solar Cells Based on Titanium Grids and Polymeric Electrolyte
Gerosa, M., Sacco, A., Scalia, A., Bella, F., Chiodoni, A. M., Quaglio, M., Tresso, E. M., & Bianco, S. (2016) · IEEE Journal of Photovoltaics · doi:10.1109/jphotov.2016.2514702 · Cited by 77
LINDA-BN: An interpretable probabilistic approach for demystifying black-box predictive models
Moreira, C., Chou, Y.-L., Velmurugan, M., Ouyang, C., Sindhgatta, R., & Bruza, P. (2021) · Decision Support Systems · doi:10.1016/j.dss.2021.113561 · Cited by 76
Alassery, F., Alzahrani, A. A., Khan, A. I., Irshad, K., & Islam, S. (2022) · Sustainable Energy Technologies and Assessments · doi:10.1016/j.seta.2022.102060 · Cited by 76
Amiri, S. S., Mueller, M., & Hoque, S. (2023) · Energy and Buildings · doi:10.1016/j.enbuild.2023.112965 · Cited by 75
Tsoka, T., Ye, X., Chen, Y., Gong, D., & Xia, X. (2022) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2022.131626 · Cited by 72
Li, G., Li, F., Xu, C., & Fang, X. (2022) · Energy and Buildings · doi:10.1016/j.enbuild.2022.112317 · Cited by 71
Singh, H., Roy, A., Setia, R. K., & Pateriya, B. (2021) · Modeling Earth Systems and Environment · doi:10.1007/s40808-021-01243-z · Cited by 70
Tahir, M. D., Sherryna, A., Khan, A. A., Madi, M. O., Zerga, A. Y., & Tahir, B. (2022) · Energy & Fuels · doi:10.1021/acs.energyfuels.2c01256 · Cited by 69
Trends and prospects of geothermal energy as an alternative source of power: A comprehensive review
Islam, M. T., Nabi, M. N., Arefin, M. A., Mostakim, K., Rashid, F., Hassan, N. M. S., Rahman, S. M. A., McIntosh, S. R., Mullins, B. J., & Muyeen, S. M. (2022) · Heliyon · doi:10.1016/j.heliyon.2022.e11836 · Cited by 69
Alnaqbi, S. A., Alasad, S., Aljaghoub, H., Alami, A. H., Abdelkareem, M. A., & Olabi, A. G. (2022) · Energies · doi:10.3390/en15072412 · Cited by 68
Machine learning approach for the prediction of biomass pyrolysis kinetics from preliminary analysis
Balsora, H. K., Kartik, S., Dua, V., Joshi, J. B., Kataria, G., Sharma, A., & Chakinala, A. G. (2022) · Journal of environmental chemical engineering · doi:10.1016/j.jece.2022.108025 · Cited by 68
Sasser, C., Yu, M., & Delgado, R. (2021) · Renewable Energy · doi:10.1016/j.renene.2021.10.034 · Cited by 67
AI Ethics for Sustainable Development Goals
Astobiza, A. M., Martín, M. T., Aparicio, M., & López, D. (2021) · IEEE Technology and Society Magazine · doi:10.1109/mts.2021.3056294 · Cited by 66
Yılmaz, C., & Sen, O. (2022) · International Journal of Hydrogen Energy · doi:10.1016/j.ijhydene.2022.03.140 · Cited by 66
Gahlot, R., Mir, S., & Dhawan, N. (2022) · Energy & Fuels · doi:10.1021/acs.energyfuels.2c02847 · Cited by 66
Towards a sustainable energy future: Factors affecting solar-hydrogen energy production in China
Huang, W., Dai, J., & Xiong, L. (2022) · Sustainable Energy Technologies and Assessments · doi:10.1016/j.seta.2022.102059 · Cited by 65
Explainable artificial intelligence for photovoltaic fault detection: A comparison of instruments
Utama, C., Meske, C., Schneider, J., Schlatmann, R., & Ulbrich, C. (2022) · Solar Energy · doi:10.1016/j.solener.2022.11.018 · Cited by 65
Microalgal-Based Bioenergy: Strategies, Prospects, and Sustainability
