Revolutionizing Renewable Energy Systems throughAdvanced Machine Learning Integration Approaches
DOI:
https://doi.org/10.33050/corisinta.v2i2.115Keywords:
Machine Learning, Renewable Energy, Energy Forecasting, Energy Systems, Machine Learning AlgorithmsAbstract
The increasing global emphasis on sustainability has accelerated investments in renewable energy technologies, positioning sources like solar, wind, and hydroelectric power as vital alternatives to fossil fuels. Despite significant progress, integrating renewable energy into existing grids remains challenging due to variability in energy output, grid instability, and inefficiencies in energy storage systems. This study investigates the potential of machine learning (ML) to revolutionize the renewable energy sector by enhancing energy forecasting, grid management, and energy storage optimization. Using a combination of supervised learning, deep learning, and reinforcement learning techniques, we developed predictive and optimization models based on historical and real-time datasets. Additionally, structural equation modeling (SEM) with SmartPLS was employed to analyze the relationships between key variables, such as machine learning algorithms, renewable energy sources, sustainability performance, and operational efficiency. The results indicate that machine learning significantly improves energy forecasting accuracy, grid reliability, and storage efficiency, with R-squared values of 0.685 for operational efficiency and 0.588 for sustainability performance. These findings highlight the transformative role of ML in optimizing renewable energy systems and achieving sustainable energy goals. While ML offers promising solutions for renewable energy challenges, further research is needed to address real-time data integration, model scalability, and economic feasibility. This study provides a foundation for future innovations, emphasizing the importance of intelligent, data-driven strategies in advancing global energy sustainability.
References
S. Han, D. Peng, Y. Guo, M. U. Aslam, and R. Xu, “Harnessing technological innovation and renewable
energy and their impact on environmental pollution in g-20 countries,” Scientific Reports, vol. 15, no. 1,
p. 2236, 2025.
❒ E-ISSN:3064-3597 P-ISSN: 3046-7616
I. Amsyar, E. Cristhopher, U. Rahardja, N. Lutfiani, and A. Rizky, “Application of building workers
services in facing industrial revolution 4.0,” Aptisi Transactions on Technopreneurship (ATT), vol. 3,
no. 1, pp. 32–41, 2021.
R. Li, Q. Wang, and Z. Yang, “Natural resource rents and energy transition: Overcoming barriers to
achieve affordable and clean energy (sustainable development goal 7),” Sustainable Development, 2025.
A. Rizky, N. Lutfiani, W. S. Mariyati, A. A. Sari, and K. R. Febrianto, “Decentralization of information
using blockchain technology on mobile apps e-journal,” Blockchain Frontier Technology, vol. 1, no. 2,
pp. 1–10, 2022.
R. Bhatt, G. Patil, H. Kaushal, H. Dehariya, M. Joshi, A. Razzak, and K. Qureshi, “Revolutionizing
healthcare: The challenges & role of artificial intelligence and machine learning in healthcare manage-
ment practice,” Journal of Neonatal Surgery, vol. 14, no. 21s, 2025.
J. Jones, E. Harris, Y. Febriansah, A. Adiwijaya, and I. N. Hikam, “Ai for sustainable development: Appli-
cations in natural resource management, agriculture, and waste management,” International Transactions
on Artificial Intelligence, vol. 2, no. 2, pp. 143–149, 2024.
G. Prabu, C. Sujatha, J. E. Shine, and T. Arulkumar, “Sffho: Development of statistical fitness-based fire
hawk optimizer for software testing and maintenance approach using adaptive deep learning method,”
Journal of Electronic Testing, pp. 1–26, 2025.
A. Rizky, S. Kurniawan, R. D. Gumelar, V. Andriyan, and M. B. Prakoso, “Use of blockchain technology
in implementing information system security on education,” BEST Journal (Biology Education, Sains and
Technology), vol. 4, no. 1, pp. 62–70, 2021.
H. Lund, Renewable energy systems: a smart energy systems approach to the choice and modeling of
fully decarbonized societies. Elsevier, 2024.
