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Unlocking renewable energy potential: Harnessing machine learning and intelligent algorithms

1Institute of Engineering, HUTECH University, Ho Chi Minh City, Viet Nam

2Department of Research and Innovation, Saveetha School of Engineering, SIMATS, Chennai, Tamil Nadu - 602105, India

3Mechanical Engineering Department, Politeknik Negeri Padang, West Sumatera, Indonesia

4 Institute of Maritime, Ho Chi Minh city University of Transport, Ho Chi Minh City, Viet Nam

5 Faculty of Automotive Engineering, Dong A University, Danang, Viet Nam, Viet Nam

6 Institute of Mechanical Engineering, Ho Chi Minh city University of Transport, Ho Chi Minh City, Viet Nam

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Received: 12 Feb 2024; Revised: 27 May 2024; Accepted: 7 Jun 2024; Available online: 25 Jun 2024; Published: 1 Jul 2024.
Editor(s): H Hadiyanto
Open Access Copyright (c) 2024 The Author(s). Published by Centre of Biomass and Renewable Energy (CBIORE)
Creative Commons License This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

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Abstract

This review article examines the revolutionary possibilities of machine learning (ML) and intelligent algorithms for enabling renewable energy, with an emphasis on the energy domains of solar, wind, biofuel, and biomass. Critical problems such as data variability, system inefficiencies, and predictive maintenance are addressed by the integration of ML in renewable energy systems. Machine learning improves solar irradiance prediction accuracy and maximizes photovoltaic system performance in the solar energy sector. ML algorithms help to generate electricity more reliably by enhancing wind speed forecasts and wind turbine efficiency. ML improves the efficiency of biofuel production by optimizing feedstock selection, process parameters, and yield forecasts. Similarly, ML models in biomass energy provide effective thermal conversion procedures and real-time process management, guaranteeing increased energy production and operational stability. Even with the enormous advantages, problems such as data quality, interpretability of the models, computing requirements, and integration with current systems still remain. Resolving these issues calls for interdisciplinary cooperation, developments in computer technology, and encouraging legislative frameworks. This study emphasizes the vital role of ML in promoting sustainable and efficient renewable energy systems by giving a thorough review of present ML applications in renewable energy, highlighting continuing problems, and outlining future prospects

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Keywords: Machine learning; Artificial Intelligence; Renewable energy; Waste-to-energy path; Sustainable energy

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  1. Abramowski, T., 2008. Application of artificial neural networks to assessment of ship manoeuvrability qualities. Polish Marit. Res. 15, 15–21. https://doi.org/10.2478/v10012-007-0059-0
  2. Achouch, M., Dimitrova, M., Dhouib, R., Ibrahim, H., Adda, M., Sattarpanah Karganroudi, S., Ziane, K., Aminzadeh, A., 2023. Predictive Maintenance and Fault Monitoring Enabled by Machine Learning: Experimental Analysis of a TA-48 Multistage Centrifugal Plant Compressor. Appl. Sci. 13, 1790. https://doi.org/10.3390/app13031790
  3. Afridi, Y.S., Ahmad, K., Hassan, L., 2022. Artificial intelligence based prognostic maintenance of renewable energy systems: A review of techniques, challenges, and future research directions. Int. J. Energy Res. 46, 21619–21642
  4. Afzal, N. S., Ağbulut, Ü., Alahmadi, A.A., Gowda, A.C., Alwetaishi, M., Shaik, S., Hoang, A.T., 2023. Poultry fat biodiesel as a fuel substitute in diesel-ethanol blends for DI-CI engine: Experimental, modeling and optimization. Energy 270, 126826. https://doi.org/10.1016/j.energy.2023.126826
  5. Ağbulut, Ü., Sirohi, R., Lichtfouse, E., Chen, W.-H., Len, C., Show, P.L., Le, A.T., Nguyen, X.P., Hoang, A.T., 2023. Microalgae bio-oil production by pyrolysis and hydrothermal liquefaction: Mechanism and characteristics. Bioresour. Technol. 376, 128860. https://doi.org/10.1016/j.biortech.2023.128860
  6. Agrafiotis, C., von Storch, H., Roeb, M., Sattler, C., 2014. Solar thermal reforming of methane feedstocks for hydrogen and syngas production—A review. Renew. Sustain. Energy Rev. 29, 656–682. https://doi.org/10.1016/j.rser.2013.08.050
  7. Agrawal, T., Gautam, R., Agrawal, S., Singh, V., Kumar, M., Kumar, S., 2020. Optimization of engine performance parameters and exhaust emissions in compression ignition engine fueled with biodiesel-alcohol blends using taguchi method, multiple regression and artificial neural network. Sustain. Futur. 2, 100039. https://doi.org/10.1016/j.sftr.2020.100039
  8. Ahmad, I., Sana, A., Kano, M., Cheema, I.I., Menezes, B.C., Shahzad, J., Ullah, Z., Khan, M., Habib, A., 2021. Machine Learning Applications in Biofuels’ Life Cycle: Soil, Feedstock, Production, Consumption, and Emissions. Energies 14, 5072. https://doi.org/10.3390/en14165072
  9. Ahmad, T., Madonski, R., Zhang, D., Huang, C., Mujeeb, A., 2022. Data-driven probabilistic machine learning in sustainable smart energy/smart energy systems: Key developments, challenges, and future research opportunities in the context of smart grid paradigm. Renew. Sustain. Energy Rev. 160, 112128. https://doi.org/10.1016/j.rser.2022.112128
  10. Ahmad, T., Zhang, D., Huang, C., Zhang, H., Dai, N., Song, Y., Chen, H., 2021. Artificial intelligence in sustainable energy industry: Status Quo, challenges and opportunities. J. Clean. Prod. 289. https://doi.org/10.1016/j.jclepro.2021.125834
  11. Ahmed, R., Sreeram, V., Mishra, Y., Arif, M.D., 2020. A review and evaluation of the state-of-the-art in PV solar power forecasting: Techniques and optimization. Renew. Sustain. Energy Rev. 124, 109792. https://doi.org/10.1016/j.rser.2020.109792
  12. Ahmed, S.F., Debnath, J.C., Mehejabin, F., Islam, N., Tripura, R., Mofijur, M., Hoang, A.T., Rasuld, M.G., Dai-Viet N. Vo, 2023. Utilization of nanomaterials in accelerating the production process of sustainable biofuels. Sustain. Energy Technol. Assessments 55, 102894. https://doi.org/10.1016/j.seta.2022.102894
  13. Ahmed, W., Ansari, H., Khan, B., Ullah, Z., Ali, S.M., Mehmood, C.A.A., Qureshi, M.B., Hussain, I., Jawad, M., Khan, M.U.S., Ullah, A., Nawaz, R., 2020. Machine learning based energy management model for smart grid and renewable energy districts. IEEE Access 8, 185059–185078. https://doi.org/10.1109/ACCESS.2020.3029943
  14. Akbar, B., Tayara, H., Chong, K.T., 2024. Unveiling dominant recombination loss in perovskite solar cells with a XGBoost-based machine learning approach. iScience 27, 109200. https://doi.org/10.1016/j.isci.2024.109200
  15. Akilu, S., Baheta, A.T., M.Said, M.A., Minea, A.A., Sharma, K.V., 2018. Properties of glycerol and ethylene glycol mixture based SiO2-CuO/C hybrid nanofluid for enhanced solar energy transport. Sol. Energy Mater. Sol. Cells 179, 118–128. https://doi.org/10.1016/j.solmat.2017.10.027
  16. Aksoy, N., Genc, I., 2023. Predictive models development using gradient boosting based methods for solar power plants. J. Comput. Sci. 67, 101958. https://doi.org/10.1016/j.jocs.2023.101958
  17. Al-Ali, E.M., Hajji, Y., Said, Y., Hleili, M., Alanzi, A.M., Laatar, A.H., Atri, M., 2023. Solar Energy Production Forecasting Based on a Hybrid CNN-LSTM-Transformer Model. Mathematics 11, 676. https://doi.org/10.3390/math11030676
  18. Al-Ruzouq, R., Abdallah, M., Shanableh, A., Alani, S., Obaid, L., Gibril, M.B.A., 2022. Waste to energy spatial suitability analysis using hybrid multi-criteria machine learning approach. Environ. Sci. Pollut. Res. 29, 2613–2628. https://doi.org/10.1007/s11356-021-15289-0
  19. Al Mamun, A., Sohel, M., Mohammad, N., Sunny, M.S.H., Dipta, D.R., Hossain, E., 2020. A comprehensive review of the load forecasting techniques using single and hybrid predictive models. IEEE access 8, 134911–134939
  20. Alassery, F., Alzahrani, A., Khan, A.I., Irshad, K., Islam, S., 2022. An artificial intelligence-based solar radiation prophesy model for green energy utilization in energy management system. Sustain. Energy Technol. Assessments 52, 102060. https://doi.org/10.1016/j.seta.2022.102060
  21. Alawi, O.A., Kamar, H.M., Homod, R.Z., Yaseen, Z.M., 2024. Incorporating artificial intelligence-powered prediction models for exergy efficiency evaluation in parabolic trough collectors. Renew. Energy 225, 120348. https://doi.org/10.1016/j.renene.2024.120348
  22. Ali, I., Seyfeli, R.C., Tahir, M.H., Ceylan, S., 2023. Pyrolytic conversion of waste hemp: Kinetics, product characterization, and boosted regression tree modeling. J. Anal. Appl. Pyrolysis 175, 106165. https://doi.org/10.1016/j.jaap.2023.106165
  23. Aljanad, A., Tan, N.M.L., Agelidis, V.G., Shareef, H., 2021. Neural Network Approach for Global Solar Irradiance Prediction at Extremely Short-Time-Intervals Using Particle Swarm Optimization Algorithm. Energies 14, 1213. https://doi.org/10.3390/en14041213
  24. Allal, Z., Noura, H.N., Chahine, K., 2024. Machine Learning Algorithms for Solar Irradiance Prediction: A Recent Comparative Study. e-Prime - Adv. Electr. Eng. Electron. Energy 7, 100453. https://doi.org/10.1016/j.prime.2024.100453
  25. Alruqi, M., Sharma, P., Algburi, S., Khan, M.A., Alsubih, M., Islam, S., 2024. Biomass energy transformation: Harnessing the power of explainable ai to unlock the potential of ultimate analysis data. Environ. Technol. Innov. 35, 103652. https://doi.org/10.1016/j.eti.2024.103652
  26. Altan, A., Karasu, S., Zio, E., 2021. A new hybrid model for wind speed forecasting combining long short-term memory neural network, decomposition methods and grey wolf optimizer. Appl. Soft Comput. 100, 106996. https://doi.org/10.1016/j.asoc.2020.106996
  27. Alzamora, B.R., Barros, R.T. de V., 2020. Review of municipal waste management charging methods in different countries. Waste Manag. 115, 47–55. https://doi.org/10.1016/j.wasman.2020.07.020
  28. Andrade Cruz, I., Chuenchart, W., Long, F., Surendra, K.C., Renata Santos Andrade, L., Bilal, M., Liu, H., Tavares Figueiredo, R., Khanal, S.K., Fernando Romanholo Ferreira, L., 2022. Application of machine learning in anaerobic digestion: Perspectives and challenges. Bioresour. Technol. 345, 126433. https://doi.org/10.1016/j.biortech.2021.126433
  29. Andrizal, -, Chadry, R., Suryani, A.I., 2018. Embedded System Using Field Programmable Gate Array (FPGA) myRIO and LabVIEW Programming to Obtain Data Patern Emission of Car Engine Combustion Categories. JOIV Int. J. Informatics Vis. 2, 56–62. https://doi.org/10.30630/joiv.2.2.50
  30. Antonopoulos, I., Robu, V., Couraud, B., Kirli, D., Norbu, S., Kiprakis, A., Flynn, D., Elizondo-Gonzalez, S., Wattam, S., 2020. Artificial intelligence and machine learning approaches to energy demand-side response: A systematic review. Renew. Sustain. Energy Rev. 130, 109899. https://doi.org/10.1016/j.rser.2020.109899
  31. Antonopoulos, V.Z., Antonopoulos, A. V., 2024. Reference Evapotranspiration Evaluation Using Solar Radiation Estimated by ANN and Empirical Models. J. Agric. Ecol. Res. Int. 25, 68–87. https://doi.org/10.9734/jaeri/2024/v25i1573
  32. Aslam, Sheraz, Herodotou, H., Mohsin, S.M., Javaid, N., Ashraf, N., Aslam, Shahzad, 2021. A survey on deep learning methods for power load and renewable energy forecasting in smart microgrids. Renew. Sustain. Energy Rev. 144, 110992. https://doi.org/10.1016/j.rser.2021.110992
  33. Astuti, I.P., Yudaputra, A., Rinandio, D.S., Yuswandi, A.Y., 2021. Biogeographical Distribution Model of Flowering Plant Capparis micracantha Using Support Vector Machine (SVM) and Generalized Linear Model (GLM) and its Ex-situ Conservation Efforts. Int. J. Adv. Sci. Eng. Inf. Technol. 11, 2328. https://doi.org/10.18517/ijaseit.11.6.14582
  34. Atabani, A.E., Tyagi, V.K., Fongaro, G., Treichel, H., Pugazhendhi, A., Hoang, A.T., 2022. Integrated biorefineries, circular bio-economy, and valorization of organic waste streams with respect to bio-products. Biomass Convers. Biorefinery 12, 565–565. https://doi.org/10.1007/s13399-021-02017-4
  35. Ayoola, A.A., Hymore, F.K., Omonhinmin, C.A., Olawole, O.C., Fayomi, O.S.I., Babatunde, D., Fagbiele, O., 2019. Analysis of waste groundnut oil biodiesel production using response surface methodology and artificial neural network. Chem. Data Collect. 22, 100238. https://doi.org/10.1016/j.cdc.2019.100238
  36. Bae, D.-J., Kwon, B.-S., Song, K.-B., 2021. XGBoost-Based Day-Ahead Load Forecasting Algorithm Considering Behind-the-Meter Solar PV Generation. Energies 15, 128. https://doi.org/10.3390/en15010128
  37. Baghban, A., Jalali, A., Shafiee, M., Ahmadi, M.H., Chau, K.-W., 2019. Developing an ANFIS-based swarm concept model for estimating the relative viscosity of nanofluids 13, 26–39. https://doi.org/10.1080/19942060.2018.1542345
  38. Baiz, A.A., Ahmadi, H., Shariatmadari, F., Karimi Torshizi, M.A., 2020. A Gaussian process regression model to predict energy contents of corn for poultry. Poult. Sci. 99, 5838–5843. https://doi.org/10.1016/j.psj.2020.07.044
  39. Bakır, H., Ağbulut, Ü., Gürel, A.E., Yıldız, G., Güvenç, U., Soudagar, M.E.M., Hoang, A.T., Deepanraj, B., Saini, G., Afzal, A., 2022. Forecasting of future greenhouse gas emission trajectory for India using energy and economic indexes with various metaheuristic algorithms. J. Clean. Prod. 360, 131946. https://doi.org/10.1016/j.jclepro.2022.131946
  40. Balat, M., Ayar, G., 2005. Biomass Energy in the World, Use of Biomass and Potential Trends. Energy Sources 27, 931–940. https://doi.org/10.1080/00908310490449045
  41. Balsora, H.K., S, K., Dua, V., Joshi, J.B., Kataria, G., Sharma, A., Chakinala, A.G., 2022. Machine learning approach for the prediction of biomass pyrolysis kinetics from preliminary analysis. J. Environ. Chem. Eng. 10, 108025. https://doi.org/10.1016/j.jece.2022.108025
  42. Bandh, S.A., Malla, F.A., Hoang, T.-D., Qayoom, I., Mohi-Ud-Din, H., Bashir, S., Betts, R., Le, T.T., Nguyen Le, D.T., Linh Le, N.V., 2024. Track to reach net-zero: Progress and pitfalls. Energy Environ. https://doi.org/10.1177/0958305X241260793
  43. Bandh, S.A., Malla, F.A., Qayoom, I., Mohi-Ud-Din, H., Butt, A.K., Altaf, A., Wani, S.A., Betts, R., Truong, T.H., Pham, N.D.K., Cao, D.N., Ahmed, S.F., 2023. Importance of Blue Carbon in Mitigating Climate Change and Plastic/Microplastic Pollution and Promoting Circular Economy. Sustainability 15, 2682. https://doi.org/10.3390/su15032682