Choudhary, S., Tripathi, S., & Poluri, K. M. (2022) · Energy & Fuels · doi:10.1021/acs.energyfuels.2c02922 · Cited by 64
Soltani, S. R. K., Mostafaeipour, A., Almutairi, K., Dehshiri, S. J. H., Dehshiri, S. S. H., & Techato, K. (2021) · Sustainable Energy Technologies and Assessments · doi:10.1016/j.seta.2021.101849 · Cited by 63
Optimization of a solar-wind- grid powered desalination system in Saudi Arabia
Ghaithan, A. M., Hanbali, A. A., Mohammed, A., Attia, A. M., Saleh, H., & Alsawafy, O. G. (2021) · Renewable Energy · doi:10.1016/j.renene.2021.06.060 · Cited by 63
Zhou, H., Yao, W., Zhou, M., Ai, X., Wen, J., & Cheng, S. (2022) · IEEE Transactions on Power Systems · doi:10.1109/tpwrs.2022.3179443 · Cited by 63
Using Artificial Neural Networks for Predicting Ship Fuel Consumption
Nguyen, V. G., Rajamohan, S., Rudzki, K., Kozak, J., Sharma, P., Pham, N. D. K., Nguyen, P. Q. P., & Nguyễn, X. P. (2023) · Polish Maritime Research · doi:10.2478/pomr-2023-0020 · Cited by 63
Self-citationZhang, X., Li, H., Manigandan, S., Elgendi, M., Krishnamoorthy, N. R., Xia, C., & Matharasi, D. P. (2022) · Fuel · doi:10.1016/j.fuel.2022.126292 · Cited by 62
Hacıefendioğlu, K., Başağa, H. B., Yavuz, Z., & Karimi, M. T. (2021) · Renewable Energy · doi:10.1016/j.renene.2021.10.025 · Cited by 61
Prediction of IC engine performance and emission parameters using machine learning: A review
Karunamurthy, K., Janvekar, A. A., Palaniappan, P. L., Adhitya, V., Lokeswar, T. T. K., & Harish, J. (2023) · Journal of Thermal Analysis and Calorimetry · doi:10.1007/s10973-022-11896-2 · Cited by 61
Moon, J., Rho, S., & Baik, S. W. (2022) · Sustainable Energy Technologies and Assessments · doi:10.1016/j.seta.2022.102888 · Cited by 60
Sehrawat, N., Vashisht, S., & Singh, A. (2023) · International Journal of Intelligent Networks · doi:10.1016/j.ijin.2023.04.001 · Cited by 59
Response of tidal dynamics to successive land reclamation in the Lingding Bay over the last century
Chu, N., Yao, P., Ou, S., Wang, H., Yang, H., & Yang, Q. (2022) · Coastal Engineering · doi:10.1016/j.coastaleng.2022.104095 · Cited by 55
Edge-based Explainable Fault Detection Systems for photovoltaic panels on edge nodes
Sairam, S., Seshadhri, S., Marafioti, G., Srinivasan, S., Mathisen, G., & Bekiroglu, K. (2021) · Renewable Energy · doi:10.1016/j.renene.2021.10.063 · Cited by 53
Wu, J., Qin, L., Chen, N., Chen, Q., & Zheng, S. (2022) · Energy · doi:10.1016/j.energy.2022.123318 · Cited by 53
Owebor, K., Diemuodeke, O. E., & Briggs, T. A. (2021) · Energy · doi:10.1016/j.energy.2021.122748 · Cited by 51
Chang, Y., Wei, Y., Zhang, J., Xu, X. S., Zhang, L., & Zhao, Y. (2020) · Resources Conservation and Recycling · doi:10.1016/j.resconrec.2020.105197 · Cited by 50
Understanding Fuel Saving and Clean Fuel Strategies Towards Green Maritime
Nguyen, V. N., Rudzki, K., Dzida, M., Pham, N. D. K., Pham, M. T., Nguyen, P. Q. P., & Nguyễn, X. P. (2023) · Polish Maritime Research · doi:10.2478/pomr-2023-0030 · Cited by 50
Self-citationMaintenance cost minimization models for offshore wind farms: A systematic and critical review
