Harnessing a Better Future: Exploring AI and ML Applications in Renewable Energy

Tien Han Nguyen - Hanoi University of Industry, Hanoi, 100000, Vietnam
Prabhu Paramasivam - Department of Research and Innovation, Saveetha School of Engineering, SIMATS, Chennai, Tamil Nadu, 602105, India
Van Huong Dong - Institute of Mechanical Engineering, Ho Chi Minh City University of Transport, Ho Chi Minh, 700000, Vietnam
Huu Cuong Le - Institute of Maritime, Ho Chi Minh City University of Transport, Ho Chi Minh, 700000, Vietnam
Duc Chuan Nguyen - Institute of Maritime, Ho Chi Minh City University of Transport, Ho Chi Minh, 700000, Vietnam

Citation Format:

DOI: http://dx.doi.org/10.62527/joiv.8.1.2637


Integrating machine learning (ML) and artificial intelligence (AI) with renewable energy sources, including biomass, biofuels, engines, and solar power, can revolutionize the energy industry. Biomass and biofuels have benefited significantly from implementing AI and ML algorithms that optimize feedstock, enhance resource management, and facilitate biofuel production. By applying insight derived from data analysis, stakeholders can improve the entire biofuel supply chain - including biomass conversion, fuel synthesis, agricultural growth, and harvesting - to mitigate environmental impacts and accelerate the transition to a low-carbon economy. Furthermore, implementing AI and ML in combustion systems and engines has yielded substantial improvements in fuel efficiency, emissions reduction, and overall performance. Enhancing engine design and control techniques with ML algorithms produces cleaner, more efficient engines with minimal environmental impact. This contributes to the sustainability of power generation and transportation. ML algorithms are employed in solar energy to analyze vast quantities of solar data to improve photovoltaic systems' design, operation, and maintenance. The ultimate goal is to increase energy output and system efficiency. Collaboration among academia, industry, and policymakers is imperative to expedite the transition to a sustainable energy future and harness the potential of AI and ML in renewable energy. By implementing these technologies, it is possible to establish a more sustainable energy ecosystem, which would benefit future generations.


Machine learning; artificial intelligence; renewable energy; biofuel; biomass energy

Full Text:



M. Yu, J. Kubiczek, K. Ding, A. Jahanzeb, and N. Iqbal, “Revisiting SDG-7 under energy efficiency vision 2050: the role of new economic models and mass digitalization in OECD,” Energy Effic., vol. 15, no. 1, p. 2, Jan. 2022, doi: 10.1007/s12053-021-10010-z.

M. Mulligan, A. van Soesbergen, D. G. Hole, T. M. Brooks, S. Burke, and J. Hutton, “Mapping nature’s contribution to SDG 6 and implications for other SDGs at policy relevant scales,” Remote Sens. Environ., vol. 239, p. 111671, Mar. 2020, doi: 10.1016/j.rse.2020.111671.

T. T. Le, J. C. Priya, H. C. Le, N. V. L. Le, T. B. N. Nguyen, and D. N. Cao, “Harnessing artificial intelligence for data-driven energy predictive analytics: A systematic survey towards enhancing sustainability,” Int. J. Renew. Energy Dev., vol. 13, no. 2, 2024, doi: 10.61435/ijred.2024.60119.

A. L. Salvia, W. Leal Filho, L. L. Brandli, and J. S. Griebeler, “Assessing research trends related to Sustainable Development Goals: local and global issues,” J. Clean. Prod., vol. 208, pp. 841–849, 2019, doi: 10.1016/j.jclepro.2018.09.242.

M. del C. Pérez-Peña, M. Jiménez-García, J. Ruiz-Chico, and A. R. Peña-Sánchez, “Analysis of Research on the SDGs: The Relationship between Climate Change, Poverty and Inequality,” Appl. Sci., vol. 11, no. 19, p. 8947, Sep. 2021, doi: 10.3390/app11198947.

P. Thapa, B. Mainali, and S. Dhakal, “Focus on Climate Action: What Level of Synergy and Trade-Off Is There between SDG 13; Climate Action and Other SDGs in Nepal?,” Energies, vol. 16, no. 1, p. 566, Jan. 2023, doi: 10.3390/en16010566.

IEA, “Tracking SDG7: The Energy Progress Report, 2022,” 2022.

R. Feng, X. Xu, Z.-T. Yu, and Q. Lin, “A machine-learning assisted multi-cluster assessment for decarbonization in the chemical fiber industry toward net-zero: A case study in a Chinese province,” J. Clean. Prod., vol. 425, p. 138965, Nov. 2023, doi: 10.1016/j.jclepro.2023.138965.

V. G. Nguyen et al., “An extensive investigation on leveraging machine learning techniques for high-precision predictive modeling of CO 2 emission,” Energy Sources, Part A Recover. Util. Environ. Eff., vol. 45, no. 3, pp. 9149–9177, Aug. 2023, doi: 10.1080/15567036.2023.2231898.

S. L. Goldenberg, G. Nir, and S. E. Salcudean, “A new era: artificial intelligence and machine learning in prostate cancer,” Nat. Rev. Urol., vol. 16, no. 7, pp. 391–403, Jul. 2019, doi: 10.1038/s41585-019-0193-3.

C. Janiesch, P. Zschech, and K. Heinrich, “Machine learning and deep learning,” Electron. Mark., vol. 31, no. 3, pp. 685–695, Sep. 2021, doi: 10.1007/s12525-021-00475-2.

T. Ahmad, R. Madonski, D. Zhang, C. Huang, and A. Mujeeb, “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., vol. 160, p. 112128, May 2022, doi: 10.1016/j.rser.2022.112128.

N. Milojevic-Dupont and F. Creutzig, “Machine learning for geographically differentiated climate change mitigation in urban areas,” Sustain. Cities Soc., vol. 64, p. 102526, 2021.

D. Rangel-Martinez, K. D. P. Nigam, and L. A. Ricardez-Sandoval, “Machine learning on sustainable energy: A review and outlook on renewable energy systems, catalysis, smart grid and energy storage,” Chem. Eng. Res. Des., vol. 174, pp. 414–441, 2021.

P. Khandare, S. Deokar, and A. Dixit, “Improvement of Traditional Protection System in the Existing Hybrid Microgrid with Advanced Intelligent Method,” Int. J. Data Sci., vol. 1, no. 2, pp. 72–81, May 2020, doi: 10.18517/ijods.1.2.72-81.2020.

M. Sharifzadeh, A. Sikinioti-Lock, and N. Shah, “Machine-learning methods for integrated renewable power generation: A comparative study of artificial neural networks, support vector regression, and Gaussian Process Regression,” Renew. Sustain. Energy Rev., vol. 108, pp. 513–538, Jul. 2019, doi: 10.1016/j.rser.2019.03.040.

M. Pan, Q. Xing, Z. Chai, H. Zhao, Q. Sun, and D. Duan, “Real-time digital twin machine learning-based cost minimization model for renewable-based microgrids considering uncertainty,” Sol. Energy, vol. 250, pp. 355–367, Jan. 2023, doi: 10.1016/j.solener.2023.01.006.

T. A. Kurniawan et al., “Decarbonization in waste recycling industry using digitalization to promote net-zero emissions and its implications on sustainability,” J. Environ. Manage., vol. 338, p. 117765, 2023.

J. D. Sachs, G. Schmidt-Traub, M. Mazzucato, D. Messner, N. Nakicenovic, and J. Rockström, “Six transformations to achieve the sustainable development goals,” Nat. Sustain., vol. 2, no. 9, pp. 805–814, 2019.