  44. Banerjee, A., Varshney, D., Kumar, S., Chaudhary, P., Gupta, V.K., 2017. Biodiesel production from castor oil: ANN modeling and kinetic parameter estimation. Int. J. Ind. Chem. 8, 253–262. https://doi.org/10.1007/s40090-017-0122-3
  45. Baruah, D., Baruah, D.C., Hazarika, M.K., 2017. Artificial neural network based modeling of biomass gasification in fixed bed downdraft gasifiers. Biomass and Bioenergy 98, 264–271. https://doi.org/10.1016/J.BIOMBIOE.2017.01.029
  46. Barus, D.H., Dalimi, R., 2021. Multi-Stage Statistical Approach to Wind Power Forecast Errors Evaluation: A Southern Sulawesi Case Study. Int. J. Adv. Sci. Eng. Inf. Technol. 11, 633–641. https://doi.org/10.18517/ijaseit.11.2.12385
  47. Behzadi, A., Sadrizadeh, S., 2023. A rule-based energy management strategy for a low-temperature solar/wind-driven heating system optimized by the machine learning-assisted grey wolf approach. Energy Convers. Manag. 277, 116590. https://doi.org/10.1016/j.enconman.2022.116590
  48. Benti, N.E., Chaka, M.D., Semie, A.G., 2023. Forecasting Renewable Energy Generation with Machine Learning and Deep Learning: Current Advances and Future Prospects. Sustainability 15, 7087. https://doi.org/10.3390/su15097087
  49. Biswas, P.P., Chen, W.-H., Lam, S.S., Park, Y.-K., Chang, J.-S., Hoang, A.T., 2024. A comprehensive study of artificial neural network for sensitivity analysis and hazardous elements sorption predictions via bone char for wastewater treatment. J. Hazard. Mater. Adv. 465, 133154. https://doi.org/10.1016/j.jhazmat.2023.133154
  50. Bonat, W.H., Kokonendji, C.C., 2017. Flexible Tweedie regression models for continuous data. J. Stat. Comput. Simul. 87, 2138–2152. https://doi.org/10.1080/00949655.2017.1318876
  51. Boyle, P., 2007. Gaussian Processes for Regression and Optimisation. Victoria University of Wellington
  52. Bui, V.G., Bui, T.M.T., Hoang, A.T., Nižetić, S., Nguyen Thi, T.X., Vo, A.V., 2021. Hydrogen-Enriched Biogas Premixed Charge Combustion and Emissions in Direct Injection and Indirect Injection Diesel Dual Fueled Engines: A Comparative Study. J. Energy Resour. Technol. 143. https://doi.org/10.1115/1.4051574
  53. Bui, V.G., Tu Bui, T.M., Ong, H.C., Nižetić, S., Bui, V.H., Xuan Nguyen, T.T., Atabani, A.E., Štěpanec, L., Phu Pham, L.H., Hoang, A.T., 2022. Optimizing operation parameters of a spark-ignition engine fueled with biogas-hydrogen blend integrated into biomass-solar hybrid renewable energy system. Energy 252, 124052. https://doi.org/10.1016/j.energy.2022.124052
  54. Burke, M.J., Melgar, R., 2022. SDG 7 requires post-growth energy sufficiency. Front. Sustain. 3. https://doi.org/10.3389/frsus.2022.940958
  55. Buster, G., Siratovich, P., Taverna, N., Rossol, M., Weers, J., Blair, A., Huggins, J., Siega, C., Mannington, W., Urgel, A., Cen, J., Quinao, J., Watt, R., Akerley, J., 2021. A New Modeling Framework for Geothermal Operational Optimization with Machine Learning (GOOML). Energies 14, 6852. https://doi.org/10.3390/en14206852
  56. Cao, D.N., Johnson, A.J.T., 2024. A Simulation Study on a Premixed-charge Compression Ignition Mode-based Engine Using a Blend of Biodiesel/Diesel Fuel under a Split Injection Strategy. Int. J. Adv. Sci. Eng. Inf. Technol. 14, 451–471. https://doi.org/10.18517/ijaseit.14.2.20007
  57. Capote-Leiva, J., Villota-Rivillas, M., Muñoz-OrdÃ3ñez, J., 2022. Access Control System based on Voice and Facial Recognition Using Artificial Intelligence. Int. J. Adv. Sci. Eng. Inf. Technol. 12, 2342–2348. https://doi.org/10.18517/ijaseit.12.6.16049
  58. Carvalho, D., Rocha, A., Gómez-Gesteira, M., Silva Santos, C., 2017. Offshore winds and wind energy production estimates derived from ASCAT, OSCAT, numerical weather prediction models and buoys – A comparative study for the Iberian Peninsula Atlantic coast. Renew. Energy 102, 433–444. https://doi.org/10.1016/j.renene.2016.10.063
  59. Cepowski, T., Chorab, P., Łozowicka, D., 2021. Application of an Artificial Neural Network and Multiple Nonlinear Regression to Estimate Container Ship Length Between Perpendiculars. Polish Marit. Res. 28, 36–45. https://doi.org/doi: 10.2478/pomr-2021-0019
  60. Çerçi, K.N., Saydam, D.B., Hürdoğan, E., Ozalp, C., 2024. Experimental investigation and artificial neural networks (ANNs) based prediction of thermal performance of solar air heaters with different surface geometry. Sol. Energy 273, 112499. https://doi.org/10.1016/j.solener.2024.112499
  61. Cerquitelli, T., Malnati, G., Apiletti, D., 2019. Exploiting Scalable Machine-Learning Distributed Frameworks to Forecast Power Consumption of Buildings. Energies 12, 2933. https://doi.org/10.3390/en12152933
  62. Cervantes, J., Garcia-Lamont, F., Rodríguez-Mazahua, L., Lopez, A., 2020. A comprehensive survey on support vector machine classification: Applications, challenges and trends. Neurocomputing 408, 189–215. https://doi.org/10.1016/j.neucom.2019.10.118
  63. Chaivivatrakul, S., Moonrinta, J., Chaiwiwatrakul, S., 2022. Convolutional Neural Networks for Herb Identification: Plain Background and Natural Environment. Int. J. Adv. Sci. Eng. Inf. Technol. 12, 1244–1252. https://doi.org/10.18517/ijaseit.12.3.15348
  64. Chammas, S., Wang, Q., Schneider, T., Ihme, M., Chen, Y., Anderson, J., 2023. Accelerating Large‐Eddy Simulations of Clouds With Tensor Processing Units. J. Adv. Model. Earth Syst. 15. https://doi.org/10.1029/2023MS003619
  65. Changxiong, L., Hu, Y., Yang, Z., Guo, H., 2023a. Experimental Study of Fuel Combustion and Emission Characteristics of Marine Diesel Engines Using Advanced Fuels. Polish Marit. Res. 30, 48–58. https://doi.org/10.2478/pomr-2023-0038
  66. Changxiong, L., Hu, Y., Yang, Z., Guo, H., 2023b. Experimental Study of Fuel Combustion and Emission Characteristics of Marine Diesel Engines Using Advanced Fuels. Polish Marit. Res. 30, 48–58. https://doi.org/10.2478/pomr-2023-0038
  67. Chaurasiya, P.K., Warudkar, V., Ahmed, S., 2019. Wind energy development and policy in India: A review. Energy Strateg. Rev. 24, 342–357. https://doi.org/10.1016/j.esr.2019.04.010
  68. Chen, T., Guestrin, C., 2016. XGBoost, in: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, New York, NY, USA, pp. 785–794. https://doi.org/10.1145/2939672.2939785
  69. Chen, W.-H., Aniza, R., Arpia, A.A., Lo, H.-J., Hoang, A.T., Goodarzi, V., Gao, J., 2022a. A comparative analysis of biomass torrefaction severity index prediction from machine learning. Appl. Energy 324, 119689. https://doi.org/10.1016/j.apenergy.2022.119689
  70. Chen, W.-H., Biswas, P.P., Ong, H.C., Hoang, A.T., Nguyen, T.-B., Dong, C.-D., 2023. A critical and systematic review of sustainable hydrogen production from ethanol/bioethanol: Steam reforming, partial oxidation, and autothermal reforming. Fuel 333, 126526. https://doi.org/10.1016/j.fuel.2022.126526
  71. Chen, W.-H., Ho, K.-Y., Aniza, R., Sharma, A.K., Saravanakumar, A., Hoang, A.T., 2024a. A review of noncatalytic and catalytic pyrolysis and co-pyrolysis products from lignocellulosic and algal biomass using Py-GC/MS. J. Ind. Eng. Chem. 134, 51–64. https://doi.org/10.1016/j.jiec.2024.01.020
  72. Chen, W.-H., Huang, T.-H., Luo, D., Jin, L., Maduabuchi, C., Lamba, R., Hoang, A.T., 2024b. Optimization of a unileg thermoelectric generator by the combination of Taguchi method and evolutionary neural network for green power generation. Appl. Therm. Eng. 248, 123204. https://doi.org/10.1016/j.applthermaleng.2024.123204
  73. Chen, W.-H., Nižetić, S., Sirohi, R., Huang, Z., Luque, R., M.Papadopoulos, A., Sakthivel, R., Phuong Nguyen, X., Tuan Hoang, A., 2022b. Liquid hot water as sustainable biomass pretreatment technique for bioenergy production: A review. Bioresour. Technol. 344, 126207. https://doi.org/10.1016/j.biortech.2021.126207
  74. Chen, W.-H., Wang, J.-S., Chang, M.-H., Mutuku, J.K., Hoang, A.T., 2021. Efficiency improvement of a vertical-axis wind turbine using a deflector optimized by Taguchi approach with modified additive method. Energy Convers. Manag. 245, 114609. https://doi.org/10.1016/j.enconman.2021.114609
  75. Chen, W.-H., Wang, J.-S., Chang, M.-H., Tuan Hoang, A., Shiung Lam, S., Kwon, E.E., Ashokkumar, V., 2022c. Optimization of a vertical axis wind turbine with a deflector under unsteady wind conditions via Taguchi and neural network applications. Energy Convers. Manag. 254, 115209. https://doi.org/10.1016/j.enconman.2022.115209
  76. Chen, W.-H., Wang, Y.-S., Chang, M.-H., Show, P.L., Hoang, A.T., 2024c. Operation parameter interaction and optimization of vertical axis wind turbine analyzed by Taguchi method with modified additive model and ANOVA. Energy Reports 11, 5189–5200
  77. Chen, Y., Luo, A., Cheng, M., Wu, Y., Zhu, J., Meng, Y., Tan, W., 2023. Classification and recycling of recyclable garbage based on deep learning. J. Clean. Prod. 414, 137558. https://doi.org/10.1016/j.jclepro.2023.137558
  78. Cheng, M.-Y., Prayogo, D., Ju, Y.-H., Wu, Y.-W., Sutanto, S., 2016. Optimizing mixture properties of biodiesel production using genetic algorithm-based evolutionary support vector machine. Int. J. Green Energy 13, 1599–1607. https://doi.org/10.1080/15435075.2016.1206549
  79. Chitsazan, M.A., Sami Fadali, M., Trzynadlowski, A.M., 2019. Wind speed and wind direction forecasting using echo state network with nonlinear functions. Renew. Energy 131, 879–889. https://doi.org/10.1016/j.renene.2018.07.060
  80. Chiu, M.-C., Wen, C.-Y., Hsu, H.-W., Wang, W.-C., 2022. Key wastes selection and prediction improvement for biogas production through hybrid machine learning methods. Sustain. Energy Technol. Assessments 52, 102223. https://doi.org/10.1016/j.seta.2022.102223
  81. Cong My, T., Dang Khanh, L., Minh Thao, P., 2023. An Artificial Neural Networks (ANN) Approach for 3 Degrees of Freedom Motion Controlling. JOIV Int. J. Informatics Vis. 7, 301. https://doi.org/10.30630/joiv.7.2.1817
  82. Cortes, C., Vapnik, V., 1995. Support-vector networks. Mach. Learn. 20, 273–297. https://doi.org/10.1007/BF00994018
  83. Cortiñas-Lorenzo, K., Lacey, G., 2024. Toward Explainable Affective Computing: A Review. IEEE Trans. Neural Networks Learn. Syst. 1–0. https://doi.org/10.1109/TNNLS.2023.3270027
  84. Dahlgren, S., 2022. Biogas-based fuels as renewable energy in the transport sector: an overview of the potential of using CBG, LBG and other vehicle fuels produced from biogas. Biofuels 13, 587–599. https://doi.org/10.1080/17597269.2020.1821571
  85. Dal Pozzo, A., Lucquiaud, M., De Greef, J., 2023. Research and Innovation Needs for the Waste-To-Energy Sector towards a Net-Zero Circular Economy. Energies 16, 1909. https://doi.org/10.3390/en16041909
  86. De Caro, F., De Stefani, J., Bontempi, G., Vaccaro, A., Villacci, D., 2020. Robust assessment of short-term wind power forecasting models on multiple time horizons. Technol. Econ. Smart Grids Sustain. Energy 5, 1–15
  87. De Clercq, D., Jalota, D., Shang, R., Ni, K., Zhang, Z., Khan, A., Wen, Z., Caicedo, L., Yuan, K., 2019. Machine learning powered software for accurate prediction of biogas production: A case study on industrial-scale Chinese production data. J. Clean. Prod. 218, 390–399. https://doi.org/10.1016/j.jclepro.2019.01.031
  88. De Poures, M.V., Dillikannan, D., Kaliyaperumal, G., Thanikodi, S., Ağbulut, Ü., Hoang, A.T., Mahmoud, Z., Shaik, S., Saleel, C.A., Afzal, A., 2023. Collective influence and optimization of 1-hexanol, fuel injection timing, and EGR to control toxic emissions from a light-duty agricultural diesel engine fueled with diesel/waste cooking oil methyl ester blends. Process Saf. Environ. Prot. 172, 738–752. https://doi.org/10.1016/j.psep.2023.02.054
  89. Deka, T.J., Osman, A.I., Baruah, D.C., Rooney, D.W., 2022. Methanol fuel production, utilization, and techno-economy: a review. Environ. Chem. Lett. 20, 3525–3554. https://doi.org/10.1007/s10311-022-01485-y
  90. Demirbas, A., 2009. Global Renewable Energy Projections. Energy Sources, Part B Econ. Planning, Policy 4, 212–224. https://doi.org/10.1080/15567240701620499
  91. Demirbas, A., 2008. Biofuels sources, biofuel policy, biofuel economy and global biofuel projections. Energy Convers. Manag. 49, 2106–2116. https://doi.org/10.1016/j.enconman.2008.02.020
  92. Demolli, H., Dokuz, A.S., Ecemis, A., Gokcek, M., 2019. Wind power forecasting based on daily wind speed data using machine learning algorithms. Energy Convers. Manag. 198, 111823. https://doi.org/10.1016/j.enconman.2019.111823
  93. Deng, X., Shao, H., Hu, C., Jiang, D., Jiang, Y., 2020. Wind power forecasting methods based on deep learning: A survey. Comput. Model. Eng. Sci. 122, 273–302
  94. Deng, Y., Wang, B., Lu, Z., 2020. A hybrid model based on data preprocessing strategy and error correction system for wind speed forecasting. Energy Convers. Manag. 212, 112779. https://doi.org/10.1016/j.enconman.2020.112779
  95. Dhyani, V., Bhaskar, T., 2018. A comprehensive review on the pyrolysis of lignocellulosic biomass. Renew. Energy. https://doi.org/10.1016/j.renene.2017.04.035
  96. Ding, S., Zhang, H., Tao, Z., Li, R., 2022. Integrating data decomposition and machine learning methods: An empirical proposition and analysis for renewable energy generation forecasting. Expert Syst. Appl. 204, 117635
  97. Doan, Q.B., Nguyen, X.P., Pham, V.V., Dong, T.M.H., Pham, M.T., Le, T.S., 2022. Performance and Emission Characteristics of Diesel Engine Using Ether Additives: A Review. Int. J. Renew. Energy Dev. Vol 11, No 1 Febr. 2022DO - https://doi.org/10.14710/ijred.2022.42522
  98. Dodo, U.A., Ashigwuike, E.C., Abba, S.I., 2022a. Machine learning models for biomass energy content prediction: A correlation-based optimal feature selection approach. Bioresour. Technol. Reports 19, 101167. https://doi.org/10.1016/j.biteb.2022.101167
  99. Dodo, U.A., Ashigwuike, E.C., Emechebe, J.N., Abba, S.I., 2022b. Prediction of energy content of biomass based on hybrid machine learning ensemble algorithm. Energy Nexus 8, 100157. https://doi.org/10.1016/j.nexus.2022.100157
  100. Dong, J., Chen, Y., Yao, B., Zhang, X., Zeng, N., 2022. A neural network boosting regression model based on XGBoost. Appl. Soft Comput. 125, 109067. https://doi.org/10.1016/j.asoc.2022.109067
  101. Dong, J., Chi, Y., Zou, D., Fu, C., Huang, Q., Ni, M., 2014. Comparison of municipal solid waste treatment technologies from a life cycle perspective in China. Waste Manag. Res. J. a Sustain. Circ. Econ. 32, 13–23. https://doi.org/10.1177/0734242X13507311
  102. dos Santos Junior, J.M., Zelioli, Í.A.M., Mariano, A.P., 2023. Hybrid Modeling of Machine Learning and Phenomenological Model for Predicting the Biomass Gasification Process in Supercritical Water for Hydrogen Production. Eng 4, 1495–1515. https://doi.org/10.3390/eng4020086