Tusar, M. I. H., & Sarker, B. R. (2021) · International Journal of Energy Research · doi:10.1002/er.7425 · Cited by 49
An economic investigation of the wind-hydrogen projects: A case study
Almutairi, K., Dehshiri, S. S. H., Dehshiri, S. J. H., Mostafaeipour, A., & Techato, K. (2022) · International Journal of Hydrogen Energy · doi:10.1016/j.ijhydene.2022.05.070 · Cited by 49
Hosseini, V. R., Mehrizi, A. A., Güngör, A., & Afrouzi, H. H. (2022) · Fuel · doi:10.1016/j.fuel.2022.125908 · Cited by 49
Understanding electricity prices beyond the merit order principle using explainable AI
Trebbien, J., Gorjão, L. R., Praktiknjo, A., Schäfer, B., & Witthaut, D. (2023) · Energy and AI · doi:10.1016/j.egyai.2023.100250 · Cited by 49
Advances and Perspectives of Bio-oil Hydrotreatment for Biofuel Production
Gholizadeh, M., Zhang, S., Hu, X., & Wang, Y. (2023) · Energy & Fuels · doi:10.1021/acs.energyfuels.3c01647 · Cited by 48
Salem, H., El‐Hasnony, I. M., Kabeel, A. E., El‐Said, E. M. S., & Elzeki, O. M. (2022) · Alexandria Engineering Journal · doi:10.1016/j.aej.2022.03.050 · Cited by 46
Li, H., Zhang, R., Mahmud, M. A., & Hredzak, B. (2022) · Applied Energy · doi:10.1016/j.apenergy.2022.119019 · Cited by 46
Heo, S., Ko, J., Kim, S., Jeong, C. H., Hwangbo, S., & Yoo, C. (2022) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2022.134793 · Cited by 45
Ugwu, J., Odo, K. C., Oluka, L. O., & Salami, K. O. (2021) · International Journal of Renewable Energy Development · doi:10.14710/ijred.2022.40359 · Cited by 42
Ariaei, A. R., Jahangiri, M., Fakhr, M. H., & Shamsabadi, A. A. (2019) · International Journal of Renewable Energy Development · doi:10.14710/ijred.8.2.149-160 · Cited by 41
A Machine Learning Methodology for Predicting Geothermal Heat Flow in the Bohai Bay Basin, China
He, J., Li, K., Wang, X., Gao, N., Mao, X., & Jia, L. (2022) · Natural Resources Research · doi:10.1007/s11053-021-10002-x · Cited by 41
Mishra, S., Saini, G., Saha, S., Chauhan, A., Kumar, A., & Maity, S. (2022) · Sustainable Energy Technologies and Assessments · doi:10.1016/j.seta.2022.102246 · Cited by 41
Altay, E. V., Gürgenç, E., Altay, O., & Dikici, A. (2022) · Geothermics · doi:10.1016/j.geothermics.2022.102476 · Cited by 41
Islam, M. S., Awal, M. A., Laboni, J. N., Pinki, F. T., Karmokar, S., Mumenin, K. M., Al-Ahmadi, S. A., Rahman, M. A., Hossain, M. S., & Mirjalili, S. (2022) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2022.105671 · Cited by 40
Techno-Economic Approach to Solar Energy Systems Onboard Marine Vehicles
Salem, A., & Seddiek, I. S. (2016) · Polish Maritime Research · doi:10.1515/pomr-2016-0033 · Cited by 38
A New Modeling Framework for Geothermal Operational Optimization with Machine Learning (GOOML)
Buster, G., Siratovich, P., Taverna, N., Rossol, M. N., Weers, J., Blair, A., Huggins, J., Siega, C., Mannington, W. I., Urgel, A., et al. (2021) · Energies · doi:10.3390/en14206852 · Cited by 38
A Critical Outlook on Lignocellulosic Biomass and Plastics Co-Gasification: A Mini-Review
Halba, A., Thengane, S. K., & Arora, P. (2022) · Energy & Fuels · doi:10.1021/acs.energyfuels.2c02907 · Cited by 38