S. Yadav, A. Samadhiya, A. Kumar, A. Majumdar, J. A. Garza-Reyes, and S. Luthra, “Achieving the sustainable development goals through net zero emissions: Innovation-driven strategies for transitioning from incremental to radical lean, green and digital technologies,” Resour. Conserv. Recycl., vol. 197, p. 107094, 2023.

IEA, “Tracking SDG7: The Energy Progress Report, 2020,” 2020.

C. W. Sadoff, E. Borgomeo, and S. Uhlenbrook, “Rethinking water for SDG 6,” Nat. Sustain., vol. 3, no. 5, pp. 346–347, May 2020, doi: 10.1038/s41893-020-0530-9.

M. Beccarello and G. Di Foggia, “Sustainable Development Goals Data-Driven Local Policy: Focus on SDG 11 and SDG 12,” Adm. Sci., vol. 12, no. 4, p. 167, Nov. 2022, doi: 10.3390/admsci12040167.

M. Munasinghe, “COVID-19 and sustainable development,” Int. J. Sustain. Dev., vol. 23, no. 1–2, pp. 1–24, 2020.

A. T. Hoang and V. D. Tran, “Experimental Analysis on the Ultrasound-based Mixing Technique Applied to Ultra-low Sulphur Diesel and Bio-oils,” Int. J. Adv. Sci. Eng. Inf. Technol., vol. 9, no. 1, p. 307, Feb. 2019, doi: 10.18517/ijaseit.9.1.7890.

A. T. Hoang and V. T. Nguyen, “Emission Characteristics of a Diesel Engine Fuelled with Preheated Vegetable Oil and Biodiesel,” Philipp. J. Sci., vol. 146, no. 4, pp. 475–482, 2017.

X. P. Nguyen, “A strategy development for optimal generating power of small wind-diesel-solar hybrid microgrid system,” in 2020 6th International Conference on Advanced Computing and Communication Systems (ICACCS), IEEE, 2020, pp. 1329–1334.

Z. Said et al., “Improving the thermal efficiency of a solar flat plate collector using MWCNT-Fe3O4/water hybrid nanofluids and ensemble machine learning,” Case Stud. Therm. Eng., vol. 40, p. 102448, Dec. 2022, doi: 10.1016/j.csite.2022.102448.

X. P. Nguyen and V. H. Dong, “A study on traction control system for solar panel on vessels,” in International Conference on Emerging Applications in Material Science and Technology, ICEAMST 2020, 2020, p. 020016. doi: 10.1063/5.0007708.

T. Sathish et al., “Waste to fuel: Synergetic effect of hybrid nanoparticle usage for the improvement of CI engine characteristics fuelled with waste fish oils,” Energy, vol. 275, p. 127397, Jul. 2023, doi: 10.1016/j.energy.2023.127397.

M. Ghodbane et al., “Thermal performance assessment of an ejector air-conditioning system with parabolic trough collector using R718 as a refrigerant: A case study in Algerian desert region,” Sustain. Energy Technol. Assessments, vol. 53, p. 102513, Oct. 2022, doi: 10.1016/j.seta.2022.102513.

T. T. Bui, H. Q. Luu, A. T. Hoang, O. Konur, T. Huu, and M. T. Pham, “A review on ignition delay times of 2,5-Dimethylfuran,” Energy Sources, Part A Recover. Util. Environ. Eff., vol. 44, no. 3, pp. 7160–7175, Sep. 2022, doi: 10.1080/15567036.2020.1860163.

A. A. Salem and I. S. Seddiek, “Techno-Economic Approach to Solar Energy Systems Onboard Marine Vehicles,” Polish Marit. Res., vol. 23, no. 3, pp. 64–71, Sep. 2016, doi: 10.1515/pomr-2016-0033.

Y. Shi and W. Luo, “Application of Solar Photovoltaic Power Generation System in Maritime Vessels and Development of Maritime Tourism,” Polish Marit. Res., vol. 25, no. s2, pp. 176–181, Aug. 2018, doi: 10.2478/pomr-2018-0090.

M. Karteris and A. M. Papadopoulos, “Legislative framework for photovoltaics in Greece: A review of the sector’s development,” Energy Policy, vol. 55, pp. 296–304, 2013.

L. De Boeck, S. Van Asch, P. De Bruecker, and A. Audenaert, “Comparison of support policies for residential photovoltaic systems in the major EU markets through investment profitability,” Renew. Energy, vol. 87, pp. 42–53, 2016.

M. Jurčević et al., “Techno-economic and environmental evaluation of photovoltaic-thermal collector design with pork fat as phase change material,” Energy, vol. 254, p. 124284, Sep. 2022, doi: 10.1016/j.energy.2022.124284.

S. F. Ahmed et al., “Perovskite solar cells: Thermal and chemical stability improvement, and economic analysis,” Mater. Today Chem., vol. 27, p. 101284, Jan. 2023, doi: 10.1016/j.mtchem.2022.101284.

N. Franzese, I. Dincer, and M. Sorrentino, “A new multigenerational solar-energy based system for electricity, heat and hydrogen production,” Appl. Therm. Eng., vol. 171, p. 115085, 2020.

T. Chitsomboon, A. Koonsrisook, A. T. Hoang, and T. H. Le, “Experimental investigation of solar energy-based water distillation using inclined metal tubes as collector and condenser,” Energy Sources, Part A Recover. Util. Environ. Eff., pp. 1–17, 2021, doi: 10.1080/15567036.2021.1966139.

S. E. Hosseini and M. A. Wahid, “Hydrogen from solar energy, a clean energy carrier from a sustainable source of energy,” Int. J. Energy Res., vol. 44, no. 6, pp. 4110–4131, 2020.

E. Ciba, P. Dymarski, and M. Grygorowicz, “Heave Plates with Holes for Floating Offshore Wind Turbines,” Polish Marit. Res., vol. 29, no. 1, pp. 26–33, Mar. 2022, doi: 10.2478/pomr-2022-0003.

H. Zhang, Y. Hu, and J. He, “Wind Tunnel Experiment of Multi-Mode ARC Sail Device,” Polish Marit. Res., vol. 28, no. 4, pp. 20–29, Jan. 2022, doi: 10.2478/pomr-2021-0046.

I. Ismail, A. H. Ismail, and G. H. N. Nur Rahayu, “Wind Energy Feasibility Study of Seven Potential Locations in Indonesia,” Int. J. Adv. Sci. Eng. Inf. Technol., vol. 10, no. 5, pp. 1970–1978, 2020, doi: 10.18517/ijaseit.10.5.10389.

W.-H. Chen et al., “Two-stage optimization of three and four straight-bladed vertical axis wind turbines (SB-VAWT) based on Taguchi approach,” e-Prime - Adv. Electr. Eng. Electron. Energy, vol. 1, p. 100025, 2021, doi: 10.1016/j.prime.2021.100025.

M. A. Hassoine, F. Lahlou, A. Addaim, and A. A. Madi, “Improved Evaluation of The Wind Power Potential of a Large Offshore Wind Farm Using Four Analytical Wake Models.,” Int. J. Renew. Energy Dev., vol. 11, no. 1, pp. 35–48, 2022.

M. M. Riaz and B. H. Khan, “Techno-Economic Analysis and Planning for the Development of Large Scale Offshore Wind Farm in India.,” Int. J. Renew. Energy Dev., vol. 10, no. 2, pp. 257–268, 2021.