A. D. A. Bin Abu Sofian, H. R. Lim, H. Siti Halimatul Munawaroh, Z. Ma, K. W. Chew, and P. L. Show,
“Machine learning and the renewable energy revolution: Exploring solar and wind energy solutions for a
sustainable future including innovations in energy storage,” Sustainable Development, vol. 32, no. 4, pp.
–3978, 2024.
E. Omol, L. Mburu, and D. Onyango, “Anomaly detection in iot sensor data using machine learning
techniques for predictive maintenance in smart grids,” International Journal of Science, Technology &
Management, vol. 5, no. 1, pp. 201–210, 2024.
U. Rahardja, A. Sari, A. H. Alsalamy, S. Askar, A. H. R. Alawadi, and B. Abdullaeva, “Tribological
properties assessment of metallic glasses through a genetic algorithm-optimized machine learning model,”
Metals and Materials International, vol. 30, no. 3, pp. 745–755, 2024.
N. L. Rane, S. P. Choudhary, and J. Rane, “Artificial intelligence and machine learning in renewable and
sustainable energy strategies: A critical review and future perspectives,” Partners Universal International
Innovation Journal, vol. 2, no. 3, pp. 80–102, 2024.
R. Supriati, N. Lutfiani, D. Apriani, A. Rizky et al., “Utilizing the potential of blockchain technology
for leading education 4.0,” in 2022 International Conference on Science and Technology (ICOSTECH).
IEEE, 2022, pp. 01–08.
A. B. Ige, P. A. Adepoju, A. O. Akinade, and A. I. Afolabi, “Machine learning in industrial applications:
An in-depth review and future directions,” 2025.
A. Ishola, “Global renewable energy transition in fossil fuel dependent regions,” World Journal of Ad-
vanced Research and Reviews, vol. 24, no. 01, pp. 1373–138, 2024.
N. Lutfiani, N. P. L. Santoso, R. Ahsanitaqwim, U. Rahardja, and A. R. A. Zahra, “Ai-based strategies to
improve resource efficiency in urban infrastructure,” International Transactions on Artificial Intelligence,
vol. 2, no. 2, pp. 121–127, 2024.
A. Trachuk, “Methodological provisions of system analysis in researching the problems of involving re-
newable energy sources in the energy balance of ukraine,” in Modern Technologies in Energy and Trans-
port II. Springer, 2025, pp. 191–235.
A. Rizky, S. Silen, and D. A. Putra, “The role of blockchain technology in facing revolution education
0,” BEST Journal (Biology Education, Sains and Technology), vol. 4, no. 1, pp. 77–85, 2021.
M. N. Hussain, M. R. Zaman, M. A. Halim, M. S. Ali, and M. Y. A. Khan, “A comprehensive review of
renewable and sustainable energy sources with solar photovoltaic electricity advancement in bangladesh,”
Control Systems and Optimization Letters, vol. 2, no. 1, pp. 1–7, 2024.
J. Shen, Y. Wang, M. Lin, C. Cheng, J. K. Kazak, J. Wang, X. Guo, X. Li, B. Zhou, and L. Ge, “Quantify-
Journal of Computer Science and Technology Application (CORISINTA) ❒ 11
ing the impact of extreme weather on china’s hydropower–wind–solar renewable energy system,” Nature
Water, pp. 1–15, 2025.
F. A. Rahardja, S.-C. Chen, and U. Rahardja, “Review of behavioral psychology in transition to solar
photovoltaics for low-income individuals,” Sustainability, vol. 14, no. 3, p. 1537, 2022.
E. Ejuh Che, K. Roland Abeng, C. D. Iweh, G. J. Tsekouras, and A. Fopah-Lele, “The impact of in-
tegrating variable renewable energy sources into grid-connected power systems: challenges, mitigation
strategies, and prospects,” Energies, vol. 18, no. 3, p. 689, 2025.