Lindorfer, J., Rosenfeld, D. C., & Böhm, H. (2020) · Elsevier eBooks · doi:10.1016/b978-0-08-102886-5.00023-2 · Cited by 37
Lin, Y., Li, B., Moiser, T. M., Griffel, L. M., Mahalik, M., Kwon, J., & Alam, S. (2022) · Journal of Energy Storage · doi:10.1016/j.est.2022.104123 · Cited by 36
Kim, Y., Cho, J., Naser, N., Kumar, S., Jeong, K., McCormick, R. L., John, P. C. S., & Kim, S. (2022) · Proceedings of the Combustion Institute · doi:10.1016/j.proci.2022.09.059 · Cited by 36
Mohamed, E. A., Betiha, M. A., & Negm, N. A. M. (2023) · Energy & Fuels · doi:10.1021/acs.energyfuels.2c03887 · Cited by 35
Analysis of a wind-PV battery hybrid renewable energy system for a dc microgrid
Doshi, K., & Harish, V. S. K. V. (2020) · Materials Today Proceedings · doi:10.1016/j.matpr.2020.09.194 · Cited by 34
Tlili, I., Sajadi, S. M., Bǎleanu, D., & Ghaemi, F. (2022) · Sustainable Energy Technologies and Assessments · doi:10.1016/j.seta.2022.102100 · Cited by 34
Bhattarai, U., Devkota, L. P., Marahatta, S., Shrestha, D., & Maraseni, T. (2022) · Environmental Research · doi:10.1016/j.envres.2022.113746 · Cited by 34
Wang, B., Pei, W., Xue, B., & Zhang, M. (2022) · IEEE Transactions on Evolutionary Computation · doi:10.1109/tevc.2022.3225591 · Cited by 33
Bao, B., & Wang, Q. (2020) · Energy Conversion and Management · doi:10.1016/j.enconman.2020.113619 · Cited by 32
Musharavati, F., Ahmadi, P., & Khanmohammadi, S. (2021) · Journal of Thermal Analysis and Calorimetry · doi:10.1007/s10973-021-10793-4 · Cited by 32
Adebayo, T. S. (2022) · Environmental Science and Pollution Research · doi:10.1007/s11356-022-23455-1 · Cited by 31
Xin, Y., & Long, D. (2023) · Renewable Energy · doi:10.1016/j.renene.2023.02.102 · Cited by 31
Yang, M., Xu, C., Bai, Y., Ma, M., & Su, X. (2025) · CSEE Journal of Power and Energy Systems · doi:10.17775/cseejpes.2021.07470 · Cited by 31
Ishaq, H., & Dinçer, İ. (2021) · Journal of Energy Resources Technology · doi:10.1115/1.4050875 · Cited by 30
Hao, Y., Baik, J., Tran, H., & Choi, M. (2022) · Journal of Hydrology · doi:10.1016/j.jhydrol.2022.127543 · Cited by 29
Icaza, D., Arias, P., Jurado, F., & Tostado‐Véliz, M. (2022) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2022.135298 · Cited by 29
Shi, Y., & Luo, W. (2018) · Polish Maritime Research · doi:10.2478/pomr-2018-0090 · Cited by 28
Yang, Y., Zhang, Y., Cheng, Y., Lei, Z., Gao, X., Huang, Y., & Ma, Y. (2023) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2023.135879 · Cited by 28
Designing hydro-energy led economic growth for pollution abatement: evidence from BRICS
Dash, D. P., Dash, A. K., & Sethi, N. (2022) · Environmental Science and Pollution Research · doi:10.1007/s11356-021-17890-9 · Cited by 27
Preliminary Analysis of the Use of Solid Biofuels in a Ship’s Power System
Zeńczak, W., & Krystosik-Gromadzińska, A. (2020) · Polish Maritime Research · doi:10.2478/pomr-2020-0067 · Cited by 26
Execution of synthetic Bayesian model average for solar energy forecasting
Abedinia, O., & Bagheri, M. (2022) · IET Renewable Power Generation · doi:10.1049/rpg2.12389 · Cited by 26
A Survey on Explainable Anomaly Detection for Industrial Internet of Things