V. N. Nguyen et al., “Potential of Explainable Artificial Intelligence in Advancing Renewable Energy: Challenges and Prospects,” Energy & Fuels, vol. 38, no. 3, pp. 1692–1712, Feb. 2024, doi: 10.1021/acs.energyfuels.3c04343.

A. E. Atabani et al., “Emerging potential of spent coffee ground valorization for fuel pellet production in a biorefinery,” Environ. Dev. Sustain., May 2022, doi: 10.1007/s10668-022-02361-z.

R. Aniza, W.-H. Chen, A. Pétrissans, A. T. Hoang, V. Ashokkumar, and M. Pétrissans, “A review of biowaste remediation and valorization for environmental sustainability: Artificial intelligence approach,” Environ. Pollut., vol. 324, p. 121363, May 2023, doi: 10.1016/j.envpol.2023.121363.

K.-T. Lee et al., “Energy-saving drying strategy of spent coffee grounds for co-firing fuel by adding biochar for carbon sequestration to approach net zero,” Fuel, vol. 326, p. 124984, Oct. 2022, doi: 10.1016/j.fuel.2022.124984.

A. T. Hoang, T. H. Nguyen, and H. P. Nguyen, “Scrap tire pyrolysis as a potential strategy for waste management pathway: a review,” Energy Sources, Part A Recover. Util. Environ. Eff., pp. 1–18, Mar. 2020, doi: 10.1080/15567036.2020.1745336.

J. Kandasamy and I. Gökalp, “Pyrolysis, Combustion, and Steam Gasification of Various Types of Scrap Tires for Energy Recovery,” Energy & Fuels, vol. 29, no. 1, pp. 346–354, Jan. 2015, doi: 10.1021/ef502283s.

S. Varjani et al., “Sustainable management of municipal solid waste through waste-to-energy technologies,” Bioresour. Technol., vol. 355, p. 127247, Jul. 2022, doi: 10.1016/j.biortech.2022.127247.

M. Q. Chau, A. T. Hoang, T. T. Truong, and X. P. Nguyen, “Endless story about the alarming reality of plastic waste in Vietnam,” Energy Sources, Part A Recover. Util. Environ. Eff., pp. 1–9, 2020.

M.-E. Ramazankhani, A. Mostafaeipour, H. Hosseininasab, and M.-B. Fakhrzad, “Feasibility of geothermal power assisted hydrogen production in Iran,” Int. J. Hydrogen Energy, vol. 41, no. 41, pp. 18351–18369, Nov. 2016, doi: 10.1016/j.ijhydene.2016.08.150.

L. Zhang, Y. Qiu, Y. Chen, and A. T. Hoang, “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., vol. 175, pp. 814–833, Jul. 2023, doi: 10.1016/j.psep.2023.05.082.

Z. Said et al., “Nanotechnology-integrated phase change material and nanofluids for solar applications as a potential approach for clean energy strategies: Progress, challenges, and opportunities,” J. Clean. Prod., vol. 416, p. 137736, Sep. 2023, doi: 10.1016/j.jclepro.2023.137736.

C. Wang, W. Lu, C. Xi, and X. P. Nguyen, “Research on green building energy management based on BIM and FM,” Nat. Environ. Pollut. Technol, vol. 18, no. 5, pp. 1641–1646, 2019.

A. T. Hoang et al., “Power generation characteristics of a thermoelectric modules-based power generator assisted by fishbone-shaped fins: Part II – Effects of cooling water parameters,” Energy Sources, Part A Recover. Util. Environ. Eff., vol. 43, no. 3, pp. 381–393, Feb. 2021, doi: 10.1080/15567036.2019.1624891.

T. H. Huan, A. T. Hoang⁠, and V. S. Vladimirovich, “Power generation characteristics of a thermoelectric modules-based power generator assisted by fishbone-shaped fins: Part I – effects of hot inlet gas parameters,” Energy Sources, Part A Recover. Util. Environ. Eff., vol. 43, no. 5, pp. 588–599, Feb. 2021, doi: 10.1080/15567036.2019.1630035.

V. A. Ferraz de Campos, V. B. Silva, J. S. Cardoso, P. S. Brito, C. E. Tuna, and J. L. Silveira, “A review of waste management in Brazil and Portugal: Waste-to-energy as pathway for sustainable development,” Renew. Energy, vol. 178, pp. 802–820, Nov. 2021, doi: 10.1016/j.renene.2021.06.107.

G. F. Ghesti, E. A. Silveira, M. G. Guimarães, R. B. W. Evaristo, and M. Costa, “Towards a sustainable waste-to-energy pathway to pequi biomass residues: Biochar, syngas, and biodiesel analysis,” Waste Manag., vol. 143, pp. 144–156, Apr. 2022, doi: 10.1016/j.wasman.2022.02.022.

N. Imanuella et al., “Interfacial-engineered CoTiO3-based composite for photocatalytic applications: a review,” Environ. Chem. Lett., vol. 20, no. 5, pp. 3039–3069, Oct. 2022, doi: 10.1007/s10311-022-01472-3.

Z. Xu, S. Zhai, and N. X. Phuong, “Research on green transition development of energy enterprises taking mining industry as an example,” Nat. Environ. Pollut. Technol, vol. 18, no. 5, pp. 1512–1526, 2019.

W. Chen et al., “Effects of material doping on the performance of thermoelectric generator with / without equal segments,” Appl. Energy, vol. 350, p. 121709, 2023, doi: 10.1016/j.apenergy.2023.121709.

S. D. Ahmed, F. S. M. Al-Ismail, M. Shafiullah, F. A. Al-Sulaiman, and I. M. El-Amin, “Grid Integration Challenges of Wind Energy: A Review,” IEEE Access, vol. 8, pp. 10857–10878, 2020, doi: 10.1109/ACCESS.2020.2964896.

Y. Seo, S. yeob Lee, J. Kim, C. Huh, and D. Chang, “Determination of optimal volume of temporary storage tanks in a ship-based carbon capture and storage (CCS) chain using life cycle cost (LCC) including unavailability cost,” Int. J. Greenh. Gas Control, 2017, doi: 10.1016/j.ijggc.2017.06.017.

X. P. Nguyen and A. T. Hoang, “The Flywheel Energy Storage System: An Effective Solution to Accumulate Renewable Energy,” in 2020 6th International Conference on Advanced Computing and Communication Systems (ICACCS), IEEE, Mar. 2020, pp. 1322–1328. doi: 10.1109/ICACCS48705.2020.9074469.

T. Kousksou, P. Bruel, A. Jamil, T. El Rhafiki, and Y. Zeraouli, “Energy storage: Applications and challenges,” Sol. Energy Mater. Sol. Cells, vol. 120, pp. 59–80, Jan. 2014, doi: 10.1016/j.solmat.2013.08.015.

M. Jurčević, S. Nižetić, D. Čoko, A. T. Hoang, and A. M. Papadopoulos, “Experimental investigation of novel hybrid phase change materials,” Clean Technol. Environ. Policy, vol. 24, no. 1, pp. 201–212, Jan. 2022, doi: 10.1007/s10098-021-02106-y.

R. Senthil, B. M. S. Punniakodi, D. Balasubramanian, X. P. Nguyen, A. T. Hoang, and V. N. Nguyen, “Numerical investigation on melting and energy storage density enhancement of phase change material in a horizontal cylindrical container,” Int. J. Energy Res., vol. 46, no. 13, pp. 19138–19158, 2022, doi: 10.1002/er.8228.