A. Manjula, R. Niraimathi, M. Rajarajeswari, and S. C. Devi, “Grid integration of renewable energy
sources: challenges and solutions,” in Green Machine Learning and Big Data for Smart Grids. Elsevier,
, pp. 263–286.
M. I. Sanni, R. D. Pramudya, D. A. Jamaludin, S. V. Sihotang, I. N. Hikam et al., “Integrating technology
and environmental policy for effective air quality monitoring in indonesia,” in 2024 3rd International
Conference on Creative Communication and Innovative Technology (ICCIT). IEEE, 2024, pp. 1–6.
K. Shafiei, A. Seifi, and M. T. Hagh, “A novel multi-objective optimization approach for resilience en-
hancement considering integrated energy systems with renewable energy, energy storage, energy sharing,
and demand-side management,” Journal of Energy Storage, vol. 115, p. 115966, 2025.
E. T. A. Ugah, O. G. Ndubuisi, E. P. S. E. Ali, C. A. R. Obiorah, O. Nesiama, E. C. O. Agbakhamen,
and E. O. P. Okoro, “Renewable energy and sustainable development: emerging trends and technologies,”
IPHO-Journal of Advance Research in Science And Engineering, vol. 3, no. 02, pp. 16–30, 2025.
A. Pambudi, N. Lutfiani, M. Hardini, A. R. A. Zahra, and U. Rahardja, “The digital revolution of startup
matchmaking: Ai and computer science synergies,” in 2023 Eighth International Conference on Infor-
matics and Computing (ICIC). IEEE, 2023, pp. 1–6.
T. Wang, Y. Zuo, T. Manda, D. Hwarari, and L. Yang, “Harnessing artificial intelligence, machine learning
and deep learning for sustainable forestry management and conservation: Transformative potential and
future perspectives,” Plants, vol. 14, no. 7, p. 998, 2025.
P. S. Mohapatra, “Artificial intelligence and machine learning for test engineers: Concepts in software
quality assurance,” Intelligent Assurance: Artificial Intelligence-Powered Software Testing in the Modern
Development Lifecycle, vol. 4, p. 17, 2025.
J. Siswanto, V. A. Goeltom, I. N. Hikam, E. A. Lisangan, and A. Fitriani, “Market trend analysis and data-
based decision making in increasing business competitiveness,” Sundara Advanced Research on Artificial
Intelligence, vol. 1, no. 1, pp. 1–8, 2025.
K. Ukoba, K. O. Olatunji, E. Adeoye, T.-C. Jen, and D. M. Madyira, “Optimizing renewable energy
systems through artificial intelligence: Review and future prospects,” Energy & Environment, vol. 35,
no. 7, pp. 3833–3879, 2024.
S. Hossain, M. Hasanuzzaman, M. Hossain, M. H. H. Amjad, M. S. S. Shovon, M. S. Hossain, and M. K.
Rahman, “Forecasting energy consumption trends with machine learning models for improved accuracy
and resource management in the usa,” Journal of Business and Management Studies, vol. 7, no. 1, pp.
–217, 2025.
P. A. Sunarya, U. Rahardja, S. C. Chen, Y.-M. Lic, and M. Hardini, “Deciphering digital social dynam-
ics: A comparative study of logistic regression and random forest in predicting e-commerce customer
behavior,” Journal of Applied Data Sciences, vol. 5, no. 1, pp. 100–113, 2024.
A. Rizky, R. W. Nugroho, W. Sejati, O. Sy et al., “Optimizing blockchain digital signature security in
driving innovation and sustainable infrastructure,” Blockchain Frontier Technology, vol. 4, no. 2, pp.
–192, 2025.
A. Atalan, L. A. G¨undo˘gdu, H. Kahyalık, and Y. A. Atalan, “Machine learning-based wind energy fore-
casting using weather parameters: The example of yalova,” Journal of Statistics and Applied Sciences,
no. 11, pp. 40–49, 2025.