  103. Dosdoğru, A.T., Boru İpek, A., 2022. Hybrid boosting algorithms and artificial neural network for wind speed prediction. Int. J. Hydrogen Energy 47, 1449–1460. https://doi.org/10.1016/j.ijhydene.2021.10.154
  104. Duc Bui, V., Phuong Vu, H., Phuong Nguyen, H., Quang Duong, X., Tuyen Nguyen, D., Tuan Pham, M., Quy Phong Nguyen, P., 2023. Techno-economic assessment and logistics management of biomass in the conversion progress to bioenergy. Sustain. Energy Technol. Assessments 55, 102991. https://doi.org/10.1016/j.seta.2022.102991
  105. Elizabeth Michael, N., Mishra, M., Hasan, S., Al-Durra, A., 2022. Short-Term Solar Power Predicting Model Based on Multi-Step CNN Stacked LSTM Technique. Energies 15, 2150. https://doi.org/10.3390/en15062150
  106. Elmaz, F., Yücel, Ö., Mutlu, A.Y., 2020. Predictive modeling of biomass gasification with machine learning-based regression methods. Energy 191, 116541. https://doi.org/10.1016/J.ENERGY.2019.116541
  107. Emenike, E.C., Iwuozor, K.O., Ighalo, J.O., Bamigbola, J.O., Omonayin, E.O., Ojo, H.T., Adeleke, J., Adeniyi, A.G., 2024. Advancing the circular economy through the thermochemical conversion of waste to biochar: a review on sawdust waste-derived fuel. Biofuels 15, 433–447. https://doi.org/10.1080/17597269.2023.2255007
  108. Ezzahra Yatim, F., Boumanchar, I., Srhir, B., Chhiti, Y., Jama, C., Ezzahrae M’hamdi Alaoui, F., 2022. Waste-to-energy as a tool of circular economy: Prediction of higher heating value of biomass by artificial neural network (ANN) and multivariate linear regression (MLR). Waste Manag. 153, 293–303. https://doi.org/10.1016/j.wasman.2022.09.013
  109. Faisal Ahmed, S., Khalid, M., Vaka, M., Walvekar, R., Numan, A., Khaliq Rasheed, A., Mujawar Mubarak, N., 2021. Recent progress in solar water heaters and solar collectors: A comprehensive review. Therm. Sci. Eng. Prog. 100981. https://doi.org/https://doi.org/10.1016/j.tsep.2021.100981
  110. Fan, X., Li, Y., 2023. Energy management of renewable based power grids using artificial intelligence: Digital twin of renewables. Sol. Energy 262, 111867. https://doi.org/10.1016/j.solener.2023.111867
  111. Fayad, M.A., Al-Ghezi, M.K., Hafad, S.A., Ibrahim, S.I., Abood, M.K., Al-Salihi, H.A., Mahdi, L.A., Chaichan, M.T., Dhahad, H.A., 2022. Emissions Characteristics and Engine Performance from the Interaction Effect of EGR and Diesel-Ethanol Blends in Diesel Engine. Int. J. Renew. Energy Dev. 11, 991–1001. https://doi.org/10.14710/ijred.2022.45051
  112. Feng, Y., Wu, Q., 2022. A statistical learning assessment of Huber regression. J. Approx. Theory 273, 105660. https://doi.org/10.1016/j.jat.2021.105660
  113. Ferkous, K., Chellali, F., Kouzou, A., Bekkar, B., 2021. Wavelet-Gaussian process regression model for forecasting daily solar radiation in the Saharan climate. Clean Energy 5, 316–328. https://doi.org/10.1093/ce/zkab012
  114. Fernandes, D.V., Silva, C.S., 2022. Open Energy Data — A regulatory framework proposal under the Portuguese electric system context. Energy Policy 170, 113240. https://doi.org/10.1016/j.enpol.2022.113240
  115. Ferraz de Campos, V.A., Silva, V.B., Cardoso, J.S., Brito, P.S., Tuna, C.E., Silveira, J.L., 2021. A review of waste management in Brazil and Portugal: Waste-to-energy as pathway for sustainable development. Renew. Energy 178, 802–820. https://doi.org/10.1016/j.renene.2021.06.107
  116. Ferrero Bermejo, J., Gómez Fernández, J.F., Olivencia Polo, F., Crespo Márquez, A., 2019. A Review of the Use of Artificial Neural Network Models for Energy and Reliability Prediction. A Study of the Solar PV, Hydraulic and Wind Energy Sources. Appl. Sci. 9, 1844. https://doi.org/10.3390/app9091844
  117. Fiore, M., Magi, V., Viggiano, A., 2020. Internal combustion engines powered by syngas: A review. Appl. Energy 276, 115415. https://doi.org/10.1016/j.apenergy.2020.115415
  118. Franco, A., Salza, P., 2011. Strategies for optimal penetration of intermittent renewables in complex energy systems based on techno-operational objectives. Renew. Energy 36, 743–753. https://doi.org/10.1016/J.RENENE.2010.07.022
  119. Franzese, N., Dincer, I., Sorrentino, M., 2020. A new multigenerational solar-energy based system for electricity, heat and hydrogen production. Appl. Therm. Eng. 171, 115085
  120. Gabbar, A., Ahmad, H., 2024. Integrated Waste-to-Energy Process Optimization for Municipal Solid Waste. Energies 17, 497. https://doi.org/10.3390/en17020497
  121. Gandhi, A.M., Shanmugan, S., Kumar, R., Elsheikh, A.H., Sharifpur, M., Bewoor, A.K., Bamisile, O., Hoang, A.T., Ongar, B., 2022. SiO2/TiO2 nanolayer synergistically trigger thermal absorption inflammatory responses materials for performance improvement of stepped basin solar still natural distiller. Sustain. Energy Technol. Assessments 52, 101974. https://doi.org/10.1016/j.seta.2022.101974
  122. Gangwar, S., Bali, V., Kumar, A., 2018. Comparative Analysis of Wind Speed Forecasting Using LSTM and SVM. ICST Trans. Scalable Inf. Syst. 159407. https://doi.org/10.4108/eai.13-7-2018.159407
  123. García-Nieto, P.J., García-Gonzalo, E., Paredes-Sánchez, B.M., Paredes-Sánchez, J.P., 2023. Modelling hydrogen production from biomass pyrolysis for energy systems using machine learning techniques. Environ. Sci. Pollut. Res. 30, 76977–76991. https://doi.org/10.1007/s11356-023-27805-5
  124. García Nieto, P.J., García–Gonzalo, E., Sánchez Lasheras, F., Paredes–Sánchez, J.P., Riesgo Fernández, P., 2019. Forecast of the higher heating value in biomass torrefaction by means of machine learning techniques. J. Comput. Appl. Math. 357, 284–301. https://doi.org/10.1016/j.cam.2019.03.009
  125. Gautam, M., Agrawal, M., 2021. Greenhouse Gas Emissions from Municipal Solid Waste Management: A Review of Global Scenario. pp. 123–160. https://doi.org/10.1007/978-981-15-9577-6_5
  126. Ge, H., Zheng, J., Xu, H., 2023. Advances in machine learning for high value-added applications of lignocellulosic biomass. Bioresour. Technol. 369, 128481. https://doi.org/10.1016/j.biortech.2022.128481
  127. Ge, S., Shi, Y., Xia, C., Huang, Z., Manzo, M., Cai, L., Ma, H., Zhang, S., Jiang, J., Sonne, C., Lam, S.S., 2021. Progress in pyrolysis conversion of waste into value-added liquid pyro-oil, with focus on heating source and machine learning analysis. Energy Convers. Manag. 245, 114638. https://doi.org/10.1016/j.enconman.2021.114638
  128. Gebremariam, S.N., 2023. Biodiesel as a transport fuel, advantages and disadvantages. Biofuels, Bioprod. Biorefining 17, 1445–1456
  129. Geetha, A., Santhakumar, J., Sundaram, K.M., Usha, S., Thentral, T.M.T., Boopathi, C.S., Ramya, R., Sathyamurthy, R., 2022. Prediction of hourly solar radiation in Tamil Nadu using ANN model with different learning algorithms. Energy Reports 8, 664–671. https://doi.org/10.1016/j.egyr.2021.11.190
  130. George, J., Arun, P., Muraleedharan, C., 2018. Assessment of producer gas composition in air gasification of biomass using artificial neural network model. Int. J. Hydrogen Energy 43, 9558–9568. https://doi.org/10.1016/j.ijhydene.2018.04.007
  131. Ghodbane, M., Benmenine, D., Khechekhouche, A., Boumeddane, B., 2020. Brief on Solar Concentrators: Differences and Applications. Instrum. Mes. Métrologie 19, 371–378. https://doi.org/10.18280/i2m.190507
  132. Ghosh, A., Kumar, S., Das, J., 2023. Impact of leachate and landfill gas on the ecosystem and health: Research trends and the way forward towards sustainability. J. Environ. Manage. 336, 117708. https://doi.org/10.1016/j.jenvman.2023.117708
  133. Gibbs, M.N., 1997. Bayesian Gaussian Processes for Regression and Classi cation. University of Cambridge
  134. Gil, A., 2022. Challenges on waste-to-energy for the valorization of industrial wastes: Electricity, heat and cold, bioliquids and biofuels. Environ. Nanotechnology, Monit. Manag. 17, 100615. https://doi.org/10.1016/j.enmm.2021.100615
  135. Giwa, S.O., Adekomaya, S.O., Adama, K.O., Mukaila, M.O., 2015. Prediction of selected biodiesel fuel properties using artificial neural network. Front. Energy 9, 433–445. https://doi.org/10.1007/s11708-015-0383-5
  136. Gonçalves, Puna, Guerra, Rodrigues, Gomes, Santos, Alves, 2019. Towards the Development of Syngas/Biomethane Electrolytic Production, Using Liquefied Biomass and Heterogeneous Catalyst. Energies 12, 3787. https://doi.org/10.3390/en12193787
  137. Gopi, A., Sharma, P., Sudhakar, K., Ngui, W.K., Kirpichnikova, I., Cuce, E., 2022. Weather Impact on Solar Farm Performance: A Comparative Analysis of Machine Learning Techniques. Sustainability 15, 439. https://doi.org/10.3390/su15010439
  138. Gopirajan, P.V., Gopinath, K.P., Sivaranjani, G., Arun, J., 2021. Optimization of hydrothermal gasification process through machine learning approach: Experimental conditions, product yield and pollution. J. Clean. Prod. 306, 127302. https://doi.org/10.1016/j.jclepro.2021.127302
  139. Güleç, F., Parthiban, A., Umenweke, G.C., Musa, U., Williams, O., Mortezaei, Y., Suk‐Oh, H., Lester, E., Ogbaga, C.C., Gunes, B., Okolie, J.A., 2023. Progress in lignocellulosic biomass valorization for biofuels and value‐added chemical production in the EU: A focus on thermochemical conversion processes. Biofuels, Bioprod. Biorefining. https://doi.org/10.1002/bbb.2544
  140. Guo, J., Baghban, A., 2017. Application of ANFIS strategy for prediction of biodiesel production using supercritical methanol. Energy Sources, Part A Recover. Util. Environ. Eff. 39, 1862–1868. https://doi.org/10.1080/15567036.2017.1380731
  141. Haghshenas, A., Hasan, A., Osen, O., Mikalsen, E.T., 2023. Predictive digital twin for offshore wind farms. Energy Informatics 6, 1. https://doi.org/10.1186/s42162-023-00257-4
  142. Haksoro, T., Aisjah, A.S., Sreerakuvandana, Rahaman, M., Biyanto, T.R., 2023. Enhancing Techno Economic Efficiency of FTC Distillation Using Cloud-Based Stochastic Algorithm. Int. J. Cloud Appl. Comput. 13, 1–16. https://doi.org/10.4018/IJCAC.332408
  143. Hameed, W.I., Sawadi, B.A., Al-Kamil, S.J., Al-Radhi, M.S., Al-Yasir, Y.I.A., Saleh, A.L., Abd-Alhameed, R.A., 2019. Prediction of Solar Irradiance Based on Artificial Neural Networks. Inventions 4, 45. https://doi.org/10.3390/inventions4030045
  144. Han, T., Paramasivam, P., Dong, V.H., Cuong, H., Chuan, D., 2024. Harnessing a Better Future : Exploring AI and ML Applications in Renewable Energy. JOIV Int. J. Informatics Vis. 8
  145. Haque, R., Quek, A., Ting, C., Goh, H., Hasan, R., 2024. Classification Techniques Using Machine Learning for Graduate Student Employability Predictions. Int. J. Adv. Sci. Eng. Inf. Technol. 14, 45–56
  146. Hartanto, A.D., Nur Kholik, Y., Pristyanto, Y., 2023. Stock Price Time Series Data Forecasting Using the Light Gradient Boosting Machine (LightGBM) Model. JOIV Int. J. Informatics Vis. 7, 2270. https://doi.org/10.30630/joiv.7.4.01740
  147. Hasanzadeh, R., Mojaver, P., Azdast, T., Khalilarya, S., Chitsaz, A., 2023. Developing gasification process of polyethylene waste by utilization of response surface methodology as a machine learning technique and multi-objective optimizer approach. Int. J. Hydrogen Energy 48, 5873–5886. https://doi.org/10.1016/j.ijhydene.2022.11.067
  148. Hassija, V., Chamola, V., Mahapatra, A., Singal, A., Goel, D., Huang, K., Scardapane, S., Spinelli, I., Mahmud, M., Hussain, A., 2024. Interpreting Black-Box Models: A Review on Explainable Artificial Intelligence. Cognit. Comput. 16, 45–74. https://doi.org/10.1007/s12559-023-10179-8
  149. Hayajneh, A.M., Alasali, F., Salama, A., Holderbaum, W., 2024. Intelligent Solar Forecasts: Modern Machine Learning Models and TinyML Role for Improved Solar Energy Yield Predictions. IEEE Access 12, 10846–10864. https://doi.org/10.1109/ACCESS.2024.3354703
  150. Heidari, A., Khovalyg, D., 2020. Short-term energy use prediction of solar-assisted water heating system: Application case of combined attention-based LSTM and time-series decomposition. Sol. Energy 207, 626–639. https://doi.org/10.1016/j.solener.2020.07.008
  151. Hoang, A.T., 2021. Prediction of the density and viscosity of biodiesel and the influence of biodiesel properties on a diesel engine fuel supply system. J. Mar. Eng. Technol. 20, 299–311. https://doi.org/10.1080/20464177.2018.1532734
  152. Hoang, A.T., Goldfarb, J.L., Foley, A.M., Lichtfouse, E., Kumar, M., Xiao, L., Ahmed, S.F., Said, Z., Luque, R., Bui, V.G., Nguyen, X.P., 2022a. Production of biochar from crop residues and its application for anaerobic digestion. Bioresour. Technol. 363, 127970. https://doi.org/10.1016/j.biortech.2022.127970
  153. Hoang, A.T., Huang, Z., Nižetić, S., Pandey, A., Nguyen, X.P., Luque, R., Ong, H.C., Said, Z., Le, T.H., Pham, V.V., 2022b. Characteristics of hydrogen production from steam gasification of plant-originated lignocellulosic biomass and its prospects in Vietnam. Int. J. Hydrogen Energy 47, 4394–4425. https://doi.org/10.1016/j.ijhydene.2021.11.091
  154. Hoang, A.T., Kumar, S., Lichtfouse, E., Cheng, C.K., Varma, R.S., Senthilkumar, N., Phong Nguyen, P.Q., Nguyen, X.P., 2022c. Remediation of heavy metal polluted waters using activated carbon from lignocellulosic biomass: An update of recent trends. Chemosphere 302, 134825. https://doi.org/10.1016/j.chemosphere.2022.134825
  155. Hoang, A.T., Murugesan, P., PV, E., Balasubramanian, D., Parida, S., Priya Jayabal, C., Nachippan, M., Kalam, M.., Truong, T.H., Cao, D.N., Le, V.V., 2023a. Strategic combination of waste plastic/tire pyrolysis oil with biodiesel for natural gas-enriched HCCI engine: Experimental analysis and machine learning model. Energy 280, 128233. https://doi.org/10.1016/j.energy.2023.128233
  156. Hoang, A.T., Nguyen, X.P., Duong, X.Q., Ağbulut, Ü., Len, C., Nguyen, P.Q.P., Kchaou, M., Chen, W.-H., 2023b. Steam explosion as sustainable biomass pretreatment technique for biofuel production: Characteristics and challenges. Bioresour. Technol. 385, 129398. https://doi.org/10.1016/j.biortech.2023.129398
  157. Hoang, A.T., Nguyen, X.P., Le, A.T., Huynh, T.T., Pham, V.V., 2021a. COVID-19 and the Global Shift Progress to Clean Energy. J. Energy Resour. Technol. 143, 094701. https://doi.org/10.1115/1.4050779
  158. Hoang, A.T., Nižetić, S., Ng, K.H., Papadopoulos, A.M., Le, A.T., Kumar, S., Hadiyanto, H., Pham, V.V., 2022d. Microbial fuel cells for bioelectricity production from waste as sustainable prospect of future energy sector. Chemosphere 287, 132285. https://doi.org/10.1016/j.chemosphere.2021.132285
  159. Hoang, A.T., Nižetić, S., Ong, H.C., Mofijur, M., Ahmed, S.F., Ashok, B., Bui, V.T.V., Chau, M.Q., 2021b. Insight into the recent advances of microwave pretreatment technologies for the conversion of lignocellulosic biomass into sustainable biofuel. Chemosphere 281, 130878. https://doi.org/10.1016/j.chemosphere.2021.130878