Y. Liu et al., “Improvement of cooling of a high heat flux CPU by employing a cooper foam and NEPCM/water suspension,” J. Energy Storage, vol. 55, p. 105682, Nov. 2022, doi: 10.1016/j.est.2022.105682.

K. A. Khan, M. M. Quamar, F. H. Al-Qahtani, M. Asif, M. Alqahtani, and M. Khalid, “Smart grid infrastructure and renewable energy deployment: A conceptual review of Saudi Arabia,” Energy Strateg. Rev., vol. 50, p. 101247, Nov. 2023, doi: 10.1016/j.esr.2023.101247.

R. Sinnott, M. Bayer, D. Houghton, D. Berry, and M. Ferrier, “Development of a Grid Infrastructure for Functional Genomics,” in Lecture Notes in Computer Science, A. Konagaya and K. Satou, Eds., Springer, Berlin, Heidelberg, 2005, pp. 125–139. doi: 10.1007/978-3-540-32251-1_12.

H. Wang, Z. Lei, X. Zhang, B. Zhou, and J. Peng, “A review of deep learning for renewable energy forecasting,” Energy Convers. Manag., vol. 198, p. 111799, Oct. 2019, doi: 10.1016/j.enconman.2019.111799.

C. Sweeney, R. J. Bessa, J. Browell, and P. Pinson, “The future of forecasting for renewable energy,” WIREs Energy Environ., vol. 9, no. 2, Mar. 2020, doi: 10.1002/wene.365.

R. Ghasempour, M. A. Nazari, M. Ebrahimi, M. H. Ahmadi, and H. Hadiyanto, “Multi-Criteria Decision Making (MCDM) Approach for Selecting Solar Plants Site and Technology: A Review.,” Int. J. Renew. Energy Dev., vol. 8, no. 1, 2019.

S. E. Hosseini, “Transition away from fossil fuels toward renewables: lessons from Russia-Ukraine crisis,” Futur. Energy, vol. 1, no. 1, pp. 2–5, May 2022, doi: 10.55670/fpll.fuen.1.1.8.

Enerdata, “Global Energy Transition Statistics: Share of renewables in electricity production,” Enerdata.

Y. Chang, Y. Wei, J. Zhang, X. Xu, L. Zhang, and Y. Zhao, “Mitigating the greenhouse gas emissions from urban roadway lighting in China via energy-efficient luminaire adoption and renewable energy utilization,” Resour. Conserv. Recycl., vol. 164, p. 105197, Jan. 2021, doi: 10.1016/j.resconrec.2020.105197.

S. Nižetić, M. Arıcı, and A. T. Hoang, “Smart and Sustainable Technologies in energy transition,” J. Clean. Prod., vol. 389, p. 135944, Feb. 2023, doi: 10.1016/j.jclepro.2023.135944.

P. A. Owusu and S. Asumadu-Sarkodie, “A review of renewable energy sources, sustainability issues and climate change mitigation,” Cogent Eng., vol. 3, no. 1, p. 1167990, Dec. 2016, doi: 10.1080/23311916.2016.1167990.

W. Chen, M. Alharthi, J. Zhang, and I. Khan, “The need for energy efficiency and economic prosperity in a sustainable environment,” Gondwana Res., vol. 127, pp. 22–35, 2024.

R. Kümmel, D. Lindenberger, and F. Weiser, “The economic power of energy and the need to integrate it with energy policy,” Energy Policy, vol. 86, pp. 833–843, 2015.

A. . Hoang, Q. . Tran, and X. . Pham, “Performance and emission characteristics of popular 4-stroke motorcycle engine in Vietnam fuelled with biogasoline compared with fossil gasoline,” Int. J. Mech. Mechatronics Eng., vol. 18, no. 2, pp. 97–103, 2018.

M. Q. Chau, V. V. Le, A. T. Hoang, A. R. M. S. Al-Tawaha, and V. V. Pham, “A simulation research of heat transfers and chemical reactions in the fuel steam reformer using exhaust gas energy from motorcycle engine,” J. Mech. Eng. Res. Dev., vol. 43, no. 5, pp. 89–102, 2020.

L. Changxiong, Y. Hu, Z. Yang, and H. Guo, “Experimental Study of Fuel Combustion and Emission Characteristics of Marine Diesel Engines Using Advanced Fuels,” Polish Marit. Res., vol. 30, no. 3, pp. 48–58, Sep. 2023, doi: 10.2478/pomr-2023-0038.

Y. Li, B. Li, F. Deng, Q. Yang, and B. Zhang, “Research on the Application of Cold Energy of Largescale Lng-Powered Container Ships to Refrigerated Containers,” Polish Marit. Res., vol. 28, no. 4, pp. 107–121, Jan. 2022, doi: 10.2478/pomr-2021-0053.

T. P. Ogundunmade and A. A. Adepoju, “Modelling Liquified Petroleum Gas Prices in Nigeria Using Machine Learning Models,” Int. J. Data Sci., vol. 3, no. 2, pp. 93–100, Dec. 2022, doi: 10.18517/ijods.3.2.93-100.2022.

A. N. Olimat, “Study of fabricated solar dryer of tomato slices under Jordan climate condition,” Int. J. Renew. Energy Dev., vol. 6, no. 2, pp. 93–101, 2017, doi: 10.14710/ijred.6.2.93-101.

S. Suherman, H. Widuri, S. Patricia, E. E. Susanto, and R. J. Sutrisna, “Energy analysis of a hybrid solar dryer for drying coffee beans,” Int. J. Renew. Energy Dev., vol. 9, no. 1, pp. 131–139, 2020, doi: 10.14710/ijred.9.1.131-139.

N. T. Hung, “Remittance, Renewable Energy, and CO2 Emissions: a Vietnamese Illustration,” J. Knowl. Econ., Mar. 2023, doi: 10.1007/s13132-023-01238-4.

M. Amir and S. Z. Khan, “Assessment of renewable energy: Status, challenges, COVID-19 impacts, opportunities, and sustainable energy solutions in Africa,” Energy Built Environ., vol. 3, no. 3, pp. 348–362, Jul. 2022, doi: 10.1016/j.enbenv.2021.03.002.

L. Chen et al., “Strategies to achieve a carbon neutral society: a review,” Environ. Chem. Lett., vol. 20, no. 4, pp. 2277–2310, Aug. 2022, doi: 10.1007/s10311-022-01435-8.

F. Wang et al., “Technologies and perspectives for achieving carbon neutrality,” Innov., vol. 2, no. 4, p. 100180, Nov. 2021, doi: 10.1016/j.xinn.2021.100180.

Y. Xin and D. Long, “Linking eco-label knowledge and sustainable consumption of renewable energy: A roadmap towards green revolution,” Renew. Energy, vol. 207, pp. 531–538, May 2023, doi: 10.1016/j.renene.2023.02.102.

A. Franco and P. Salza, “Strategies for optimal penetration of intermittent renewables in complex energy systems based on techno-operational objectives,” Renew. Energy, vol. 36, no. 2, pp. 743–753, Feb. 2011, doi: 10.1016/J.RENENE.2010.07.022.

R. Syahyadi, N. Safitri, R. Widia, Safriadi, and Azwar, “Building Integrated Photovoltaic (BIPV): Implementing Artificial Intelligent (AI) on Designing Rooftile Photovoltaic,” Int. J. Data Sci., vol. 4, no. 1, pp. 60–66, May 2023, doi: 10.18517/ijods.4.1.60-66.2023.