Huang, Z., & Wu, Y. (2022) · IEEE Conference on Dependable and Secure Computing (DSC) · doi:10.1109/dsc54232.2022.9888874 · Cited by 26
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
Explainable Artificial Intelligence (XAI): Motivation, Terminology, and Taxonomy
Notovich, A., Gal, H. C., & Ben‐Gal, I. (2023) · Springer eBooks · doi:10.1007/978-3-031-24628-9_41 · Cited by 25
Fuentes, M. F., Eras-Almeida, A. A., & Egido-Aguilera, M. A. (2021) · Sustainability · doi:10.3390/su13063032 · Cited by 23
Biomass resources and thermal conversion biomass to biofuel for cleaner energy: A review
Nguyen, T. B. N., & Le, N. V. L. (2023) · Journal of Emerging Science and Engineering · doi:10.61435/jese.2023.2 · Cited by 22
Geng, P., Xu, X., & Tarasiuk, T. (2020) · Polish Maritime Research · doi:10.2478/pomr-2020-0051 · Cited by 22
Techno-economic analysis of fixed versus sun-tracking solar panels
Gol, A. E., & Ščasný, M. (2023) · International Journal of Renewable Energy Development · doi:10.14710/ijred.2023.50165 · Cited by 22
Kumar, S., & Maiti, P. (2023) · Energy & Fuels · doi:10.1021/acs.energyfuels.3c01695 · Cited by 22
Bortoluzzi, M. B. D. O., Furlan, M., & Neto, J. F. D. R. (2022) · Renewable Energy · doi:10.1016/j.renene.2022.10.066 · Cited by 21
Nimmy, S. F., Hussain, F. K., Chakrabortty, R. K., Hussain, F. K., & Saberi, M. (2023) · Knowledge-Based Systems · doi:10.1016/j.knosys.2023.110552 · Cited by 20
Systematic complexity reduction of wave-to-wire models for wave energy system design
Peñalba, M., & Ringwood, J. V. (2020) · Ocean Engineering · doi:10.1016/j.oceaneng.2020.107651 · Cited by 20
Exploring deterministic frequency deviations with explainable AI
Kruse, J., Schäfer, B., & Witthaut, D. (2021) · IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm) · doi:10.1109/smartgridcomm51999.2021.9632335 · Cited by 20
Wind Tunnel Experiment of Multi-Mode ARC Sail Device
Zhang, H., Hu, Y., & He, J. (2021) · Polish Maritime Research · doi:10.2478/pomr-2021-0046 · Cited by 20
Rajapaksha, D., & Bergmeir, C. (2022) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v36i11.21469 · Cited by 20
Rule reduction for control of a building cooling system using explainable AI
Cho, S., & Park, C. S. (2022) · Journal of Building Performance Simulation · doi:10.1080/19401493.2022.2103586 · Cited by 20
Ehteram, M., Graf, R., Ahmed, A. N., & El‐Shafie, A. (2022) · Stochastic Environmental Research and Risk Assessment · doi:10.1007/s00477-022-02235-w · Cited by 19
A Short Review on Explainable Artificial Intelligence in Renewable Energy and Resources
Ersöz, B., Sağıroğlu, Ş., & Bülbül, H. İ. (2022) · International Conference on Renewable Energy Research and Applications (ICRERA) · doi:10.1109/icrera55966.2022.9922870 · Cited by 19
Sarkar, D., Shukla, S., Alom, N., Sharma, P., & Bora, B. J. (2022) · Journal of Energy Resources Technology · doi:10.1115/1.4056331 · Cited by 17
Self-citationLin, B., & Shi, L. (2022) · Sustainable Energy Technologies and Assessments · doi:10.1016/j.seta.2022.101962 · Cited by 15
VS-CAM: Vertex Semantic Class Activation Mapping to Interpret Vision Graph Neural Network