  160. Hoang, A.T., Ölçer, A.I., Nižetić, S., 2021c. Prospective review on the application of biofuel 2,5-dimethylfuran to diesel engine. J. Energy Inst. 94, 360–386. https://doi.org/10.1016/j.joei.2020.10.004
  161. Hoang, A.T., Ong, H.C., Fattah, I.M.R., Chong, C.T., Cheng, C.K., Sakthivel, R., Ok, Y.S., 2021d. Progress on the lignocellulosic biomass pyrolysis for biofuel production toward environmental sustainability. Fuel Process. Technol. 223, 106997. https://doi.org/10.1016/j.fuproc.2021.106997
  162. Hoang, A.T., Pandey, A., Huang, Z., Luque, R., Ng, K.H., Papadopoulos, A.M., Chen, W.-H., Rajamohan, S., Hadiyanto, H., Nguyen, X.P., Pham, V.V., 2022e. Catalyst-Based Synthesis of 2,5-Dimethylfuran from Carbohydrates as a Sustainable Biofuel Production Route. ACS Sustain. Chem. Eng. 10, 3079–3115. https://doi.org/10.1021/acssuschemeng.1c06363
  163. Hoang, A.T., Pandey, A., Lichtfouse, E., Bui, V.G., Veza, I., Nguyen, H.L., Nguyen, X.P., 2023c. Green hydrogen economy: Prospects and policies in Vietnam. Int. J. Hydrogen Energy 48, 31049–31062. https://doi.org/10.1016/j.ijhydene.2023.05.306
  164. Hoang, A.T., Pham, V.V., 2021. 2-Methylfuran (MF) as a potential biofuel: A thorough review on the production pathway from biomass, combustion progress, and application in engines. Renew. Sustain. Energy Rev. 148, 111265. https://doi.org/10.1016/j.rser.2021.111265
  165. Hoang, A.T., Sirohi, R., Pandey, A., Nižetić, S., Lam, S.S., Chen, W.-H., Luque, R., Thomas, S., Arıcı, M., Pham, V.V., 2023d. Biofuel production from microalgae: challenges and chances. Phytochem. Rev. 22, 1089–1126. https://doi.org/10.1007/s11101-022-09819-y
  166. Hoang, A.T., Tabatabaei, M., Aghbashlo, M., Carlucci, A.P., Ölçer, A.I., Le, A.T., Ghassemi, A., 2021e. Rice bran oil-based biodiesel as a promising renewable fuel alternative to petrodiesel: A review. Renew. Sustain. Energy Rev. 135, 110204. https://doi.org/10.1016/J.RSER.2020.110204
  167. Hoang, A.T., Tran, V.D., Dong, V.H., Le, A.T., 2022f. An experimental analysis on physical properties and spray characteristics of an ultrasound-assisted emulsion of ultra-low-sulphur diesel and Jatropha-based biodiesel. J. Mar. Eng. Technol. 21, 73–81. https://doi.org/10.1080/20464177.2019.1595355
  168. Hoang, A.T., Varbanov, P.S., Nižetić, S., Sirohi, R., Pandey, A., Luque, R., Ng, K.H., Pham, V.V., 2022f. Perspective review on Municipal Solid Waste-to-energy route: Characteristics, management strategy, and role in circular economy. J. Clean. Prod. 359, 131897. https://doi.org/10.1016/j.jclepro.2022.131897
  169. Hoofnagle, C.J., van der Sloot, B., Borgesius, F.Z., 2019. The European Union general data protection regulation: what it is and what it means. Inf. Commun. Technol. Law 28, 65–98. https://doi.org/10.1080/13600834.2019.1573501
  170. Hossen, M.M., Ashraf, A., Hasan, M., Majid, M.E., Nashbat, M., Kashem, S.B.A., Kunju, A.K.A., Khandakar, A., Mahmud, S., Chowdhury, M.E.H., 2024. GCDN-Net: Garbage classifier deep neural network for recyclable urban waste management. Waste Manag. 174, 439–450. https://doi.org/10.1016/j.wasman.2023.12.014
  171. Hou, X., Ju, C., Wang, B., 2023. Prediction of solar irradiance using convolutional neural network and attention mechanism-based long short-term memory network based on similar day analysis and an attention mechanism. Heliyon 9, e21484. https://doi.org/10.1016/j.heliyon.2023.e21484
  172. Hu, G., You, F., 2022. Renewable energy-powered semi-closed greenhouse for sustainable crop production using model predictive control and machine learning for energy management. Renew. Sustain. Energy Rev. 168, 112790. https://doi.org/10.1016/j.rser.2022.112790
  173. Huang, C., Cao, L., Peng, N., Li, S., Zhang, J., Wang, L., Luo, X., Wang, J.-H., 2018. Day-Ahead Forecasting of Hourly Photovoltaic Power Based on Robust Multilayer Perception. Sustainability 10, 4863. https://doi.org/10.3390/su10124863
  174. Huang, J., Koroteev, D.D., 2021. Artificial intelligence for planning of energy and waste management. Sustain. Energy Technol. Assessments 47, 101426. https://doi.org/10.1016/j.seta.2021.101426
  175. Huang, L., Kang, J., Wan, M., Fang, L., Zhang, C., Zeng, Z., 2021. Solar Radiation Prediction Using Different Machine Learning Algorithms and Implications for Extreme Climate Events. Front. Earth Sci. 9. https://doi.org/10.3389/feart.2021.596860
  176. Huang, Yang, Gao, Jiang, Dong, 2019. A Novel Prediction Approach for Short-Term Renewable Energy Consumption in China Based on Improved Gaussian Process Regression. Energies 12, 4181. https://doi.org/10.3390/en12214181
  177. Ibidoja, O.J., Shan, F.P., Mukhtar, Sulaiman, J., Majahar Ali, M.K., 2023. Robust M-estimators and Machine Learning Algorithms for Improving the Predictive Accuracy of Seaweed Contaminated Big Data. J. Niger. Soc. Phys. Sci. 1137. https://doi.org/10.46481/jnsps.2023.1137
  178. IEA, 2022a. Tracking SDG 7: The Energy Progress Report 2022
  179. IEA, 2022b. World Energy Outlook 2022
  180. IEA, 2021. Tracking SDG 7: The Energy Progress Report 2021
  181. IEA, 2019. World Energy Outlook 2019
  182. IEA, 2015. World Energy Outlook. Paris
  183. Ihsan, A.F., Darmadi, Uttunggadewa, S., Rahmawati, S.D., Giovanni, I., Himawan, S.N., 2023. Performance Analysis of Deep Learning Implementation in Operational Condition Forecasting of a Gas Transmission Pipeline Network. Int. J. Adv. Sci. Eng. Inf. Technol. 13, 1423–1429. https://doi.org/10.18517/ijaseit.13.4.18250
  184. Ilham, A., Assaffat, L., Khikmah, L., Safuan, S., Suprapedi, S., 2023. k-Means Cluster-based Random Undersampling and Meta-Learning Approach for Village Development Status Classification. JOIV Int. J. Informatics Vis. 7, 542. https://doi.org/10.30630/joiv.7.2.989
  185. IRENA, 2013. Production of Bio-methanol: Technology Brief
  186. Istadi, I., Riyanto, T., Buchori, L., Anggoro, D.D., Pakpahan, A.W.S., Pakpahan, A.J., 2021. Biofuels Production from Catalytic Cracking of Palm Oil Using Modified HY Zeolite Catalysts over A Continuous Fixed Bed Catalytic Reactor. Int. J. Renew. Energy Dev. Vol 10, No 1 Febr. 2021DO - https://doi.org/10.14710/ijred.2021.33281
  187. Itto-Nakama, K., Watanabe, S., Kondo, N., Ohnuki, S., Kikuchi, R., Nakamura, T., Ogasawara, W., Kasahara, K., Ohya, Y., 2021. AI-based forecasting of ethanol fermentation using yeast morphological data. Biosci. Biotechnol. Biochem. 86, 125–134. https://doi.org/10.1093/bbb/zbab188
  188. Jahirul, M., Brown, R., Senadeera, W., O’Hara, I., Ristovski, Z., 2013. The Use of Artificial Neural Networks for Identifying Sustainable Biodiesel Feedstocks. Energies 6, 3764–3806. https://doi.org/10.3390/en6083764
  189. Jain, A., Bora, B.J., Kumar, R., Sharma, P., Deepanraj, B., Irshad, K., Ravikiran, C., 2023. Application of hybrid Taguchi L16 and desirability for model prediction and optimization in assessment of the performance of a novel Water Hyacinth biodiesel run diesel engine. Fuel 339, 127377. https://doi.org/10.1016/j.fuel.2022.127377
  190. Janarthanan, R., Maheshwari, R.U., Shukla, Prashant Kumar, Shukla, Piyush Kumar, Mirjalili, S., Kumar, M., 2021. Intelligent Detection of the PV Faults Based on Artificial Neural Network and Type 2 Fuzzy Systems. Energies 14, 6584. https://doi.org/10.3390/en14206584
  191. Jathar, L.D., Nikam, K., Awasarmol, U. V., Gurav, R., Patil, J.D., Shahapurkar, K., Soudagar, M.E.M., Khan, T.M.Y., Kalam, M.A., Hnydiuk-Stefan, A., Gürel, A.E., Hoang, A.T., Ağbulut, Ü., 2024. A comprehensive analysis of the emerging modern trends in research on photovoltaic systems and desalination in the era of artificial intelligence and machine learning. Heliyon 10, e25407. https://doi.org/10.1016/j.heliyon.2024.e25407
  192. Jebli, I., Belouadha, F.Z., Kabbaj, M.I., Tilioua, A., 2021. Prediction of solar energy guided by pearson correlation using machine learning. Energy. https://doi.org/10.1016/j.energy.2021.120109
  193. Jia, T., Li, C., Wang, H., Hu, Y., Wang, S., Xu, G., Hoang, A.T., 2024. Subsidy policy or dual-credit policy? Evolutionary game analysis of green methanol vehicles promotion. Energy 293, 130763. https://doi.org/10.1016/j.energy.2024.130763
  194. Jiang, H., 2023. Forecasting global solar radiation using a robust regularization approach with mixture kernels. J. Forecast. 42, 1989–2010. https://doi.org/10.1002/for.3001
  195. Jiang, P., Liu, Z., Niu, X., Zhang, L., 2021. A combined forecasting system based on statistical method, artificial neural networks, and deep learning methods for short-term wind speed forecasting. Energy 217, 119361
  196. Jiang, Z., Jia, Q.-S., Guan, X., 2017. Review of wind power forecasting methods: From multi-spatial and temporal perspective, in: 2017 36th Chinese Control Conference (CCC). IEEE, pp. 10576–10583
  197. Jin, S., Yang, Z., Królczykg, G., Liu, X., Gardoni, P., Li, Z., 2023. Garbage detection and classification using a new deep learning-based machine vision system as a tool for sustainable waste recycling. Waste Manag. 162, 123–130. https://doi.org/10.1016/j.wasman.2023.02.014
  198. Ju, Y., Sun, G., Chen, Q., Zhang, M., Zhu, H., Rehman, M.U., 2019. A Model Combining Convolutional Neural Network and LightGBM Algorithm for Ultra-Short-Term Wind Power Forecasting. IEEE Access 7, 28309–28318. https://doi.org/10.1109/ACCESS.2019.2901920
  199. Kahan, A., 2020. EIA projects nearly 50% increase in world energy usage by 2050, led by growth in Asia [WWW Document]. EIA
  200. Kahia, M., Moulahi, T., Mahfoudhi, S., Boubaker, S., Omri, A., 2022. A machine learning process for examining the linkage among disaggregated energy consumption, economic growth, and environmental degradation. Resour. Policy 79, 103104. https://doi.org/10.1016/j.resourpol.2022.103104
  201. Kaltschmitt, M., 2019. Energy from Organic Materials (Biomass). Springer New York, New York, NY. https://doi.org/10.1007/978-1-4939-7813-7
  202. Kang, Z., Yang, J., Li, G., Zhang, Z., 2020. An Automatic Garbage Classification System Based on Deep Learning. IEEE Access 8, 140019–140029. https://doi.org/10.1109/ACCESS.2020.3010496
  203. Kannan, R., Jet, C.C., Ramakrishnan, K., Ramdass, S., 2023. Predicting Student’s Soft Skills Based on Socio-Economical Factors: An Educational Data Mining Approach. JOIV Int. J. Informatics Vis. 7, 2040. https://doi.org/10.30630/joiv.7.3-2.2342
  204. Kapoor, R., Ghosh, P., Kumar, M., Sengupta, S., Gupta, A., Kumar, S.S., Vijay, V., Kumar, V., Kumar Vijay, V., Pant, D., 2020. Valorization of agricultural waste for biogas based circular economy in India: A research outlook. Bioresour. Technol. 304, 123036. https://doi.org/10.1016/j.biortech.2020.123036
  205. Kapp, S., Choi, J.-K., Hong, T., 2023. Predicting industrial building energy consumption with statistical and machine-learning models informed by physical system parameters. Renew. Sustain. Energy Rev. 172, 113045. https://doi.org/10.1016/j.rser.2022.113045
  206. Kartal, F., Özveren, U., 2022. Investigation of the chemical exergy of torrefied biomass from raw biomass by means of machine learning. Biomass and Bioenergy 159, 106383. https://doi.org/10.1016/j.biombioe.2022.106383
  207. Kaur, S., Brar, Y.S., Dhillon, J.S., 2021. Short-term Hydro-Thermal-Wind-Solar Power Scheduling: A Case Study of Kanyakumari Region of India. Int. J. Renew. Energy Dev. Vol 10, No 3 August 2021DO - https://doi.org/10.14710/ijred.2021.35558
  208. Kazemi Shariat Panahi, H., Dehhaghi, M., Aghbashlo, M., Karimi, K., Tabatabaei, M., 2020. Conversion of residues from agro-food industry into bioethanol in Iran: An under-valued biofuel additive to phase out MTBE in gasoline. Renew. Energy 145. https://doi.org/10.1016/j.renene.2019.06.081
  209. Keerthi, S.S., Lin, C.-J., 2003. Asymptotic Behaviors of Support Vector Machines with Gaussian Kernel. Neural Comput. 15, 1667–1689. https://doi.org/10.1162/089976603321891855
  210. khan, M., Raza Naqvi, S., Ullah, Z., Ali Ammar Taqvi, S., Nouman Aslam Khan, M., Farooq, W., Taqi Mehran, M., Juchelková, D., Štěpanec, L., 2023. Applications of machine learning in thermochemical conversion of biomass-A review. Fuel 332, 126055. https://doi.org/10.1016/j.fuel.2022.126055
  211. Khan, N., Ullah, F.U.M., Haq, I.U., Khan, S.U., Lee, M.Y., Baik, S.W., 2021. AB-Net: A Novel Deep Learning Assisted Framework for Renewable Energy Generation Forecasting. Mathematics 9, 2456. https://doi.org/10.3390/math9192456
  212. Khan, O., Parvez, M., Yahya, Z., Alhodaib, A., Yadav, A.K., Hoang, A.T., Ağbulut, Ü., 2024. Waste-to-energy power plants: Multi-objective analysis and optimization of landfill heat and methane gas by recirculation of leachate. Process Saf. Environ. Prot. 186, 957–968. https://doi.org/10.1016/j.psep.2024.04.022
  213. Khan, P.W., Byun, Y.-C., Lee, S.-J., Kang, D.-H., Kang, J.-Y., Park, H.-S., 2020. Machine Learning-Based Approach to Predict Energy Consumption of Renewable and Nonrenewable Power Sources. Energies 13, 4870. https://doi.org/10.3390/en13184870
  214. Khan, Y., Raman, R., Rashidi, M.M., Said, Z., Caliskan, H., Hoang, A.T., 2024. Thermodynamic and exergoenvironmental assessments of solar-assisted combined power cycle using eco-friendly fluids. J. Therm. Anal. Calorim. 149, 1125–1139. https://doi.org/10.1007/s10973-023-12760-7
  215. Kokila, M., Reddy K, S., 2025. Authentication, access control and scalability models in Internet of Things Security–A review. Cyber Secur. Appl. 3, 100057. https://doi.org/10.1016/j.csa.2024.100057
  216. Korberg, A.D., Skov, I.R., Mathiesen, B.V., 2020. The role of biogas and biogas-derived fuels in a 100% renewable energy system in Denmark. Energy 199, 117426. https://doi.org/10.1016/j.energy.2020.117426
  217. Kousheshi, N., Yari, M., Paykani, A., Saberi Mehr, A., de la Fuente, G.F., 2020. Effect of Syngas Composition on the Combustion and Emissions Characteristics of a Syngas/Diesel RCCI Engine. Energies 13, 212. https://doi.org/10.3390/en13010212
  218. Koutroulis, E., Kalaitzakis, K., 2003. Development of an integrated data-acquisition system for renewable energy sources systems monitoring. Renew. Energy 28, 139–152. https://doi.org/10.1016/S0960-1481(01)00197-5
  219. Kumar, A., Samadder, S.R., 2017. A review on technological options of waste to energy for effective management of municipal solid waste. Waste Manag. 69, 407–422. https://doi.org/10.1016/j.wasman.2017.08.046
  220. Kumar, S., Jain, S., Kumar, H., 2019. Prediction of jatropha-algae biodiesel blend oil yield with the application of artificial neural networks technique. Energy Sources, Part A Recover. Util. Environ. Eff. 41, 1285–1295