M. Samimi and H. Moghadam, “Investigation of structural parameters for inclined weir-type solar stills,” Renew. Sustain. Energy Rev., vol. 190, p. 113969, Feb. 2024, doi: 10.1016/j.rser.2023.113969.

G. K. Karayel and I. Dincer, “Green hydrogen production potential of Canada with solar energy,” Renew. Energy, vol. 221, p. 119766, Feb. 2024, doi: 10.1016/j.renene.2023.119766.

Z. Xuan, M. Ge, C. Zhao, Y. Li, S. Wang, and Y. Zhao, “Effect of nonuniform solar radiation on the performance of solar thermoelectric generators,” Energy, vol. 290, p. 130249, Mar. 2024, doi: 10.1016/j.energy.2024.130249.

X. Li et al., “Dimensional diversity (0D, 1D, 2D, and 3D) in perovskite solar cells: exploring the potential of mixed-dimensional integrations,” J. Mater. Chem. A, vol. 12, no. 8, pp. 4421–4440, 2024, doi: 10.1039/D3TA06953B.

P. Sharma and A. K. Sharma, “AI-Based Prognostic Modeling and Performance Optimization of CI Engine Using Biodiesel-Diesel Blends,” Int. J. Renew. Energy Res., no. v11i2, 2021, doi: 10.20508/ijrer.v11i2.11854.g8191.

P. Sharma, M. Sivaramakrishnaiah, B. Deepanraj, R. Saravanan, and M. V. Reddy, “A novel optimization approach for biohydrogen production using algal biomass,” Int. J. Hydrogen Energy, vol. 52, pp. 94–103, Jan. 2024, doi: 10.1016/j.ijhydene.2022.09.274.

V. G. Nguyen et al., “Machine learning for the management of biochar yield and properties of biomass sources for sustainable energy,” Biofuels, Bioprod. Biorefining, Feb. 2024, doi: 10.1002/bbb.2596.

M. J. Deka et al., “Enhancing the performance of a photovoltaic thermal system with phase change materials: Predictive modelling and evaluation using neural networks,” Renew. Energy, vol. 224, p. 120091, Apr. 2024, doi: 10.1016/j.renene.2024.120091.

A. T. Hoang and D. C. Nguyen, “Properties of DMF-fossil gasoline RON95 blends in the consideration as the alternative fuel,” Int. J. Adv. Sci. Eng. Inf. Technol., vol. 8, no. 6, pp. 2555–2560, 2018.

A. T. Hoang and M. T. Pham, “Influences of heating temperatures on physical properties, spray characteristics of bio-oils and fuel supply system of a conventional diesel engine,” Int. J. Adv. Sci. Eng. Inf. Technol., vol. 8, no. 5, pp. 2231–2240, 2018, doi: 10.18517/ijaseit.8.5.5487.

K. Rudzki, P. Gomulka, and A. T. Hoang, “Optimization Model to Manage Ship Fuel Consumption and Navigation Time,” Polish Marit. Res., vol. 29, no. 3, pp. 141–153, Sep. 2022, doi: 10.2478/pomr-2022-0034.

A. Zakaria, F. B. Ismail, M. S. H. Lipu, and M. A. Hannan, “Uncertainty models for stochastic optimization in renewable energy applications,” Renew. Energy, vol. 145, pp. 1543–1571, 2020.

A. Palzer and H.-M. Henning, “A comprehensive model for the German electricity and heat sector in a future energy system with a dominant contribution from renewable energy technologies–Part II: Results,” Renew. Sustain. Energy Rev., vol. 30, pp. 1019–1034, 2014.

A. Q. Al-Shetwi, “Sustainable development of renewable energy integrated power sector: Trends, environmental impacts, and recent challenges,” Sci. Total Environ., vol. 822, p. 153645, May 2022, doi: 10.1016/j.scitotenv.2022.153645.

A. Sajadi, L. Strezoski, V. Strezoski, M. Prica, and K. A. Loparo, “Integration of renewable energy systems and challenges for dynamics, control, and automation of electrical power systems,” Wiley Interdiscip. Rev. Energy Environ., vol. 8, no. 1, p. e321, 2019.

T. Ahmad et al., “Artificial intelligence in sustainable energy industry: Status Quo, challenges and opportunities,” J. Clean. Prod., vol. 289, no. January, 2021, doi: 10.1016/j.jclepro.2021.125834.

Z. Fan, Z. Yan, and S. Wen, “Deep learning and artificial intelligence in sustainability: a review of SDGs, renewable energy, and environmental health,” Sustainability, vol. 15, no. 18, p. 13493, 2023.

P. W. Khan, Y.-C. Byun, S.-J. Lee, D.-H. Kang, J.-Y. Kang, and H.-S. Park, “Machine Learning-Based Approach to Predict Energy Consumption of Renewable and Nonrenewable Power Sources,” Energies, vol. 13, no. 18, p. 4870, Sep. 2020, doi: 10.3390/en13184870.

S. E. Haupt et al., “Combining artificial intelligence with physics-based methods for probabilistic renewable energy forecasting,” Energies, vol. 13, no. 8, p. 1979, 2020.

K. S. Perera, Z. Aung, and W. L. Woon, “Machine Learning Techniques for Supporting Renewable Energy Generation and Integration: A Survey,” 2014, pp. 81–96. doi: 10.1007/978-3-319-13290-7_7.

K. Kumar, R. Rao, O. Kaiwartya, S. Kaiser, and P. Sanjeevikumar, Sustainable Developments by Artificial Intelligence and Machine Learning for Renewable Energies. Elsevier, 2022. doi: 10.1016/C2020-0-04074-0.

S. Chandrasekaran, “Feasibility study on machine‐learning‐based hybrid renewable energy applications for engineering education,” Comput. Appl. Eng. Educ., vol. 29, no. 2, pp. 465–473, Mar. 2021, doi: 10.1002/cae.22237.

X. Fu, X. Wu, C. Zhang, S. Fan, and N. Liu, “Planning of distributed renewable energy systems under uncertainty based on statistical machine learning,” Prot. Control Mod. Power Syst., vol. 7, no. 1, p. 41, Dec. 2022, doi: 10.1186/s41601-022-00262-x.

A. Al-Dahoud, M. Fezari, and A. Aldahoud, “Machine Learning in Renewable Energy Application: Intelligence System for Solar Panel Cleaning,” WSEAS Trans. Environ. Dev., vol. 19, pp. 472–478, May 2023, doi: 10.37394/232015.2023.19.45.

M. S. S. Danish, “AI in Energy: Overcoming Unforeseen Obstacles,” AI, vol. 4, no. 2, pp. 406–425, May 2023, doi: 10.3390/ai4020022.

N. Salleh, S. S. Yuhaniz, and N. F. Mohd Azmi, “Modeling Orbital Propagation Using Regression Technique and Artificial Neural Network,” Int. J. Adv. Sci. Eng. Inf. Technol., vol. 12, no. 3, p. 1279, May 2022, doi: 10.18517/ijaseit.12.3.15366.

A. Kurniawan and E. Shintaku, “Estimation of Hourly Solar Radiations on Horizontal Surface from Daily Average Solar Radiations Using Artificial Neural Network,” Int. J. Adv. Sci. Eng. Inf. Technol., vol. 12, no. 6, pp. 2336–2341, Dec. 2022, doi: 10.18517/ijaseit.12.6.12940.

E. H. Flaieh, F. O. Hamdoon, and A. A. Jaber, “Estimation the Natural Frequencies of a Cracked Shaft Based on Finite Element Modeling and Artificial Neural Network,” Int. J. Adv. Sci. Eng. Inf. Technol., vol. 10, no. 4, pp. 1410–1416, Aug. 2020, doi: 10.18517/ijaseit.10.4.12211.