Feng, Z., Cui, X., Ji, H., Zhu, M., & Stanković, L. (2023) · Neurocomputing · doi:10.1016/j.neucom.2023.02.057 · Cited by 15
Integrated Gradients for Feature Assessment in Point Cloud-Based Data Sets
Schwegler, M., Müller, C. E., & Reiterer, A. (2023) · Algorithms · doi:10.3390/a16070316 · Cited by 15
Neural Network modelling for prediction of energy in hybrid renewable energy systems
Roseline, J. F., Dhanya, D., Selvan, S., Yuvaraj, M., Duraipandy, P., Kumar, S. S., Prasad, A. R., Sathyamurthy, R., & Mohanavel, V. (2022) · Energy Reports · doi:10.1016/j.egyr.2022.10.284 · Cited by 14
Ilham, N. I., Hussin, M. Z., Dahlan, N. Y., & Setiawan, E. A. (2022) · International Journal of Renewable Energy Development · doi:10.14710/ijred.2022.45662 · Cited by 13
Yalama, V., Yakovleva, O., Trandafilov, V. V., & Khmelniuk, M. (2022) · Polish Maritime Research · doi:10.2478/pomr-2022-0028 · Cited by 13
Paturi, U. M. R., Cheruku, S., & Reddy, N. S. (2022) · Journal of the Brazilian Society of Mechanical Sciences and Engineering · doi:10.1007/s40430-022-03757-8 · Cited by 12
Zhang, J., Jia, X., & Hu, J. (2020) · International Conference on Digital Image Computing: Techniques and Applications (DICTA) · doi:10.1109/dicta51227.2020.9363379 · Cited by 11
Lu, S., Wei, W., Zhu, Z., Liang, Y., & Liu, H. (2022) · Frontiers in Energy Research · doi:10.3389/fenrg.2021.827942 · Cited by 10
Ferretti, C., & Saletta, M. (2022) · Algorithms · doi:10.3390/a15120449 · Cited by 7
Kozina, A., Radica, G., & Nižetić, S. (2021) · Journal of Energy Resources Technology · doi:10.1115/1.4053070 · Cited by 6
Li, Y., Huang, S. Y., Xu, S., Yuan, Z., Jiang, K., Wei, Y. Y., Zhang, J., Xiong, Q. Y., Wang, Z., Lin, R. T., et al. (2022) · The Astrophysical Journal Supplement Series · doi:10.3847/1538-4365/ac616c · Cited by 6
Numerical and experimental investigations on a bladeless turbine: Tesla's cohesion-type innovation
Sivaramakrishnaiah, M., Nasan, D. S., Sharma, P., Le, T. T., Tran, M. H., Nguyen, T. B. N., Nguyen, P. Q. P., & Trần, V. D. (2023) · International Journal of Renewable Energy Development · doi:10.14710/ijred.2024.55455 · Cited by 6
Self-citationLearning Non-linear White-box Predictors: A Use Case in Energy Systems
Wilfling, S., Ebrahimi, M., Alfalouji, Q., Schweiger, G., & Basirat, M. (2022) · International Conference on Machine Learning and Applications · doi:10.1109/icmla55696.2022.00082 · Cited by 5
Mohanty, P. K., Das, P., & Roy, D. S. (2022) · OITS International Conference on Information Technology (OCIT) · doi:10.1109/ocit56763.2022.00106 · Cited by 4
Benchmarking the Accuracy of Algorithms for Memory-Constrained Image Classification
Müksch, S., Olausson, T., Wilhelm, J., & Andreadis, P. (2020) · IEEE/ACM Symposium on Edge Computing (SEC) · doi:10.1109/sec50012.2020.00059 · Cited by 3
Bedoud, K., Bahi, T., & Hichem, M. (2022) · International Journal of Power Electronics and Drive Systems/International Journal of Electrical and Computer Engineering · doi:10.11591/ijpeds.v13.i3.pp1721-1733 · Cited by 1
Research works printed but not linked: 1
- Spreitzer, N.; Haned, H.; van der Linden, I. Evaluating the Practicality of Counterfactual Explanations NeurIPS 2022 Work. TSRML ; 2022; p 11.