  221. Kumar, S.S., Kumar, A., Singh, S., Malyan, S.K., Baram, S., Sharma, J., Singh, R., Pugazhendhi, A., 2020. Industrial wastes: Fly ash, steel slag and phosphogypsum- potential candidates to mitigate greenhouse gas emissions from paddy fields. Chemosphere 241, 124824. https://doi.org/10.1016/j.chemosphere.2019.124824
  222. Kumara, E.A.D., Hettiarachchi, N.K., Jayathilake, K.G.R.M., 2017. Review Paper: Overview of the Vertical Axis Wind Turbines. Int. J. Sci. Res. Innov. Technol. 4, 56–67
  223. Larik, T.A., Jakhrani, A.Q., Jatoi, A.R., Mukwana, K.C., 2019. Performance analysis of a fabricated line focusing concentrated solar distillation system. Int. J. Renew. Energy Dev. 8, 185–192. https://doi.org/10.14710/ijred.8.2.185-192
  224. Le, A.T., Pandey, A., Sirohi, R., Sharma, P., Chen, W.-H., Pham, N.D.K., Tran, V.D., Nguyen, X.P., Hoang, A.T., 2023. Precise Prediction of Biochar Yield and Proximate Analysis by Modern Machine Learning and SHapley Additive exPlanations. Energy & Fuels 37, 17310–17327. https://doi.org/10.1021/acs.energyfuels.3c02868
  225. Le, T.T., Le, H.C., Paramasivam, P., Chung, N., 2024a. Artificial intelligence applications in solar energy. JOIV Int. J. Informatics Vis. 8
  226. Le, T.T., Sharma, P., Osman, S.M., Dzida, M., Nguyen, P.Q.P., Tran, M.H., Cao, D.N., Tran, V.D., 2024b. Forecasting energy consumption and carbon dioxide emission of Vietnam by prognostic models based on explainable machine learning and time series. Clean Technol. Environ. Policy. https://doi.org/10.1007/s10098-024-02852-9
  227. Li, B., Haneklaus, N., 2022. The potential of India’s net-zero carbon emissions: Analyzing the effect of clean energy, coal, urbanization, and trade openness. Energy Reports 8, 724–733. https://doi.org/10.1016/j.egyr.2022.01.241
  228. Li, D., Zhu, D., Tao, T., Qu, J., 2023. Power Generation Prediction for Photovoltaic System of Hose-Drawn Traveler Based on Machine Learning Models. Processes 12, 39. https://doi.org/10.3390/pr12010039
  229. Li, G., Liu, S., Wang, L., Zhu, R., 2020. Skin-inspired quadruple tactile sensors integrated on a robot hand enable object recognition. Sci. Robot. 5. https://doi.org/10.1126/scirobotics.abc8134
  230. Li, H., Chen, J., Zhang, W., Zhan, H., He, C., Yang, Z., Peng, H., Leng, L., 2023. Machine-learning-aided thermochemical treatment of biomass: a review. Biofuel Res. J. 10, 1786–1809. https://doi.org/10.18331/BRJ2023.10.1.4
  231. Li, J., Li, L., Suvarna, M., Pan, L., Tabatabaei, M., Ok, Y.S., Wang, X., 2022. Wet wastes to bioenergy and biochar: A critical review with future perspectives. Sci. Total Environ. 817, 152921. https://doi.org/10.1016/j.scitotenv.2022.152921
  232. Li, J., Yu, D., Pan, L., Xu, X., Wang, X., Wang, Y., 2023. Recent advances in plastic waste pyrolysis for liquid fuel production: Critical factors and machine learning applications. Appl. Energy 346, 121350. https://doi.org/10.1016/j.apenergy.2023.121350
  233. Li, R., Sun, H., Wei, X., Ta, W., Wang, H., 2022. Lithium Battery State-of-Charge Estimation Based on AdaBoost.Rt-RNN. Energies 15, 6056. https://doi.org/10.3390/en15166056
  234. Li, S., Leng, Y., Abed, A.M., Dutta, A.K., Ganiyeva, O., Fouad, Y., 2024. Waste-to-energy poly-generation scheme for hydrogen/freshwater/power/oxygen/heating capacity production; optimized by regression machine learning algorithms. Process Saf. Environ. Prot. 187, 876–891. https://doi.org/10.1016/j.psep.2024.04.118
  235. Li, X., Ma, L., Chen, P., Xu, H., Xing, Q., Yan, J., Lu, S., Fan, H., Yang, L., Cheng, Y., 2022. Probabilistic solar irradiance forecasting based on XGBoost. Energy Reports 8, 1087–1095. https://doi.org/10.1016/j.egyr.2022.02.251
  236. Li, X., Zhang, W., 2022. Physics-informed deep learning model in wind turbine response prediction. Renew. Energy 185, 932–944. https://doi.org/10.1016/j.renene.2021.12.058
  237. Liao, Z., Dai, S., Kuosmanen, T., 2024. Convex support vector regression. Eur. J. Oper. Res. 313, 858–870. https://doi.org/10.1016/j.ejor.2023.05.009
  238. Lin, X., Wang, H., Zhang, Q., Yao, C., Chen, C., Cheng, L., Li, Z., 2022. A Spatiotemporal Network Model for Global Ionospheric TEC Forecasting. Remote Sens. 14, 1717. https://doi.org/10.3390/rs14071717
  239. Lingayat, A.B., Chandramohan, V.P., Raju, V.R.K., Meda, V., 2020. A review on indirect type solar dryers for agricultural crops – Dryer setup, its performance, energy storage and important highlights. Appl. Energy 258, 114005. https://doi.org/10.1016/j.apenergy.2019.114005
  240. Lio, W.H., Li, A., Meng, F., 2021. Real-time rotor effective wind speed estimation using Gaussian process regression and Kalman filtering. Renew. Energy 169, 670–686. https://doi.org/10.1016/j.renene.2021.01.040
  241. Lipu, M.S.H., Miah, M.S., Hannan, M.A., Hussain, A., Sarker, M.R., Ayob, A., Saad, M.H.M., Mahmud, M.S., 2021. Artificial intelligence based hybrid forecasting approaches for wind power generation: Progress, challenges and prospects. IEEE Access 9, 102460–102489
  242. Lisbona, P., Pascual, S., Pérez, V., 2023. Waste to energy: Trends and perspectives. Chem. Eng. J. Adv. 14, 100494. https://doi.org/10.1016/j.ceja.2023.100494
  243. Liu, B., Liu, C., Xiao, Y., Liu, L., Li, W., Chen, X., 2022. AdaBoost-based transfer learning method for positive and unlabelled learning problem. Knowledge-Based Syst. 241, 108162. https://doi.org/10.1016/j.knosys.2022.108162
  244. Liu, M., Cao, Z., Zhang, J., Wang, L., Huang, C., Luo, X., 2020. Short-term wind speed forecasting based on the Jaya-SVM model. Int. J. Electr. Power Energy Syst. 121, 106056. https://doi.org/10.1016/j.ijepes.2020.106056
  245. Liu, Q., Zhang, G., Yu, J., Kong, G., Cao, T., Ji, G., Zhang, X., Han, L., 2024. Machine learning-aided hydrothermal carbonization of biomass for coal-like hydrochar production: Parameters optimization and experimental verification. Bioresour. Technol. 393, 130073. https://doi.org/10.1016/j.biortech.2023.130073
  246. Liu, T., Liu, S., Heng, J., Gao, Y., 2018. A New Hybrid Approach for Wind Speed Forecasting Applying Support Vector Machine with Ensemble Empirical Mode Decomposition and Cuckoo Search Algorithm. Appl. Sci. 8, 1754. https://doi.org/10.3390/app8101754
  247. Liu, Z., Jiang, P., Wang, J., Zhang, L., 2021. Ensemble forecasting system for short-term wind speed forecasting based on optimal sub-model selection and multi-objective version of mayfly optimization algorithm. Expert Syst. Appl. 177, 114974. https://doi.org/10.1016/j.eswa.2021.114974
  248. Lubbe, F., Maritz, J., Harms, T., 2020. Evaluating the Potential of Gaussian Process Regression for Solar Radiation Forecasting: A Case Study. Energies 13, 5509. https://doi.org/10.3390/en13205509
  249. Luo, X., Zhang, D., Zhu, X., 2021. Deep learning based forecasting of photovoltaic power generation by incorporating domain knowledge. Energy 225, 120240. https://doi.org/10.1016/j.energy.2021.120240
  250. Lv, Z., Chen, T., Cai, Z., Chen, Z., 2023. Machine Learning-Based Garbage Detection and 3D Spatial Localization for Intelligent Robotic Grasp. Appl. Sci. 13, 10018. https://doi.org/10.3390/app131810018
  251. Ma, Y., He, Y., Wang, L., Zhang, J., 2022. Probabilistic reconstruction for spatiotemporal sensor data integrated with Gaussian process regression. Probabilistic Eng. Mech. 69, 103264. https://doi.org/10.1016/j.probengmech.2022.103264
  252. Madhankumar, S., Viswanathan, K., Taipabu, M.I., Wu, W., 2023. A review on the latest developments in solar dryer technologies for food drying process. Sustain. Energy Technol. Assessments 58, 103298. https://doi.org/10.1016/j.seta.2023.103298
  253. Makarichi, L., Jutidamrongphan, W., Techato, K., 2018. The evolution of waste-to-energy incineration: A review. Renew. Sustain. Energy Rev. 91, 812–821
  254. Malhotra, C., Kotwal, V., Dalal, S., 2018. ETHICAL FRAMEWORK FOR MACHINE LEARNING, in: 2018 ITU Kaleidoscope: Machine Learning for a 5G Future (ITU K). IEEE, pp. 1–8. https://doi.org/10.23919/ITU-WT.2018.8597767
  255. Mandal, D.K., Biswas, N., Manna, N.K., Gayen, D.K., Benim, A.C., 2024. An application of artificial neural network (ANN) for comparative performance assessment of solar chimney (SC) plant for green energy production. Sci. Rep. 14, 979. https://doi.org/10.1038/s41598-023-46505-1
  256. Manimaran, R., Mohanraj, T., Ashwin, R., 2023. Green synthesized nano-additive dosed biodiesel-diesel-water emulsion blends for CI engine application: Performance, combustion, emission, and exergy analysis. J. Clean. Prod. 413, 137497. https://doi.org/10.1016/j.jclepro.2023.137497
  257. Manochio, C., Andrade, B.R., Rodriguez, R.P., Moraes, B.S., 2017. Ethanol from biomass: A comparative overview. Renew. Sustain. Energy Rev. 80, 743–755. https://doi.org/10.1016/j.rser.2017.05.063
  258. Marrel, A., Iooss, B., 2024. Probabilistic surrogate modeling by Gaussian process: A review on recent insights in estimation and validation. Reliab. Eng. Syst. Saf. 247, 110094. https://doi.org/10.1016/j.ress.2024.110094
  259. Martins, F., Felgueiras, C., Smitkova, M., Caetano, N., 2019. Analysis of Fossil Fuel Energy Consumption and Environmental Impacts in European Countries. Energies 12, 964. https://doi.org/10.3390/en12060964
  260. Marzouq, M., Bounoua, Z., Mechaqrane, A., Fadili, H.E., Lakhliai, Z., Zenkouar, K., 2018. ANN-based modelling and prediction of daily global solar irradiation using commonly measured meteorological parameters. IOP Conf. Ser. Earth Environ. Sci. 161, 012017. https://doi.org/10.1088/1755-1315/161/1/012017
  261. Medina, C., Ana, C.R.M., González, G., 2022. Transmission grids to foster high penetration of large-scale variable renewable energy sources–A review of challenges, problems, and solutions. Int. J. Renew. Energy Res. 12, 146–169
  262. Meenal, R., Selvakumar, A.I., 2018. Assessment of SVM, empirical and ANN based solar radiation prediction models with most influencing input parameters. Renew. Energy 121, 324–343. https://doi.org/10.1016/J.RENENE.2017.12.005
  263. Megawati, M., Bahlawan, Z.A.S., Damayanti, A., Putri, R.D.A., Triwibowo, B., Prasetiawan, H., 2022. Comparative Study on the Various Hydrolysis and Fermentation Methods of Chlorella vulgaris Biomass for the Production of Bioethanol. Int. J. Renew. Energy Dev. 11, 515–522. https://doi.org/10.14710/ijred.2022.41696
  264. Mellit, A., Pavan, A.M., 2010. A 24-h forecast of solar irradiance using artificial neural network: Application for performance prediction of a grid-connected PV plant at Trieste, Italy. Sol. Energy 84, 807–821. https://doi.org/10.1016/j.solener.2010.02.006
  265. Mohajeri, N., Assouline, D., Guiboud, B., Bill, A., Gudmundsson, A., Scartezzini, J.-L., 2018. A city-scale roof shape classification using machine learning for solar energy applications. Renew. Energy 121, 81–93. https://doi.org/10.1016/j.renene.2017.12.096
  266. Moonlight, L.S., Harianto, B.B., Suprapto, Y., Faizah, F., 2023. Forecasting the Currency Rate of The Indonesian Rupiah (IDR) against the US Dollar (USD) Using Time Series Data and Indonesian Fundamental Data. Int. J. Adv. Sci. Eng. Inf. Technol. 13, 694–702. https://doi.org/10.18517/ijaseit.13.2.17944
  267. Muhammad, G., Potchamyou Ngatcha, A.D., Lv, Y., Xiong, W., El-Badry, Y.A., Asmatulu, E., Xu, J., Alam, M.A., 2022. Enhanced biodiesel production from wet microalgae biomass optimized via response surface methodology and artificial neural network. Renew. Energy 184, 753–764. https://doi.org/10.1016/J.RENENE.2021.11.091
  268. Murugavelh, S., Anand, B., Midhun Prasad, K., Nagarajan, R., Azariah Pravin Kumar, S., 2019. Exergy analysis and kinetic study of tomato waste drying in a mixed mode solar tunnel dryer. Energy Sources, Part A Recover. Util. Environ. Eff. 00, 1–17. https://doi.org/10.1080/15567036.2019.1679289
  269. Nam, K., Hwangbo, S., Yoo, C., 2020. A deep learning-based forecasting model for renewable energy scenarios to guide sustainable energy policy: A case study of Korea. Renew. Sustain. Energy Rev. 122, 109725. https://doi.org/10.1016/j.rser.2020.109725
  270. Nascimento, E.G.S., de Melo, T.A.C., Moreira, D.M., 2023. A transformer-based deep neural network with wavelet transform for forecasting wind speed and wind energy. Energy 278, 127678. https://doi.org/10.1016/j.energy.2023.127678
  271. Nassef, A.M., Md Atiqure Rahman, S., Rezk, H., Said, Z., 2020. ANFIS-Based Modelling and Optimal Operating Parameter Determination to Enhance Cocoa Beans Drying-Rate. IEEE Access 8, 45964–45973. https://doi.org/10.1109/ACCESS.2020.2977165
  272. Nguyen-Thi, T.X., Bui, T.M.T., 2023. Effects of Injection Strategies on Mixture Formation and Combustion in a Spark-Ignition Engine Fueled with Syngas-Biogas-Hydrogen. Int. J. Renew. Energy Dev. 12, 118–128. https://doi.org/10.14710/ijred.2023.49368
  273. Nguyen, H.P., Nguyen, C.T.U., Tran, T.M., Dang, Q.H., Pham, N.D.K., 2024a. Artificial Intelligence and Machine Learning for Green Shipping : Navigating towards Sustainable Maritime Practices. Int. J. Informatics Vis. 8, 1–17
  274. Nguyen, H.P., Nguyen, P.Q.P., Nguyen, D.K.P., Bui, V.D., Nguyen, D.T., 2023. Application of IoT Technologies in Seaport Management. JOIV Int. J. Informatics Vis. 7, 228. https://doi.org/10.30630/joiv.7.1.1697
  275. Nguyen, T.B.N., Le, N.V.L., 2023. Biomass resources and thermal conversion biomass to biofuel for cleaner energy: A review. J. Emerg. Sci. Eng. 1, 6–13. https://doi.org/10.61435/jese.2023.2
  276. Nguyen, V.G., Dager, B., Chhillar, A., Sharma, P., Osman, S.M., Nguyen Le, D.T., Kowalski, J., Truong, T.H., Yadav, P.S., Cao, D.N., Tran, V.D., 2024b. Desirability-based optimization of dual-fuel diesel engine using acetylene as an alternative fuel. Case Stud. Therm. Eng. 59, 104488. https://doi.org/10.1016/j.csite.2024.104488
  277. Nguyen, V.G., Duong, X.Q., Nguyen, L.H., Nguyen, P.Q.P., Priya, J.C., Truong, T.H., Le, H.C., Pham, N.D.K., Nguyen, X.P., 2023a. An extensive investigation on leveraging machine learning techniques for high-precision predictive modeling of CO2 emission. Energy Sources, Part A Recover. Util. Environ. Eff. 45, 9149–9177. https://doi.org/10.1080/15567036.2023.2231898
  278. Nguyen, V.G., Rajamohan, S., Rudzki, K., Kozak, J., Sharma, P., Pham, N.D.K., Nguyen, P.Q.P., Xuan, P.N., 2023b. Using Artificial Neural Networks for Predicting Ship Fuel Consumption. Polish Marit. Res. 30, 39–60. https://doi.org/10.2478/pomr-2023-0020
  279. Nguyen, V.G., Sharma, P., Ağbulut, Ü., Le, H.S., Cao, D.N., Dzida, M., Osman, S.M., Le, H.C., Tran, V.D., 2024c. Improving the prediction of biochar production from various biomass sources through the implementation of eXplainable machine learning approaches. Int. J. Green Energy 1–28. https://doi.org/10.1080/15435075.2024.2326076