R. Concepcion II, E. Dadios, A. Bandala, J. Cuello, and Y. Kodama, “Hybrid Genetic Programming and Multiverse-based Optimization of Pre-Harvest Growth Factors of Aquaponic Lettuce Based on Chlorophyll Concentration,” Int. J. Adv. Sci. Eng. Inf. Technol., vol. 11, no. 6, p. 2128, Dec. 2021, doi: 10.18517/ijaseit.11.6.14991.

A. Bustamam, D. Sarwinda, B. Abdillah, and T. P. Kaloka, “Detecting Lesion Characteristics of Diabetic Retinopathy Using Machine Learning and Computer Vision,” Int. J. Adv. Sci. Eng. Inf. Technol., vol. 10, no. 4, p. 1367, Aug. 2020, doi: 10.18517/ijaseit.10.4.8876.

R. I. Perwira, M. Y. Florestiyanto, I. R. Nurjanah, - Heriyanto, and D. B. Prasetyo, “Implementation of Gabor Wavelet and Support Vector Machine for Braille Recognition,” Int. J. Adv. Sci. Eng. Inf. Technol., vol. 12, no. 4, p. 1449, Aug. 2022, doi: 10.18517/ijaseit.12.4.14445.

J. Capote-Leiva, M. Villota-Rivillas, and J. Muñoz-OrdÃ3ñez, “Access Control System based on Voice and Facial Recognition Using Artificial Intelligence,” Int. J. Adv. Sci. Eng. Inf. Technol., vol. 12, no. 6, pp. 2342–2348, Dec. 2022, doi: 10.18517/ijaseit.12.6.16049.

Sumarsono, F. A. N. Farida Afiatna, and N. Muflihah, “The Monitoring System of Soil PH Factor Using IoT-Webserver-Android and Machine Learning: A Case Study,” Int. J. Adv. Sci. Eng. Inf. Technol., vol. 14, no. 1, pp. 118–130, Feb. 2024, doi: 10.18517/ijaseit.14.1.18745.

R. Fredyan, M. R. N. Majiid, and G. P. Kusuma, “Spatiotemporal Analysis for Rainfall Prediction Using Extreme Learning Machine Cluster,” Int. J. Adv. Sci. Eng. Inf. Technol., vol. 13, no. 6, pp. 2240–2248, Dec. 2023, doi: 10.18517/ijaseit.13.6.18214.

Desniorita, N. Nazir, R. Youfa, M. T. E. Prasada, and E. Pelita, “Sustainable Biorefinery: Effect of Time Fermentation on Hidrolisis Product from Cocoa Pod Husk,” Int. J. Adv. Sci. Eng. Inf. Technol., vol. 14, no. 1, pp. 151–156, Feb. 2024, doi: 10.18517/ijaseit.14.1.18401.

M. M. Forootan, I. Larki, R. Zahedi, and A. Ahmadi, “Machine Learning and Deep Learning in Energy Systems: A Review,” Sustainability, vol. 14, no. 8, p. 4832, Apr. 2022, doi: 10.3390/su14084832.

T. V. Ramachandra, R. K. Jha, S. V. Krishna, and B. V. Shruthi, “Solar energy decision support system,” Int. J. Sustain. Energy, vol. 24, no. 4, pp. 207–224, Dec. 2005, doi: 10.1080/14786450500292105.

A. S. Bin Mohd Shah, H. Yokoyama, and N. Kakimoto, “High-Precision Forecasting Model of Solar Irradiance Based on Grid Point Value Data Analysis for an Efficient Photovoltaic System,” IEEE Trans. Sustain. Energy, vol. 6, no. 2, pp. 474–481, Apr. 2015, doi: 10.1109/TSTE.2014.2383398.

Z. Şen, “Solar energy in progress and future research trends,” Prog. Energy Combust. Sci., vol. 30, no. 4, pp. 367–416, Jan. 2004, doi: 10.1016/j.pecs.2004.02.004.

N. I. Ilham, M. Z. Hussin, N. Y. Dahlan, and E. A. Setiawan, “Prospects and Challenges of Malaysia’s Distributed Energy Resources in Business Models Towards Zero – Carbon Emission and Energy Security,” Int. J. Renew. Energy Dev., vol. 11, no. 4, pp. 1089–1100, Nov. 2022, doi: 10.14710/ijred.2022.45662.

B. Wattana and P. Aungyut, “Impacts of Solar Electricity Generation on the Thai Electricity Industry,” Int. J. Renew. Energy Dev., vol. 11, no. 1, pp. 157–163, Feb. 2022, doi: 10.14710/ijred.2022.41059.

E. Kabir, P. Kumar, S. Kumar, A. A. Adelodun, and K.-H. Kim, “Solar energy: Potential and future prospects,” Renew. Sustain. Energy Rev., vol. 82, pp. 894–900, Feb. 2018, doi: 10.1016/j.rser.2017.09.094.

T. H. Le, M. T. Pham, H. Hadiyanto, V. V. Pham, and A. T. Hoang, “Influence of Various Basin Types on Performance of Passive Solar Still: A Review,” Int. J. Renew. Energy Dev., vol. 10, no. 4, pp. 789–802, Nov. 2021, doi: 10.14710/ijred.2021.38394.

J. Liu, R. Song, S. Nasreen, and A. T. Hoang, “Analysis of the complementary property of solar energy and thermal power based on coupling model,” Nat. Environ. Pollut. Technol., vol. 18, no. 5, pp. 1675–1681, 2019.

F. Hyder, K. Sudhakar, and R. Mamat, “Solar PV tree design: A review,” Renew. Sustain. Energy Rev., vol. 82, pp. 1079–1096, Feb. 2018, doi: 10.1016/j.rser.2017.09.025.

S. Hayat, A. Safi, S. Wahab, K. Shahzad, and Y. Chen, “Renewable energy R&D and natural resources: A success story of environmentally friendly financing in OECD economies,” Resour. Policy, vol. 83, p. 103655, Jun. 2023, doi: 10.1016/j.resourpol.2023.103655.

M. Ahmad, M. A. Khan, M. Zafar, and S. Sultana, “Environment-friendly Renewable Energy from Sesame Biodiesel,” Energy Sources, Part A Recover. Util. Environ. Eff., vol. 32, no. 2, pp. 189–196, Nov. 2009, doi: 10.1080/15567030802467480.

A. Y. Kian and S. C. Lim, “On the Potential of Solar Energy for Chemical and Metal Manufacturing Plants in Malaysia,” Int. J. Adv. Sci. Eng. Inf. Technol., vol. 13, no. 5, pp. 1898–1904, 2023, doi: 10.18517/ijaseit.13.5.19052.

N. S. Asefa, “Computer Programming to Estimate the Global Daily and Hourly solar Radiation of any location around the Globe,” Int. J. Data Sci., vol. 3, no. 2, pp. 101–106, May 2023, doi: 10.18517/ijods.3.2.101-106.2022.

S. Sargunanathan, A. Elango, and S. T. Mohideen, “Performance enhancement of solar photovoltaic cells using effective cooling methods: A review,” Renew. Sustain. Energy Rev., vol. 64, pp. 382–393, Oct. 2016, doi: 10.1016/j.rser.2016.06.024.

N. Rathore, N. L. Panwar, F. Yettou, and A. Gama, “A comprehensive review of different types of solar photovoltaic cells and their applications,” Int. J. Ambient Energy, vol. 42, no. 10, pp. 1200–1217, Jul. 2021, doi: 10.1080/01430750.2019.1592774.