A. R. Singh, R. S. Kumar, M. Bajaj, C. B. Khadse, and I. Zaitsev, “Machine learning-based energy man-
agement and power forecasting in grid-connected microgrids with multiple distributed energy sources,”
Scientific Reports, vol. 14, no. 1, p. 19207, 2024.
A. Rizky, A. Gunawan, M. A. Komara, M. Madani, and E. Harris, “Optimization of machine learning
algorithms for fraud detection in e-payment systems,” Journal of Computer Science and Technology Ap-
plication, vol. 2, no. 1, pp. 55–64, 2025.
T. Zhang, X. Wang, and A. Parisio, “A corrective control framework for mitigating voltage fluctuations
❒ E-ISSN:3064-3597 P-ISSN: 3046-7616
and congestion in distribution networks with high renewable energy penetration,” International Journal
of Electrical Power & Energy Systems, vol. 165, p. 110508, 2025.
V. Dileep and Muneeshwari, “Improving the accuracy of predicting congested traffic flow road transport
using random forest algorithm and compared with the decision tree algorithm using machine learning,” in
AIP Conference Proceedings, vol. 3267, no. 1. AIP Publishing LLC, 2025, p. 020169.
A. PAS¸ AO ˘GLU and A. HABIBNEZHAD, “Machine learning enhanced hybrid energy storage manage-
ment system for renewable integration and grid stability optimization in smart microgrids,” Journal of
Thermal Engineering, vol. 11, no. 4, 2025.
H. J. C. Conde, C. M. Demition, and J. Honra, “Storage is the new black: A review of energy storage
system applications to resolve intermittency in renewable energy systems,” Energies, vol. 18, no. 2, p.
, 2025.
A. Rizky, M. Z. Firli, N. A. Lindzani, S. Audiah, and L. Pasha, “Advanced cyber threat detection: Big
data-driven ai solutions in complex networks,” Journal of Computer Science and Technology Application,
vol. 1, no. 2, pp. 136–143, 2024.
R. Yasmin, B. R. Amin, R. Shah, and A. Barton, “A survey of commercial and industrial demand response
flexibility with energy storage systems and renewable energy,” Sustainability, vol. 16, no. 2, p. 731, 2024.
A. Gulraiz, A. J. Al Bastaki, K. Magamal, M. Subhi, A. Hammad, A. Allanjawi, S. H. Zaidi, H. M.
Khalid, A. Ismail, G. A. Hussain et al., “Energy advancements and integration strategies in hydrogen and
battery storage for renewable energy systems,” iScience, vol. 28, no. 3, 2025.
M. Yusuf, M. Yusup, R. D. Pramudya, A. Y. Fauzi, and A. Rizky, “Enhancing user login efficiency via
single sign-on integration in internal quality assurance system (espmi),” International Transactions on
Artificial Intelligence, vol. 2, no. 2, pp. 164–172, 2024.
A. Hassan, S. S. U. H. Mohani, I. Y. Essani, S. A. Taj, A. Aslam, and Y. Abbas, “Ai-enabled energy
management for large-scale solar farms: Optimizing power distribution, grid stability, and real-time per-
formance monitoring,” THE PROGRESS: A Journal of Multidisciplinary Studies, vol. 6, no. 1, pp. 66–84,
S. Aslam, P. P. Aung, A. S. Rafsanjani, and A. P. A. Majeed, “Machine learning applications in energy
systems: current trends, challenges, and research directions,” Energy Informatics, vol. 8, no. 1, p. 62,
A. Sharma, “K.(2025). use of machine learning in predicting electric school bus battery range for opti-
mized routing,” International Journal of Supply Chain Management, vol. 14, no. 2, pp. 46–54.
A. Aghazadeh Ardebili, O. Hasidi, A. Bendaouia, A. Khalil, S. Khalil, D. Luceri, A. Longo, E. H. Abdel-
wahed, S. Qassimi, and A. Ficarella, “Enhancing resilience in complex energy systems through real-time
anomaly detection: a systematic literature review,” Energy Informatics, vol. 7, no. 1, p. 96, 2024.