  280. Nguyen, V.G., Sharma, P., Ağbulut, Ü., Le, H.S., Truong, T.H., Dzida, M., Tran, M.H., Le, H.C., Tran, V.D., 2024d. Machine learning for the management of biochar yield and properties of biomass sources for sustainable energy. Biofuels, Bioprod. Biorefining 18, 567–593. https://doi.org/10.1002/bbb.2596
  281. Nguyen, V.G., Sirohi, R., Tran, M.H., Truong, T.H., Duong, M.T., Pham, M.T., Cao, D.N., 2024e. Renewable energy role in low-carbon economy and net-zero goal: Perspectives and prospects. Energy Environ. https://doi.org/10.1177/0958305X241253772
  282. Nguyen, V.G., Tran, M.H., Paramasivam, P., Le, H.C., Nguyen, D.T., 2024f. Biomass: A Versatile Resource for Biofuel, Industrial, and Environmental Solution. Int. J. Adv. Sci. Eng. Inf. Technol. 14, 268–286. https://doi.org/10.18517/ijaseit.14.1.17489
  283. Nguyen, V.N., Rudzki, K., Marek, D., Pham, N.D.K., Pham, M.T., Nguyen, P.Q.P., Nguyen, X.P., 2023. Understanding fuel saving and clean fuel strategies towards green maritime. Polish Marit. Res. 30, 146–164. https://doi.org/10.2478/pomr-2023-0030
  284. Nguyen, V.N., Sharma, P., Rowinski, L., Le, H.C., Le, D.T.N., Osman, S.M., Le, H.S., Truong, T.H., Nguyen, P.Q.P., Cao, D.N., 2024a. Biochar‐based catalysts derived from biomass waste: production, characterization, and application for liquid biofuel synthesis. Biofuels, Bioprod. Biorefining. https://doi.org/10.1002/bbb.2593
  285. Nguyen, V.N., Tarełko, W., Sharma, P., El-Shafay, A.S., Chen, W.-H., Nguyen, P.Q.P., Nguyen, X.P., Hoang, A.T., 2024b. Potential of Explainable Artificial Intelligence in Advancing Renewable Energy: Challenges and Prospects. Energy & Fuels 38, 1692–1712. https://doi.org/10.1021/acs.energyfuels.3c04343
  286. Nguyen, X.P., Hoang, A.T., Ölçer, A.I., Huynh, T.T., 2021a. Record decline in global CO2 emissions prompted by COVID-19 pandemic and its implications on future climate change policies. Energy Sources, Part A Recover. Util. Environ. Eff. 1–4. https://doi.org/10.1080/15567036.2021.1879969
  287. Nguyen, X.P., Le, N.D., Pham, V.V., Huynh, T.T., Dong, V.H., Hoang, A.T., 2021b. Mission, challenges, and prospects of renewable energy development in Vietnam. Energy Sources, Part A Recover. Util. Environ. Eff. 1–13. https://doi.org/10.1080/15567036.2021.1965264
  288. Niu, Y., Lv, Y., Lei, Y., Liu, S., Liang, Y., Wang, D., Hui, S., 2019. Biomass torrefaction: properties, applications, challenges, and economy. Renew. Sustain. Energy Rev. 115, 109395. https://doi.org/10.1016/j.rser.2019.109395
  289. Notton, G., Voyant, C., Fouilloy, A., Duchaud, J.L., Nivet, M.L., 2019. Some applications of ANN to solar radiation estimation and forecasting for energy applications. Appl. Sci. 9, 209
  290. Okolie, J.A., 2024. Introduction of machine learning and artificial intelligence in biofuel technology. Curr. Opin. Green Sustain. Chem. 47, 100928. https://doi.org/10.1016/j.cogsc.2024.100928
  291. Okolie, J.A., Epelle, E.I., Tabat, M.E., Orivri, U., Amenaghawon, A.N., Okoye, P.U., Gunes, B., 2022. Waste biomass valorization for the production of biofuels and value-added products: A comprehensive review of thermochemical, biological and integrated processes. Process Saf. Environ. Prot. 159, 323–344. https://doi.org/10.1016/j.psep.2021.12.049
  292. Okonkwo, P.C., Barhoumi, E.M., Mansir, I.B., Emori, W., Uzoma, P.C., 2022. Techno-economic analysis and optimization of solar and wind energy systems for hydrogen production: a case study. Energy Sources, Part A Recover. Util. Environ. Eff. 44, 9119–9134
  293. Okumuş, F., Ekmekçioğlu, A., Kara, S.S., 2021. Modelling Ships Main and Auxiliary Engine Powers with Regression-Based Machine Learning Algorithms. Polish Marit. Res. 28, 83–96. https://doi.org/doi: 10.2478/pomr-2021-0008
  294. Oueslati, F., 2023. HOMER optimization of standalone PV/Wind/Battery powered hydrogen refueling stations located at twenty selected French cities. Int. J. Renew. Energy Dev. Vol 12, No 6 Novemb. 2023DO - https://doi.org/10.14710/ijred.2023.58218
  295. Ozbas, E.E., Aksu, D., Ongen, A., Aydin, M.A., Ozcan, H.K., 2019. Hydrogen production via biomass gasification, and modeling by supervised machine learning algorithms. Int. J. Hydrogen Energy 44, 17260–17268. https://doi.org/10.1016/j.ijhydene.2019.02.108
  296. Palanichamy, J., Palani, S., 2014. Simulation of anaerobic digestion processes using stochastic algorithm. J. Environ. Heal. Sci. Eng. 12, 121. https://doi.org/10.1186/s40201-014-0121-7
  297. Pan, M., Xing, Q., Chai, Z., Zhao, H., Sun, Q., Duan, D., 2023. Real-time digital twin machine learning-based cost minimization model for renewable-based microgrids considering uncertainty. Sol. Energy 250, 355–367. https://doi.org/10.1016/j.solener.2023.01.006
  298. Paramasivama, P., Naima, K., Dzida, M., 2024. Soft computing-based modelling and optimization of NOx emission from a variable compression ratio diesel engine. J. Emerg. Sci. Eng. 2, e21. https://doi.org/10.61435/jese.2024.e21
  299. Parveen, N., Mullah, M., Ahshanullah, M., 2016. Tweedie Model for Analyzing Zero-Inflated Continuous Response: An Application to Job Training Data. Br. J. Econ. Manag. Trade 14, 1–7. https://doi.org/10.9734/BJEMT/2016/26043
  300. Pehlken, A., Garmatter, H., Dawel, L., Cyris, F., Beck, H., Schwark, F., Scharf, R., Niebe, A., 2022. How can machine learning improve waste-to-energy plant operation, in: 2022 IEEE 28th International Conference on Engineering, Technology and Innovation (ICE/ITMC) & 31st International Association For Management of Technology (IAMOT) Joint Conference. IEEE, pp. 1–8. https://doi.org/10.1109/ICE/ITMC-IAMOT55089.2022.10033210
  301. Peng, W., Karimi Sadaghiani, O., 2024. Machine learning for sustainable reutilization of waste materials as energy sources – a comprehensive review. Int. J. Green Energy 21, 1641–1666. https://doi.org/10.1080/15435075.2023.2255647
  302. Petterle, R.R., Bonat, W.H., Kokonendji, C.C., Seganfredo, J.C., Moraes, A., da Silva, M.G., 2019. Double Poisson-Tweedie Regression Models. Int. J. Biostat. 15. https://doi.org/10.1515/ijb-2018-0119
  303. Phap, V.M., Sang, L.Q., Ninh, N.Q., Binh, D. Van, Hung, B.B., Huyen, C.T.T., Tung, N.T., 2022. Feasibility analysis of hydrogen production potential from rooftop solar power plant for industrial zones in Vietnam. Energy Reports 8, 14089–14101. https://doi.org/10.1016/j.egyr.2022.10.337
  304. Philibert, C., 2017. Renewable Energy for Industry: From green energy to green materials and fuels
  305. Pitalúa-Díaz, N., Arellano-Valmaña, F., Ruz-Hernandez, J.A., Matsumoto, Y., Alazki, H., Herrera-López, E.J., Hinojosa-Palafox, J.F., García-Juárez, A., Pérez-Enciso, R.A., Velázquez-Contreras, E.F., 2019. An ANFIS-Based Modeling Comparison Study for Photovoltaic Power at Different Geographical Places in Mexico. Energies 12, 2662. https://doi.org/10.3390/en12142662
  306. Ponkumar, G., Jayaprakash, S., Kanagarathinam, K., 2023. Advanced machine learning techniques for accurate very-short-term wind power forecasting in wind energy systems using historical data analysis. Energies 16, 5459
  307. Prasada Rao, K., Victor Babu, T., Anuradha, G., Appa Rao, B. V., 2017. IDI diesel engine performance and exhaust emission analysis using biodiesel with an artificial neural network (ANN). Egypt. J. Pet. https://doi.org/10.1016/j.ejpe.2016.08.006
  308. Premalatha, S., Baskar, N., 2012. Implementation of supervised statistical data mining algorithm for single machine scheduling. J. Adv. Manag. Res. 9, 170–177. https://doi.org/10.1108/09727981211271913
  309. Puri, D., Nalbalwar, S., Nandgaonkar, A., Rajput, J., Wagh, A., 2023. Identification of Alzheimer’s Disease Using Novel Dual Decomposition Technique and Machine Learning Algorithms from EEG Signals. Int. J. Adv. Sci. Eng. Inf. Technol. 13, 658–665. https://doi.org/10.18517/ijaseit.13.2.18252
  310. Purwanto, E., Ferdiansyah, I., Nugraha, S.D., Qudsi, O.A., 2021. The Effect of ANFIS Controller on The Performance of Induction Motor Drives in Low-Speed Operation Based on IFOC. Int. J. Adv. Sci. Eng. Inf. Technol. 11, 440–450. https://doi.org/10.18517/ijaseit.11.2.12087
  311. Qaiyum, S., Margala, M., Kshirsagar, P.R., Chakrabarti, P., Irshad, K., 2023. Energy Performance Analysis of Photovoltaic Integrated with Microgrid Data Analysis Using Deep Learning Feature Selection and Classification Techniques. Sustainability 15, 11081. https://doi.org/10.3390/su151411081
  312. Qin, X., Cheng, S., Xing, B., Qu, X., Shi, C., Meng, W., Zhang, C., Xia, H., 2023. Preparation of pyrolysis products by catalytic pyrolysis of poplar: Application of biochar in antibiotic wastewater treatment. Chemosphere 338, 139519. https://doi.org/10.1016/j.chemosphere.2023.139519
  313. Rabehi, A., Guermoui, M., Lalmi, D., 2020. Hybrid models for global solar radiation prediction: a case study. Int. J. Ambient Energy 41, 31–40. https://doi.org/10.1080/01430750.2018.1443498
  314. Radonjić, A., Pjevčević, D., Maraš, V., 2020. Neural Network Ensemble Approach to Pushed Convoys Dispatching Problems. Polish Marit. Res. https://doi.org/10.2478/pomr-2020-0008
  315. Rangel-Martinez, D., Nigam, K.D.P., Ricardez-Sandoval, L.A., 2021. Machine learning on sustainable energy: A review and outlook on renewable energy systems, catalysis, smart grid and energy storage. Chem. Eng. Res. Des. 174, 414–441
  316. Raschka, S., Patterson, J., Nolet, C., 2020. Machine Learning in Python: Main Developments and Technology Trends in Data Science, Machine Learning, and Artificial Intelligence. Information 11, 193. https://doi.org/10.3390/info11040193
  317. Rathore, A.S., Singh, A., 2022. Biomass to fuels and chemicals: A review of enabling processes and technologies. J. Chem. Technol. Biotechnol. 97, 597–607. https://doi.org/10.1002/jctb.6960
  318. Reis, M.F., 2011. Solid Waste Incinerators: Health Impacts, in: Encyclopedia of Environmental Health. Elsevier, pp. 162–217. https://doi.org/10.1016/B978-0-444-52272-6.00489-X
  319. Resch, B., Sagl, G., Törnros, T., Bachmaier, A., Eggers, J.-B., Herkel, S., Narmsara, S., Gündra, H., 2014. GIS-Based Planning and Modeling for Renewable Energy: Challenges and Future Research Avenues. ISPRS Int. J. Geo-Information 3, 662–692. https://doi.org/10.3390/ijgi3020662
  320. Rezania, S., Oryani, B., Nasrollahi, V.R., Darajeh, N., Lotfi Ghahroud, M., Mehranzamir, K., 2023. Review on Waste-to-Energy Approaches toward a Circular Economy in Developed and Developing Countries. Processes 11, 2566. https://doi.org/10.3390/pr11092566
  321. Richardson, Y., Drobek, M., Julbe, A., Blin, J., Pinta, F., 2015. Biomass Gasification to Produce Syngas, in: Recent Advances in Thermo-Chemical Conversion of Biomass. Elsevier, pp. 213–250. https://doi.org/10.1016/B978-0-444-63289-0.00008-9
  322. Ronda, A., Haro, P., Gómez-Barea, A., 2023. Sustainability assessment of alternative waste-to-energy technologies for the management of sewage sludge. Waste Manag. 159, 52–62. https://doi.org/10.1016/j.wasman.2023.01.025
  323. Roscher, R., Bohn, B., Duarte, M.F., Garcke, J., 2020. Explainable Machine Learning for Scientific Insights and Discoveries. IEEE Access 8, 42200–42216. https://doi.org/10.1109/ACCESS.2020.2976199
  324. Rosiani, D., Gibral Walay, M., Rahalintar, P., Candra, A.D., Sofyan, A., Arison Haratua, Y., 2023. Application of Artificial Intelligence in Predicting Oil Production Based on Water Injection Rate. Int. J. Adv. Sci. Eng. Inf. Technol. 13, 2338–2344. https://doi.org/10.18517/ijaseit.13.6.19399
  325. Routis, G., Michailidis, M., Roussaki, I., 2024. Plant Disease Identification Using Machine Learning Algorithms on Single-Board Computers in IoT Environments. Electronics 13, 1010. https://doi.org/10.3390/electronics13061010
  326. Safari, M., Tahmasbi, V., Rabiee, A.H., 2021. Investigation into the automatic drilling of cortical bones using ANFIS-PSO and sensitivity analysis. Neural Comput. Appl. 33, 16499–16517. https://doi.org/10.1007/s00521-021-06248-4
  327. Said, Z., Rahman, S., Sharma, P., Amine Hachicha, A., Issa, S., 2022a. Performance characterization of a solar-powered shell and tube heat exchanger utilizing MWCNTs/Water-based nanofluids: An experimental, Numerical, and Artificial Intelligence approach. Appl. Therm. Eng. 118633. https://doi.org/10.1016/j.applthermaleng.2022.118633
  328. Said, Z., Sharma, P., Bora, B.J., Nguyen, V.N., Bui, T.A.E., Nguyen, D.T., Dinh, X.T., Nguyen, X.P., 2023a. Modeling-optimization of performance and emission characteristics of dual-fuel engine powered with pilot diesel and agricultural-food waste-derived biogas. Int. J. Hydrogen Energy 48, 6761–6777. https://doi.org/10.1016/j.ijhydene.2022.07.150
  329. Said, Z., Sharma, P., Thi Bich Nhuong, Q., Bora, B.J., Lichtfouse, E., Khalid, H.M., Luque, R., Nguyen, X.P., Hoang, A.T., 2023b. Intelligent approaches for sustainable management and valorisation of food waste. Bioresour. Technol. 377, 128952. https://doi.org/10.1016/j.biortech.2023.128952
  330. Said, Z., Sharma, P., Tiwari, A.K., Le, V.V., Huang, Z., Bui, V.G., Hoang, A.T., 2022b. Application of novel framework based on ensemble boosted regression trees and Gaussian process regression in modelling thermal performance of small-scale Organic Rankine Cycle (ORC) using hybrid nanofluid. J. Clean. Prod. 360, 132194. https://doi.org/10.1016/j.jclepro.2022.132194
  331. Salmenperä, H., Pitkänen, K., Kautto, P., Saikku, L., 2021. Critical factors for enhancing the circular economy in waste management. J. Clean. Prod. 280, 124339. https://doi.org/10.1016/j.jclepro.2020.124339
  332. Sankari Subbiah, S., Kumar Paramasivan, S., Arockiasamy, K., Senthivel, S., Thangavel, M., 2023. Deep Learning for Wind Speed Forecasting Using Bi-LSTM with Selected Features. Intell. Autom. Soft Comput. 35, 3829–3844. https://doi.org/10.32604/iasc.2023.030480
  333. Saraswat, P., Agrawal, R., 2023. Artificial Intelligence as Key Enabler for Sustainable Maintenance in the Manufacturing Industry: Scope & Challenges. Evergreen 10, 2490–2497. https://doi.org/10.5109/7162012
  334. Saravanakumar, A., Vijayakumar, P., Hoang, A.T., Kwon, E.E., Chen, W.-H., 2023. Thermochemical conversion of large-size woody biomass for carbon neutrality: Principles, applications, and issues. Bioresour. Technol. 370, 128562. https://doi.org/10.1016/j.biortech.2022.128562
  335. Saravanan, R., Sujatha, P., 2018. A State of Art Techniques on Machine Learning Algorithms: A Perspective of Supervised Learning Approaches in Data Classification, in: 2018 Second International Conference on Intelligent Computing and Control Systems (ICICCS). IEEE, pp. 945–949. https://doi.org/10.1109/ICCONS.2018.8663155