K. Obaideen et al., “Solar Energy: Applications, Trends Analysis, Bibliometric Analysis and Research Contribution to Sustainable Development Goals (SDGs),” Sustainability, vol. 15, no. 2, p. 1418, Jan. 2023, doi: 10.3390/su15021418.

A. Gopi, P. Sharma, K. Sudhakar, W. K. Ngui, I. Kirpichnikova, and E. Cuce, “Weather Impact on Solar Farm Performance: A Comparative Analysis of Machine Learning Techniques,” Sustainability, vol. 15, no. 1, p. 439, Dec. 2022, doi: 10.3390/su15010439.

A. Shirole, M. Wagh, and V. Kulkarni, “Thermal Performance Comparison of Parabolic Trough Collector (PTC) Using Various Nanofluids,” Int. J. Renew. Energy Dev., vol. 10, no. 4, pp. 875–889, Nov. 2021, doi: 10.14710/ijred.2021.33801.

A. M. Ghaithan, A. Al-Hanbali, A. Mohammed, A. M. Attia, H. Saleh, and O. Alsawafy, “Optimization of a solar-wind- grid powered desalination system in Saudi Arabia,” Renew. Energy, vol. 178, pp. 295–306, Nov. 2021, doi: 10.1016/j.renene.2021.06.060.

A. Behzadi and S. Sadrizadeh, “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., vol. 277, p. 116590, Feb. 2023, doi: 10.1016/j.enconman.2022.116590.

Z. Yao et al., “Machine learning for a sustainable energy future,” Nat. Rev. Mater., vol. 8, no. 3, pp. 202–215, Oct. 2022, doi: 10.1038/s41578-022-00490-5.

T. Cheng, X. Zhu, F. Yang, and W. Wang, “Machine learning enabled learning based optimization algorithm in digital twin simulator for management of smart islanded solar-based microgrids,” Sol. Energy, vol. 250, pp. 241–247, Jan. 2023, doi: 10.1016/j.solener.2022.12.040.

A. S. Al-Buraiki and A. Al-Sharafi, “Technoeconomic analysis and optimization of hybrid solar/wind/battery systems for a standalone house integrated with electric vehicle in Saudi Arabia,” Energy Convers. Manag., vol. 250, p. 114899, Dec. 2021, doi: 10.1016/j.enconman.2021.114899.

A. Singh, P. Baredar, and B. Gupta, “Computational Simulation & Optimization of a Solar, Fuel Cell and Biomass Hybrid Energy System Using HOMER Pro Software,” Procedia Eng., vol. 127, pp. 743–750, 2015, doi: 10.1016/j.proeng.2015.11.408.

S. Jafari, S. Hoseinzadeh, and A. Sohani, “Deep Q-Value Neural Network (DQN) Reinforcement Learning for the Techno-Economic Optimization of a Solar-Driven Nanofluid-Assisted Desalination Technology,” Water, vol. 14, no. 14, p. 2254, Jul. 2022, doi: 10.3390/w14142254.

H. Adun et al., “Estimation of thermophysical property of hybrid nanofluids for solar Thermal applications: Implementation of novel Optimizable Gaussian Process regression (O-GPR) approach for Viscosity prediction,” Neural Comput. Appl., pp. 1–22, 2022, doi: 10.1007/s00521-022-07038-2.

E. M. Al-Ali et al., “Solar Energy Production Forecasting Based on a Hybrid CNN-LSTM-Transformer Model,” Mathematics, vol. 11, no. 3, p. 676, Jan. 2023, doi: 10.3390/math11030676.

P. Vengatesh Ramamurthi and E. Rajan Samuel Nadar, “IoT-based Energy Monitoring and Controlling of an Optimum Inclination Angle of the Solar Panels,” IETE J. Res., vol. 68, no. 4, pp. 3108–3118, Jul. 2022, doi: 10.1080/03772063.2020.1754301.

I. Segovia Ramírez, A. Pliego Marugán, and F. P. García Márquez, “A novel approach to optimize the positioning and measurement parameters in photovoltaic aerial inspections,” Renew. Energy, vol. 187, pp. 371–389, Mar. 2022, doi: 10.1016/j.renene.2022.01.071.

Y. Wang, Z. Rao, J. Liu, and S. Liao, “An optimized control strategy for integrated solar and air-source heat pump water heating system with cascade storage tanks,” Energy Build., vol. 210, p. 109766, Mar. 2020, doi: 10.1016/j.enbuild.2020.109766.

Z. Said, P. Sharma, L. Syam Sundar, V. G. Nguyen, V. D. Tran, and V. V. Le, “Using Bayesian optimization and ensemble boosted regression trees for optimizing thermal performance of solar flat plate collector under thermosyphon condition employing MWCNT-Fe3O4/water hybrid nanofluids,” Sustain. Energy Technol. Assessments, vol. 53, p. 102708, Oct. 2022, doi: 10.1016/j.seta.2022.102708.

W. Quitiaquez, J. Estupinán-Campos, C. Nieto-Londoño, C. A. Isaza-Roldán, P. Quitiaquez, and F. Toapanta-Ramos, “CFD Analysis of Heat Transfer Enhancement in a Flat-Plate Solar Collector with Different Geometric Variations in the Superficial Section,” Int. J. Adv. Sci. Eng. Inf. Technol., vol. 11, no. 5, p. 2039, Oct. 2021, doi: 10.18517/ijaseit.11.5.15288.

A. Damayanti, F. Arifianto, and T. L. Indra, “Development Area for Floating Solar Panel and Dam in The Former Mine Hole (Void) Samarinda City, East Kalimantan Province,” Int. J. Adv. Sci. Eng. Inf. Technol., vol. 11, no. 5, p. 1713, Oct. 2021, doi: 10.18517/ijaseit.11.5.14097.

M. S. Ibrahim, W. Dong, and Q. Yang, “Machine learning driven smart electric power systems: Current trends and new perspectives,” Appl. Energy, vol. 272, p. 115237, Aug. 2020, doi: 10.1016/j.apenergy.2020.115237.

C. Zhang, Y. Zhang, J. Pu, Z. Liu, Z. Wang, and L. Wang, “An hourly solar radiation prediction model using eXtreme gradient boosting algorithm with the effect of fog-haze,” Energy Built Environ., Aug. 2023, doi: 10.1016/j.enbenv.2023.08.001.

S. Kallio and M. Siroux, “Photovoltaic power prediction for solar micro-grid optimal control,” Energy Reports, vol. 9, pp. 594–601, Mar. 2023, doi: 10.1016/j.egyr.2022.11.081.

D. Chakraborty, J. Mondal, H. B. Barua, and A. Bhattacharjee, “Computational solar energy – Ensemble learning methods for prediction of solar power generation based on meteorological parameters in Eastern India,” Renew. Energy Focus, vol. 44, pp. 277–294, Mar. 2023, doi: 10.1016/j.ref.2023.01.006.

N. V. Sridharan and V. Sugumaran, “Visual fault detection in photovoltaic modules using decision tree algorithms with deep learning features,” Energy Sources, Part A Recover. Util. Environ. Eff., pp. 1–17, Dec. 2021, doi: 10.1080/15567036.2021.2020379.

M. M. Najafabadi, F. Villanustre, T. M. Khoshgoftaar, N. Seliya, R. Wald, and E. Muharemagic, “Deep learning applications and challenges in big data analytics,” J. Big Data, vol. 2, no. 1, p. 1, Dec. 2015, doi: 10.1186/s40537-014-0007-7.