Cited by
Sorted by citations
Explainable Artificial Intelligence (XAI)
Dey, M. T. (2024) · Advances in environmental engineering and green technologies book series · doi:10.4018/979-8-3693-7822-9.ch012 · Cited by 131
Major challenges for commercialization of perovskite solar cells: A critical review
Seyisi, T., Fouda-Mbanga, B. G., Mnyango, J. I., Nthwane, Y. B., Nyoni, B., Mhlanga, S. D., Hlangothi, S. P., & Tywabi-Ngeva, Z. (2025) · Energy Reports · doi:10.1016/j.egyr.2025.01.019 · Cited by 105
Raina, N., Chuetor, S., Elalami, D., Tayibi, S., & Barakat, A. (2024) · BioEnergy Research · doi:10.1007/s12155-024-10792-x · Cited by 56
Renewable energy role in low-carbon economy and net-zero goal: Perspectives and prospects
Nguyen, V. G., Sirohi, R., Tran, M. H., Truong, T. H., Duong, M. T., Pham, M. T., & Cao, D. N. (2024) · Energy & Environment · doi:10.1177/0958305x241253772 · Cited by 51
Self-citationHybrid renewable multi-generation system optimization: Attaining sustainable development goals
Mohtasim, M. S., Das, B. K., Paul, U. K., Kibria, M. G., & Hossain, M. S. (2025) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2025.115415 · Cited by 46
Metadata
- Authors
- Nguyen, V. N., Tarełko, W., Sharma, P., El-Shafay, A. S., Chen, W.-H., Nguyen, P. Q. P., Nguyen, X. P., & Hoang, A. T.
- Affiliations
- HUTECH University (VN)
- Gdańsk University of Technology (PL)
- Delhi Skill and Entrepreneurship University (IN)
- Prince Sattam Bin Abdulaziz University (SA)
- Tunghai University (TW)
- Ho Chi Minh City University of Transport (VN)
- PATET Research Group, Ho Chi Minh City University of Transport, Ho Chi Minh City 700000, Vietnam
- Dong A University (VN)
- Corresponding author
- Sharma, Prabhakar
- Chen, Wei-Hsin
- Hoang, Anh Tuan
- ORCID
- Venue
- Energy & Fuels
- Publisher
- American Chemical Society (ACS)
- Published
- 2024-01-19
- Volume
- 38
- Issue
- 3
- Pages
- 1692-1712
- ISSN
- 0887-0624 (ISSN-L)
- 1520-5029
- OpenAlex
- W4391035178
- Cited by
- 130
- References
- 190
- Citations by year
- 2026: 44 · 2025: 52 · 2024: 34
- FWCI
- 19.22
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- Energy & Fuels (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
- Physical Sciences › Computer Science › Artificial Intelligence › Artificial Intelligence Applications
- Physical Sciences › Energy › Renewable Energy, Sustainability and the Environment › Renewable Energy and Sustainability
- Keywords
- explainable AI, renewable energy, black-box models, transparency, accountability, fairness, ethical implications, energy system management, energy use efficiency, model interpretability, evaluation metrics
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