  336. Sarp, A.O., Menguc, E.C., Peker, M., Guvenc, B.C., 2022. Data-Adaptive Censoring for Short-Term Wind Speed Predictors Based on MLP, RNN, and SVM. IEEE Syst. J. 16, 3625–3634. https://doi.org/10.1109/JSYST.2022.3150749
  337. Sayed, E.T., Wilberforce, T., Elsaid, K., Rabaia, M.K.H., Abdelkareem, M.A., Chae, K.-J., Olabi, A.G., 2021. A critical review on environmental impacts of renewable energy systems and mitigation strategies: Wind, hydro, biomass and geothermal. Sci. Total Environ. 766, 144505. https://doi.org/10.1016/j.scitotenv.2020.144505
  338. Secilmis, A., Aksu, N., Dael, F.A., Shayea, I., El-Saleh, A.A., 2023. Machine Learning-Based Fire Detection: A Comprehensive Review and Evaluation of Classification Models. JOIV Int. J. Informatics Vis. 7, 1982. https://doi.org/10.30630/joiv.7.3-2.2332
  339. Sen, P.C., Hajra, M., Ghosh, M., 2020. Supervised Classification Algorithms in Machine Learning: A Survey and Review. pp. 99–111. https://doi.org/10.1007/978-981-13-7403-6_11
  340. Seo, M.W., Lee, S.H., Nam, H., Lee, D., Tokmurzin, D., Wang, S., Park, Y.-K., 2022. Recent advances of thermochemical conversion processes for biorefinery. Bioresour. Technol. 343, 126109. https://doi.org/10.1016/j.biortech.2021.126109
  341. Serrano, D., Castelló, D., 2020. Tar prediction in bubbling fluidized bed gasification through artificial neural networks. Chem. Eng. J. 402, 126229. https://doi.org/10.1016/j.cej.2020.126229
  342. Serrano, D., Golpour, I., Sánchez-Delgado, S., 2020. Predicting the effect of bed materials in bubbling fluidized bed gasification using artificial neural networks (ANNs) modeling approach. Fuel 266, 117021. https://doi.org/10.1016/j.fuel.2020.117021
  343. Shahraki, A., Abbasi, M., Haugen, Ø., 2020. Boosting algorithms for network intrusion detection: A comparative evaluation of Real AdaBoost, Gentle AdaBoost and Modest AdaBoost. Eng. Appl. Artif. Intell. 94, 103770. https://doi.org/10.1016/j.engappai.2020.103770
  344. Shakibi, H., Assareh, E., Chitsaz, A., Keykhah, S., Behrang, M., Golshanzadeh, M., Ghodrat, M., Lee, M., 2023. Exergoeconomic and optimization study of a solar and wind-driven plant employing machine learning approaches; a case study of Las Vegas city. J. Clean. Prod. 385, 135529. https://doi.org/10.1016/j.jclepro.2022.135529
  345. Shams Amiri, S., Mottahedi, S., Lee, E.R., Hoque, S., 2021. Peeking inside the black-box: Explainable machine learning applied to household transportation energy consumption. Comput. Environ. Urban Syst. 88, 101647. https://doi.org/10.1016/j.compenvurbsys.2021.101647
  346. Shao, H., Deng, X., Cui, F., 2016. Short‐term wind speed forecasting using the wavelet decomposition and AdaBoost technique in wind farm of East China. IET Gener. Transm. Distrib. 10, 2585–2592. https://doi.org/10.1049/iet-gtd.2015.0911
  347. Sharma, A., Gunreddy, N., Mulamalla, A.R., Duraisamy, S., Sivan, S., Poongavanam, G.K., Kumar, B., 2022. Conductive and convective heat transfer augmentation in flat plate solar collector from energy, economic and environmental perspectives — a comprehensive review. Environ. Sci. Pollut. Res. 29, 87019–87067. https://doi.org/10.1007/s11356-022-23694-2
  348. Sharma, P., 2021. Artificial intelligence‐based model prediction of biodiesel‐fueled engine performance and emission characteristics: A comparative evaluation of gene expression programming and artificial neural network. Heat Transf. 50, 5563–5587. https://doi.org/10.1002/htj.22138
  349. Sharma, P., Sahoo, B.B., 2022. An ANFIS-RSM based modeling and multi-objective optimization of syngas powered dual-fuel engine. Int. J. Hydrogen Energy. https://doi.org/10.1016/J.IJHYDENE.2022.04.093
  350. Sharma, P., Sahoo, B.B., Said, Z., Hadiyanto, H., Nguyen, X.P., Nižetić, S., Huang, Z., Hoang, A.T., Li, C., 2023. Application of machine learning and Box-Behnken design in optimizing engine characteristics operated with a dual-fuel mode of algal biodiesel and waste-derived biogas. Int. J. Hydrogen Energy 48, 6738–6760. https://doi.org/10.1016/j.ijhydene.2022.04.152
  351. Sharma, P., Said, Z., Kumar, A., Nižetić, S., Pandey, A., Hoang, A.T., Huang, Z., Afzal, A., Li, C., Le, A.T., Nguyen, X.P., Tran, V.D., 2022a. Recent Advances in Machine Learning Research for Nanofluid-Based Heat Transfer in Renewable Energy System. Energy & Fuels 36, 6626–6658. https://doi.org/10.1021/acs.energyfuels.2c01006
  352. Sharma, P., Said, Z., Memon, S., Elavarasan, R.M., Khalid, M., Nguyen, X.P., Arıcı, M., Hoang, A.T., Nguyen, L.H., 2022b. Comparative evaluation of AI‐based intelligent GEP and ANFIS models in prediction of thermophysical properties of Fe3O4‐coated MWCNT hybrid nanofluids for potential application in energy systems. Int. J. Energy Res. 46, 19242–19257. https://doi.org/10.1002/er.8010
  353. Sharma, P., Sharma, A.K., 2022. Statistical and Continuous Wavelet Transformation-Based Analysis of Combustion Instabilities in a Biodiesel-Fueled Compression Ignition Engine. J. Energy Resour. Technol. 144. https://doi.org/10.1115/1.4051340
  354. Sharma, S., Basu, S., Shetti, N.P., Kamali, M., Walvekar, P., Aminabhavi, T.M., 2020. Waste-to-energy nexus: A sustainable development. Environ. Pollut. 267, 115501. https://doi.org/10.1016/j.envpol.2020.115501
  355. Sharma, V., Sharma, D., Tsai, M.-L., Ortizo, R.G.G., Yadav, A., Nargotra, P., Chen, C.-W., Sun, P.-P., Dong, C.-D., 2023a. Insights into the recent advances of agro-industrial waste valorization for sustainable biogas production. Bioresour. Technol. 390, 129829. https://doi.org/10.1016/j.biortech.2023.129829
  356. Sharma, V., Tsai, M.-L., Chen, C.-W., Sun, P.-P., Nargotra, P., Dong, C.-D., 2023b. Advances in machine learning technology for sustainable biofuel production systems in lignocellulosic biorefineries. Sci. Total Environ. 886, 163972. https://doi.org/10.1016/j.scitotenv.2023.163972
  357. Sharmila, V.G., Shanmugavel, S.P., Banu, J.R., 2024. A review on emerging technologies and machine learning approaches for sustainable production of biofuel from biomass waste. Biomass and Bioenergy 180, 106997. https://doi.org/10.1016/j.biombioe.2023.106997
  358. Shawon, S.M.R.H., Saaklayen, M.A., Liang, X., 2021. Wind Speed Forecasting by Conventional Statistical Methods and Machine Learning Techniques, in: 2021 IEEE Electrical Power and Energy Conference (EPEC). IEEE, pp. 304–309. https://doi.org/10.1109/EPEC52095.2021.9621686
  359. Shelare, S.D., Belkhode, P.N., Nikam, K.C., Jathar, L.D., Shahapurkar, K., Soudagar, M.E.M., Veza, I., Khan, T.M.Y., Kalam, M.A., Nizami, A.-S., Rehan, M., 2023. Biofuels for a sustainable future: Examining the role of nano-additives, economics, policy, internet of things, artificial intelligence and machine learning technology in biodiesel production. Energy 282, 128874. https://doi.org/10.1016/j.energy.2023.128874
  360. Shi, J., Lee, W.-J., Liu, Y., Yang, Y., Wang, P., 2012. Forecasting Power Output of Photovoltaic Systems Based on Weather Classification and Support Vector Machines. IEEE Trans. Ind. Appl. 48, 1064–1069. https://doi.org/10.1109/TIA.2012.2190816
  361. Sierra-García, J.E., Santos, M., 2020. Performance Analysis of a Wind Turbine Pitch Neurocontroller with Unsupervised Learning. Complexity 2020, 1–15. https://doi.org/10.1155/2020/4681767
  362. Sigiel, N., Chodnicki, M., Socik, P., Kot, R., 2024. Automatic Classification of Unexploded Ordnance (UXO) Based on Deep Learning Neural Networks (DLNNS). Polish Marit. Res. 31, 77–84. https://doi.org/doi: 10.2478/pomr-2024-0008
  363. Simankov, V., Buchatskiy, P., Teploukhov, S., Onishchenko, S., Kazak, A., Chetyrbok, P., 2023. Review of Estimating and Predicting Models of the Wind Energy Amount. Energies 16, 5926
  364. Singh, J., Singh, S., Mohapatra, S.K., 2020. Production of syngas from agricultural residue as a renewable fuel and its sustainable use in dual-fuel compression ignition engine to investigate performance, emission, and noise characteristics. Energy Sources, Part A Recover. Util. Environ. Eff. 42, 41–55. https://doi.org/10.1080/15567036.2019.1587053
  365. Singh, P., Chauhan, S.R., Goel, V., Gupta, A.K., 2020. Enhancing Diesel Engine Performance and Reducing Emissions Using Binary Biodiesel Fuel Blend. J. Energy Resour. Technol. 142. https://doi.org/10.1115/1.4044058
  366. Sitharthan, R., Swaminathan, J.N., Parthasarathy, T., 2018. Exploration of Wind Energy in India: A Short Review, in: 2018 National Power Engineering Conference (NPEC). IEEE, pp. 1–5. https://doi.org/10.1109/NPEC.2018.8476733
  367. Sivakumar, S., Neeraja, B., Jamuna Rani, M., Anandaram, H., Ramya, S., Padhan, G., Gurusamy, S., 2022. Machine Learning Approach on Time Series for PV-Solar Energy. Adv. Mater. Sci. Eng. 2022, 1–7. https://doi.org/10.1155/2022/6458377
  368. Sivamani, S., Selvakumar, S., Rajendran, K., Muthusamy, S., 2019. Artificial neural network–genetic algorithm-based optimization of biodiesel production from Simarouba glauca. Biofuels 10, 393–401. https://doi.org/10.1080/17597269.2018.1432267
  369. Smirnova, E., Kot, S., Kolpak, E., Shestak, V., 2021. Governmental support and renewable energy production: A cross-country review. Energy 230, 120903
  370. Son Le, H., Chen, W.-H., Forruque Ahmed, S., Said, Z., Rafa, N., Tuan Le, A., Ağbulut, Ü., Veza, I., Phuong Nguyen, X., Quang Duong, X., Huang, Z., Hoang, A.T., 2022. Hydrothermal carbonization of food waste as sustainable energy conversion path. Bioresour. Technol. 363, 127958. https://doi.org/10.1016/j.biortech.2022.127958
  371. Stahl, B.C., 2021. Ethical Issues of AI, in: Artificial Intelligence for a Better Future. Springer, Cham, pp. 35–53. https://doi.org/10.1007/978-3-030-69978-9_4
  372. Stetco, A., Dinmohammadi, F., Zhao, X., Robu, V., Flynn, D., Barnes, M., Keane, J., Nenadic, G., 2019. Machine learning methods for wind turbine condition monitoring: A review. Renew. Energy 133, 620–635. https://doi.org/10.1016/j.renene.2018.10.047
  373. Su, W., Zhang, Q., Liu, Y., 2023. Event-Triggered Adaptive Neural Network Trajectory Tracking Control For Underactuated Ships Under Uncertain Disturbance. Polish Marit. Res. 30, 119–131. https://doi.org/doi: 10.2478/pomr-2023-0045
  374. Subhashini, P., Chitra, P., Muthuvairavan Pillai, N., Vanitha, M., 2023. Theoretical Enhancement of Energy Production Performance in PV Arrays through Effective Shadow Detection Using Hybrid Technique. Sol. Energy 264, 112006. https://doi.org/10.1016/j.solener.2023.112006
  375. Sumari, A.D.W., Adinandra, D.E., Syulistyo, A.R., Lovrencic, S., 2022. Intelligent Military Aircraft Recognition and Identification to Support Military Personnel on the Air Observation Operation. Int. J. Adv. Sci. Eng. Inf. Technol. 12, 2571–2580. https://doi.org/10.18517/ijaseit.12.6.16944
  376. Sun, Q., Zhou, W.-X., Fan, J., 2020. Adaptive Huber Regression. J. Am. Stat. Assoc. 115, 254–265. https://doi.org/10.1080/01621459.2018.1543124
  377. Sunil Kumar, K., Surakasi, R., Patro, S.G.K., Govil, N., Ramis, M.K., Razak, A., Sharma, P., Alsubih, M., Islam, S., Khan, T.M.Y., Almakayeel, N., Chintakindi, S., 2024. Performance, Combustion, and Emission analysis of diesel engine fuelled with pyrolysis oil blends and n-propyl alcohol-RSM optimization and ML modelling. J. Clean. Prod. 434, 140354. https://doi.org/10.1016/j.jclepro.2023.140354
  378. Suvon, I.H., Loh, Y.P., Hashim, N., Mohd-Isa, W.N., Ting, C.Y., Ghauth, K.I., Bhattacharijee, A., Matsah, W.R., 2023. Business Category Classification via Indistinctive Satellite Image Analysis Using Deep Learning. Int. J. Adv. Sci. Eng. Inf. Technol. 13, 2219–2230. https://doi.org/10.18517/ijaseit.13.6.19059
  379. Swasono, D.I., Tjandrasa, H., Fatichah, C., 2022. Classification of Air-Cured Tobacco Leaf Pests Using Pruning Convolutional Neural Networks and Transfer Learning. Int. J. Adv. Sci. Eng. Inf. Technol. 12, 1229–1235. https://doi.org/10.18517/ijaseit.12.3.15950
  380. Taghavifar, H., Perera, L.P., 2023. Data-driven modeling of energy-exergy in marine engines by supervised ANNs based on fuel type and injection angle classification. Process Saf. Environ. Prot. 172, 546–561
  381. Taki, M., Rohani, A., 2022a. Machine learning models for prediction the Higher Heating Value (HHV) of Municipal Solid Waste (MSW) for waste-to-energy evaluation. Case Stud. Therm. Eng. 31, 101823. https://doi.org/10.1016/j.csite.2022.101823
  382. Taki, M., Rohani, A., 2022b. Machine learning models for prediction the Higher Heating Value (HHV) of Municipal Solid Waste (MSW) for waste-to-energy evaluation. Case Stud. Therm. Eng. 31, 101823. https://doi.org/10.1016/j.csite.2022.101823
  383. Taki, M., Rohani, A., Yildizhan, H., 2021. Application of machine learning for solar radiation modeling. Theor. Appl. Climatol. 143, 1599–1613. https://doi.org/10.1007/s00704-020-03484-x
  384. Tan, K.M., Babu, T.S., Ramachandaramurthy, V.K., Kasinathan, P., Solanki, S.G., Raveendran, S.K., 2021. Empowering smart grid: A comprehensive review of energy storage technology and application with renewable energy integration. J. Energy Storage 39, 102591. https://doi.org/10.1016/j.est.2021.102591
  385. Tang, J.Y., Chung, B.Y.H., Ang, J.C., Chong, J.W., Tan, R.R., Aviso, K.B., Chemmangattuvalappil, N.G., Thangalazhy-Gopakumar, S., 2023. Prediction model for biochar energy potential based on biomass properties and pyrolysis conditions derived from rough set machine learning. Environ. Technol. 1–15. https://doi.org/10.1080/09593330.2023.2192877
  386. Tang, Q., Chen, Y., Yang, H., Liu, M., Xiao, H., Wu, Z., Chen, H., Naqvi, S.R., 2020. Prediction of Bio-oil Yield and Hydrogen Contents Based on Machine Learning Method: Effect of Biomass Compositions and Pyrolysis Conditions. Energy & Fuels 34, 11050–11060. https://doi.org/10.1021/acs.energyfuels.0c01893
  387. Tchandao, E.-W.H., Salami, A.A., Kodjo, K.M., Nabiliou, A., Ouedraogo, S., 2023. Modelling the Optimal Electricity Mix for Togo by 2050 Using OSeMOSYS. Int. J. Renew. Energy Dev. Vol 12, No 2 March 2023DO - https://doi.org/10.14710/ijred.2023.50104
  388. Tekin, K., Karagöz, S., Bektaş, S., 2014. A review of hydrothermal biomass processing. Renew. Sustain. Energy Rev. 40, 673–687. https://doi.org/10.1016/j.rser.2014.07.216
  389. Thi, T.X.N., Bui, T.M.T., Truong, L.B.T., 2024. Effects of Syngas from Various Biomass Gasification on Combustion of Spark Ignition Engine. GMSARN Int. J. 18, 123–129
  390. Tian, J., Ooka, R., Lee, D., 2023. Multi-scale solar radiation and photovoltaic power forecasting with machine learning algorithms in urban environment: A state-of-the-art review. J. Clean. Prod. 426, 139040. https://doi.org/10.1016/j.jclepro.2023.139040
  391. Torres, J.F., Galicia, A., Troncoso, A., Martínez-Álvarez, F., 2018. A scalable approach based on deep learning for big data time series forecasting. Integr. Comput. Aided. Eng. 25, 335–348. https://doi.org/10.3233/ICA-180580