S. Paliwal, A. Sharma, S. Jain, and S. Sharma, “Machine learning and deep learning in bioinformatics,” in Bioinformatics and Computational Biology, Chapman and Hall/CRC, 2024, pp. 63–74.

Y. Zhou, Y. Li, D. Wang, and Y. Liu, “A multi-step ahead global solar radiation prediction method using an attention-based transformer model with an interpretable mechanism,” Int. J. Hydrogen Energy, vol. 48, no. 40, pp. 15317–15330, May 2023, doi: 10.1016/j.ijhydene.2023.01.068.

V. Nikolaeva and E. Gordeev, “SPAM: Solar Spectrum Prediction for Applications and Modeling,” Atmosphere (Basel)., vol. 14, no. 2, p. 226, Jan. 2023, doi: 10.3390/atmos14020226.

M. Gao et al., “Temperature prediction of solar greenhouse based on NARX regression neural network,” Sci. Rep., vol. 13, no. 1, p. 1563, Jan. 2023, doi: 10.1038/s41598-022-24072-1.

W. Chen, W. Zou, K. Zhong, and A. Aliyeva, “Machine learning assessment under the development of green technology innovation: A perspective of energy transition,” Renew. Energy, vol. 214, pp. 65–73, Sep. 2023, doi: 10.1016/j.renene.2023.05.108.

Y. Gao, S. Miyata, Y. Matsunami, and Y. Akashi, “Spatio-temporal interpretable neural network for solar irradiation prediction using transformer,” Energy Build., vol. 297, p. 113461, Oct. 2023, doi: 10.1016/j.enbuild.2023.113461.

D. El Bourakadi, H. Ramadan, A. Yahyaouy, and J. Boumhidi, “A novel solar power prediction model based on stacked BiLSTM deep learning and improved extreme learning machine,” Int. J. Inf. Technol., vol. 15, no. 2, pp. 587–594, Feb. 2023, doi: 10.1007/s41870-022-01118-1.

Ü. Ağbulut, A. E. Gürel, and Y. Biçen, “Prediction of daily global solar radiation using different machine learning algorithms: Evaluation and comparison,” Renew. Sustain. Energy Rev., vol. 135, p. 110114, Jan. 2021, doi: 10.1016/j.rser.2020.110114.

Y. Feng, W. Hao, H. Li, N. Cui, D. Gong, and L. Gao, “Machine learning models to quantify and map daily global solar radiation and photovoltaic power,” Renew. Sustain. Energy Rev., vol. 118, p. 109393, Feb. 2020, doi: 10.1016/j.rser.2019.109393.

M. Taki, A. Rohani, and H. Yildizhan, “Application of machine learning for solar radiation modeling,” Theor. Appl. Climatol., vol. 143, no. 3–4, pp. 1599–1613, Feb. 2021, doi: 10.1007/s00704-020-03484-x.

I. Jebli, F. Z. Belouadha, M. I. Kabbaj, and A. Tilioua, “Prediction of solar energy guided by pearson correlation using machine learning,” Energy, vol. 224. 2021. doi: 10.1016/j.energy.2021.120109.

B. Zazoum, “Solar photovoltaic power prediction using different machine learning methods,” Energy Reports, vol. 8, pp. 19–25, Apr. 2022, doi: 10.1016/j.egyr.2021.11.183.

G. Narvaez, L. F. Giraldo, M. Bressan, and A. Pantoja, “Machine learning for site-adaptation and solar radiation forecasting,” Renew. Energy, vol. 167, pp. 333–342, Apr. 2021, doi: 10.1016/j.renene.2020.11.089.

R. A. A. Ramadhan, Y. R. J. Heatubun, S. F. Tan, and H.-J. Lee, “Comparison of physical and machine learning models for estimating solar irradiance and photovoltaic power,” Renew. Energy, vol. 178, pp. 1006–1019, Nov. 2021, doi: 10.1016/j.renene.2021.06.079.

V. Demir and H. Citakoglu, “Forecasting of solar radiation using different machine learning approaches,” Neural Comput. Appl., vol. 35, no. 1, pp. 887–906, Jan. 2023, doi: 10.1007/s00521-022-07841-x.

A. Rajagopalan et al., “Empowering power distribution: Unleashing the synergy of IoT and cloud computing for sustainable and efficient energy systems,” Results Eng., vol. 21, p. 101949, Mar. 2024, doi: 10.1016/j.rineng.2024.101949.

D. Wilantara, N. B. Mulyono, and D. Winarso, “Automate Short Cyclic Well Job Candidacy Using Artificial Neural Networks–Enabled Lean Six Sigma Approach: A Case Study in Oil and Gas Company,” Int. J. Adv. Sci. Eng. Inf. Technol., vol. 12, no. 4, p. 1639, Aug. 2022, doi: 10.18517/ijaseit.12.4.12845.

D. Rosiani, M. Gibral Walay, P. Rahalintar, A. D. Candra, A. Sofyan, and Y. Arison Haratua, “Application of Artificial Intelligence in Predicting Oil Production Based on Water Injection Rate,” Int. J. Adv. Sci. Eng. Inf. Technol., vol. 13, no. 6, pp. 2338–2344, Dec. 2023, doi: 10.18517/ijaseit.13.6.19399.

I. P. Astuti, A. Yudaputra, D. S. Rinandio, and A. Y. Yuswandi, “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., vol. 11, no. 6, p. 2328, Dec. 2021, doi: 10.18517/ijaseit.11.6.14582.

S. M. Padmaja et al., “Stability and Reliability Analysis for Multiple WT Using Deep Reinforcement Learning,” Electr. Power Components Syst., vol. 52, no. 2, pp. 308–321, Jan. 2024, doi: 10.1080/15325008.2023.2220313.

P. Ashok Babu, J. L. Mazher Iqbal, S. Siva Priyanka, M. Jithender Reddy, G. Sunil Kumar, and R. Ayyasamy, “Power Control and Optimization for Power Loss Reduction Using Deep Learning in Microgrid Systems,” Electr. Power Components Syst., vol. 52, no. 2, pp. 219–232, Jan. 2024, doi: 10.1080/15325008.2023.2217175.

J. Wang et al., “Upstream Solar Wind Prediction up to Mars by an Operational Solar Wind Prediction System,” Sp. Weather, vol. 21, no. 1, Jan. 2023, doi: 10.1029/2022SW003281.

F. Nawab, A. S. Abd Hamid, A. Ibrahim, K. Sopian, A. Fazlizan, and M. F. Fauzan, “Solar irradiation prediction using empirical and artificial intelligence methods: A comparative review,” Heliyon, vol. 9, no. 6, p. e17038, Jun. 2023, doi: 10.1016/j.heliyon.2023.e17038.

L. Goliatt and Z. M. Yaseen, “Development of a hybrid computational intelligent model for daily global solar radiation prediction,” Expert Syst. Appl., vol. 212, p. 118295, Feb. 2023, doi: 10.1016/j.eswa.2022.118295.

H. Zhang, E. Hu, C. Duan, and J. Qin, “An improved model to evaluate the performance of solar-aided power generation plants,” Int. J. Green Energy, vol. 19, no. 3, pp. 300–313, Feb. 2022, doi: 10.1080/15435075.2021.1946810.

Z. Said et al., “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, vol. 48, no. 18, pp. 6761–6777, Feb. 2023, doi: 10.1016/j.ijhydene.2022.07.150.