  392. Tri Le, H., Chitsomboon, T., Koonsrisuk, A., 2020. Development of a Solar Water Distiller with a Receiver and Condenser. IOP Conf. Ser. Mater. Sci. Eng. 886. https://doi.org/10.1088/1757-899X/886/1/012042
  393. Trinh, V.L., Chung, C.K., 2023a. Renewable energy for SDG-7 and sustainable electrical production, integration, industrial application, and globalization: Review. Clean. Eng. Technol. 15, 100657. https://doi.org/10.1016/j.clet.2023.100657
  394. Trinh, V.L., Chung, C.K., 2023b. Renewable energy for SDG-7 and sustainable electrical production, integration, industrial application, and globalization: Review. Clean. Eng. Technol. 15, 100657. https://doi.org/10.1016/j.clet.2023.100657
  395. Truong, T.T., Nguyen, X.P., Pham, V.V., Le, V.V., Le, A.T., Bui, V.T., 2021. Effect of alcohol additives on diesel engine performance: a review. Energy Sources, Part A Recover. Util. Environ. Eff. 1–25. https://doi.org/10.1080/15567036.2021.2011490
  396. Tuan Hoang, A., Nižetić, S., Chyuan Ong, H., Tarelko, W., Viet Pham, V., Hieu Le, T., Quang Chau, M., Phuong Nguyen, X., 2021. A review on application of artificial neural network (ANN) for performance and emission characteristics of diesel engine fueled with biodiesel-based fuels. Sustain. Energy Technol. Assessments 47, 101416. https://doi.org/10.1016/j.seta.2021.101416
  397. Tuerxun, W., Chang, X., Hongyu, G., Zhijie, J., Huajian, Z., 2021. Fault Diagnosis of Wind Turbines Based on a Support Vector Machine Optimized by the Sparrow Search Algorithm. IEEE Access 9, 69307–69315. https://doi.org/10.1109/ACCESS.2021.3075547
  398. Tumenbayar, U., Ko, K., 2023. An Effect of Wind Veer on Wind Turbine Performance. Int. J. Renew. Energy Dev. Vol 12, No 1 January 2023DO - https://doi.org/10.14710/ijred.2023.47905
  399. Tweedie, L., Reynolds, J., 2016. Climate change: Implications for superannuation funds in Australia. Colon. First State Glob. Asset Manag
  400. Ullah, Z., Khan, M., Raza Naqvi, S., Farooq, W., Yang, H., Wang, S., Vo, D.-V.N., 2021. A comparative study of machine learning methods for bio-oil yield prediction – A genetic algorithm-based features selection. Bioresour. Technol. 335, 125292. https://doi.org/10.1016/j.biortech.2021.125292
  401. Umer, M., Brandoni, C., Jaffar, M., Hewitt, N.J., Dunlop, P., Zhang, K., Huang, Y., 2024. An Experimental Investigation of Hydrogen Production through Biomass Electrolysis. Processes 12, 112. https://doi.org/10.3390/pr12010112
  402. Ünal Uyar, G.F., Terzioğlu, M., Kayakuş, M., Tutcu, B., Çoşgun, A., Tonguç, G., Kaplan Yildirim, R., 2023. Estimation of Methane Gas Production in Turkey Using Machine Learning Methods. Appl. Sci. 13, 8442. https://doi.org/10.3390/app13148442
  403. UNDP, UNOHRLLS, World Bank, 2021. THEME REPORT ON ENERGY ACCESS: TOWARDS THE ACHIEVEMENT OF SDG 7 AND NET-ZERO EMISSIONS
  404. Utama, C., Meske, C., Schneider, J., Schlatmann, R., Ulbrich, C., 2023. Explainable artificial intelligence for photovoltaic fault detection: A comparison of instruments. Sol. Energy 249, 139–151. https://doi.org/10.1016/j.solener.2022.11.018
  405. Vats, G., Mathur, R., 2022. A net-zero emissions energy system in India by 2050: An exploration. J. Clean. Prod. 352, 131417. https://doi.org/https://doi.org/10.1016/j.jclepro.2022.131417
  406. Venkateswaran, D., Cho, Y., 2024. Efficient solar power generation forecasting for greenhouses: A hybrid deep learning approach. Alexandria Eng. J. 91, 222–236. https://doi.org/10.1016/j.aej.2024.02.004
  407. Vennila, C., Titus, A., Sudha, T.S., Sreenivasulu, U., Reddy, N.P.R., Jamal, K., Lakshmaiah, D., Jagadeesh, P., Belay, A., 2022. Forecasting Solar Energy Production Using Machine Learning. Int. J. Photoenergy 2022, 1–7. https://doi.org/10.1155/2022/7797488
  408. Veza, I., Afzal, A., Mujtaba, M.A., Tuan Hoang, A., Balasubramanian, D., Sekar, M., Fattah, I.M.R., Soudagar, M.E.M., EL-Seesy, A.I., Djamari, D.W., Hananto, A.L., Putra, N.R., Tamaldin, N., 2022a. Review of artificial neural networks for gasoline, diesel and homogeneous charge compression ignition engine. Alexandria Eng. J. 61, 8363–8391. https://doi.org/10.1016/j.aej.2022.01.072
  409. Veza, I., Karaoglan, A.D., Ileri, E., Kaulani, S.A., Tamaldin, N., Latiff, Z.A., Muhamad Said, M.F., Hoang, A.T., Yatish, K.V., Idris, M., 2022b. Grasshopper optimization algorithm for diesel engine fuelled with ethanol-biodiesel-diesel blends. Case Stud. Therm. Eng. 31, 101817. https://doi.org/10.1016/j.csite.2022.101817
  410. Wang, C., Xu, S., Yang, J., 2021. Adaboost Algorithm in Artificial Intelligence for Optimizing the IRI Prediction Accuracy of Asphalt Concrete Pavement. Sensors 21, 5682. https://doi.org/10.3390/s21175682
  411. Wang, H.S.-H., Yao, Y., 2023. Machine learning for sustainable development and applications of biomass and biomass-derived carbonaceous materials in water and agricultural systems: A review. Resour. Conserv. Recycl. 190, 106847. https://doi.org/10.1016/j.resconrec.2022.106847
  412. Wang, Y., Wang, D., Tang, Y., 2020. Clustered Hybrid Wind Power Prediction Model Based on ARMA, PSO-SVM, and Clustering Methods. IEEE Access 8, 17071–17079. https://doi.org/10.1109/ACCESS.2020.2968390
  413. Wasista, S., Tjandrasa, H., Djanali, S., 2023. Design an Intelligent Balanced Control of Quadruped Legs Based on Adaptive Neuro-Fuzzy Inference System (ANFIS). Int. J. Adv. Sci. Eng. Inf. Technol. 13, 901–910. https://doi.org/10.18517/ijaseit.13.3.18589
  414. Wedashwara, W., Yadnya, M.S., Sudiarta, I.W., Arimbawa, I.W.A., Mulyana, T., 2023. Solar Powered Vibration Propagation Analysis System using nRF24l01 based WSN and FRBR. JOIV Int. J. Informatics Vis. 7, 15–21. https://doi.org/10.30630/joiv.7.1.1592
  415. Wei, C., Jiang, G., Wu, G., Zhou, Y., Liu, Y., 2024. Effects on of Blended Biodiesel and Heavy Oil on Engine Combustion and Black Carbon Emissions of a Low-Speed Two-Stroke Engine. Polish Marit. Res. 31, 94–101. https://doi.org/10.2478/pomr-2024-0010
  416. Wicaksono, G.W., Nur Oktaviana, U., Noor Prasetyo, S., Intana Sari, T., Hidayah, N.P., Yunus, N.R., Al-Fatih, S., 2023. Classification of Industrial Relations Dispute Court Verdict Document with XGBoost and Bidirectional LSTM. JOIV Int. J. Informatics Vis. 7, 1041. https://doi.org/10.30630/joiv.7.3-2.2373
  417. Wu, Q., Zheng, H., Guo, X., Liu, G., 2022. Promoting wind energy for sustainable development by precise wind speed prediction based on graph neural networks. Renew. Energy 199, 977–992. https://doi.org/10.1016/j.renene.2022.09.036
  418. Ximenes, B.H., Ramalho, G.L., 2021. Concrete ethical guidelines and best practices in machine learning development, in: 2021 IEEE International Symposium on Technology and Society (ISTAS). IEEE, pp. 1–8. https://doi.org/10.1109/ISTAS52410.2021.9728979
  419. Xue, H., Jia, Y., Wen, P., Farkoush, S.G., 2020. Using of improved models of Gaussian Processes in order to Regional wind power forecasting. J. Clean. Prod. 262, 121391. https://doi.org/10.1016/j.jclepro.2020.121391
  420. Yadav, P., Athanassiadis, D., Yacout, D.M.M., Tysklind, M., Upadhyayula, V.K.K., 2020. Environmental Impact and Environmental Cost Assessment of Methanol Production from wood biomass. Environ. Pollut. 265, 114990. https://doi.org/10.1016/j.envpol.2020.114990
  421. Yadav, P.K., Bhasker, R., Upadhyay, S.K., 2021. Comparative study of ANFIS fuzzy logic and neural network scheduling based load frequency control for two-area hydro thermal system. Mater. Today Proc
  422. Yan, G., Hu, Y., Shi, Q., 2022. A Convolutional Neural Network-Based Method of Inverter Fault Diagnosis in a Ship’s DC Electrical System. Polish Marit. Res. 29, 105–114. https://doi.org/10.2478/pomr-2022-0048
  423. Yang, J.Q., Liu, H.Z., 2022. Application of EMD-Adaboost in wind speed prediction. Int. J. Data Sci. 7, 164. https://doi.org/10.1504/IJDS.2022.126854
  424. Yang, L., Nguyen, H., Bui, X.-N., Nguyen-Thoi, T., Zhou, J., Huang, J., 2021. Prediction of gas yield generated by energy recovery from municipal solid waste using deep neural network and moth-flame optimization algorithm. J. Clean. Prod. 311, 127672. https://doi.org/10.1016/j.jclepro.2021.127672
  425. Yang, Y., Brammer, J.G., Wright, D.G., Scott, J.A., Serrano, C., Bridgwater, A.V., 2017. Combined heat and power from the intermediate pyrolysis of biomass materials: performance, economics and environmental impact. Appl. Energy 191, 639–652. https://doi.org/10.1016/j.apenergy.2017.02.004
  426. Yang, Y., Shahbeik, H., Shafizadeh, A., Rafiee, S., Hafezi, A., Du, X., Pan, J., Tabatabaei, M., Aghbashlo, M., 2023. Predicting municipal solid waste gasification using machine learning: A step toward sustainable regional planning. Energy 278, 127881. https://doi.org/10.1016/j.energy.2023.127881
  427. Yaqin, A., Laksito, A.D., Fatonah, S., 2021. Evaluation of Backpropagation Neural Network Models for Early Prediction of Student’s Graduation in XYZ University. Int. J. Adv. Sci. Eng. Inf. Technol. 11, 610–617. https://doi.org/10.18517/ijaseit.11.2.11152
  428. Yeo, Q.F., Ooi, S.Y., Pang, Y.H., Gan, Y.H., 2023. Facial Skin Type Analysis Using Few-shot Learning with Prototypical Networks. Int. J. Adv. Sci. Eng. Inf. Technol. 13, 2249–2266. https://doi.org/10.18517/ijaseit.13.6.19040
  429. Yeter, B., Garbatov, Y., Guedes Soares, C., 2022. Life-extension classification of offshore wind assets using unsupervised machine learning. Reliab. Eng. Syst. Saf. 219, 108229. https://doi.org/10.1016/j.ress.2021.108229
  430. Yilmaz, N., 2012. Comparative analysis of biodiesel–ethanol–diesel and biodiesel–methanol–diesel blends in a diesel engine. Energy 40, 210–213. https://doi.org/10.1016/j.energy.2012.01.079
  431. Yoro, K.O., Daramola, M.O., Sekoai, P.T., Wilson, U.N., Eterigho-Ikelegbe, O., 2021. Update on current approaches, challenges, and prospects of modeling and simulation in renewable and sustainable energy systems. Renew. Sustain. Energy Rev. 150, 111506. https://doi.org/10.1016/j.rser.2021.111506
  432. You, H., Bai, S., Wang, R., Li, Z., Xiang, S., Huang, F., 2022. New PSO-SVM Short-Term Wind Power Forecasting Algorithm Based on the CEEMDAN Model. J. Electr. Comput. Eng. 2022, 1–9. https://doi.org/10.1155/2022/7161445
  433. Yu, M., Kubiczek, J., Ding, K., Jahanzeb, A., Iqbal, N., 2022. Revisiting SDG-7 under energy efficiency vision 2050: the role of new economic models and mass digitalization in OECD. Energy Effic. 15, 2. https://doi.org/10.1007/s12053-021-10010-z
  434. Yusuf, A.A., Ampah, J.D., Veza, I., Atabani, A.E., Hoang, A.T., Nippae, A., Powoe, M.T., Afrane, S., Yusuf, D.A., Yahuza, I., 2023. Investigating the influence of plastic waste oils and acetone blends on diesel engine combustion, pollutants, morphological and size particles: Dehalogenation and catalytic pyrolysis of plastic waste. Energy Convers. Manag. 291, 117312. https://doi.org/10.1016/j.enconman.2023.117312
  435. Yuvenda, D., Sudarmanta, B., Wahjudi, A., Hirowati, R.A., 2022. Effect of Adding Combustion Air on Emission in a Diesel Dual-Fuel Engine with Crude Palm Oil Biodiesel Compressed Natural Gas Fuels. Int. J. Renew. Energy Dev. 11, 871–877. https://doi.org/10.14710/ijred.2022.41275
  436. Zafar, M.H., Khan, N.M., Mansoor, M., Mirza, A.F., Moosavi, S.K.R., Sanfilippo, F., 2022. Adaptive ML-based technique for renewable energy system power forecasting in hybrid PV-Wind farms power conversion systems. Energy Convers. Manag. 258, 115564. https://doi.org/10.1016/j.enconman.2022.115564
  437. Zaki, E.S.G., El Saeed, S.M., Hassan, H.H., 2023. Artificial Intelligence and Machine Learning of Petroleum Wastewater Treatment by Nanofilteration Membranes, in: Wastewater Treatment. CRC Press, Boca Raton, pp. 375–384. https://doi.org/10.1201/9781003354475-12
  438. Zarra, T., Galang, M.G., Ballesteros Jr, F., Belgiorno, V., Naddeo, V., 2019. Environmental odour management by artificial neural network–A review. Environ. Int. 133, 105189
  439. Zeńczak, W., Gromadzińska, A.K., 2020. Preliminary Analysis of the Use of Solid Biofuels in a Ship’s Power System. Polish Marit. Res. 27, 67–79. https://doi.org/10.2478/pomr-2020-0067
  440. Zhang, H., Hu, Y., He, J., 2021. Wind Tunnel Experiment of Multi-Mode ARC Sail Device. Polish Marit. Res. 28, 20–29. https://doi.org/doi: 10.2478/pomr-2021-0046
  441. Zhang, L., Qiu, Y., Chen, Y., Hoang, A.T., 2023. Multi-objective particle swarm optimization applied to a solar-geothermal system for electricity and hydrogen production; Utilization of zeotropic mixtures for performance improvement. Process Saf. Environ. Prot. 175, 814–833. https://doi.org/10.1016/j.psep.2023.05.082
  442. Zhang, S., Jian, W., Zhou, J., Li, J., Yan, G., 2023. A new solar, natural gas, and biomass-driven polygeneration cycle to produce electrical power and hydrogen fuel; thermoeconomic and prediction approaches. Fuel 334, 126825. https://doi.org/10.1016/j.fuel.2022.126825
  443. Zhang, W., Chen, Q., Chen, J., Xu, D., Zhan, H., Peng, H., Pan, J., Vlaskin, M., Leng, L., Li, H., 2023. Machine learning for hydrothermal treatment of biomass: A review. Bioresour. Technol. 370, 128547. https://doi.org/10.1016/j.biortech.2022.128547
  444. Zhang, Y., Aldosky, A.J., Goyal, V., Meqdad, M.N., Nutakki, T.U.K., Alsenani, T.R., Nguyen, V.N., Dahari, M., Nguyen, P.Q.P., Ali, H.E., 2024. A machine learning study on a municipal solid waste-to-energy system for environmental sustainability in a multi-generation energy system for hydrogen production. Process Saf. Environ. Prot. 182, 1171–1184. https://doi.org/10.1016/j.psep.2023.12.054
  445. Zhang, Y., Salem, M., Elmasry, Y., Hoang, A.T., Galal, A.M., Pham Nguyen, D.K., Wae-hayee, M., 2022. Triple-objective optimization and electrochemical/technical/environmental study of biomass gasification process for a novel high-temperature fuel cell/electrolyzer/desalination scheme. Renew. Energy 201, 379–399. https://doi.org/10.1016/j.renene.2022.10.059
  446. Zhihong, C., Hebin, Z., Yanbo, W., Binyan, L., Yu, L., 2017. A vision-based robotic grasping system using deep learning for garbage sorting, in: 2017 36th Chinese Control Conference (CCC). IEEE, pp. 11223–11226. https://doi.org/10.23919/ChiCC.2017.8029147
  447. Zhu, S., Preuss, N., You, F., 2023. Advancing sustainable development goals with machine learning and optimization for wet waste biomass to renewable energy conversion. J. Clean. Prod. 422, 138606. https://doi.org/10.1016/j.jclepro.2023.138606
  448. Zou, C., Zhao, Q., Zhang, G., Xiong, B., 2016. Energy revolution: From a fossil energy era to a new energy era. Nat. Gas Ind. B 3, 1–11. https://doi.org/10.1016/j.ngib.2016.02.001

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