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Applications of artificial intelligence methods in renewable energy systems

Yıl 2025, Cilt: 11 Sayı: 1, 86 - 99, 22.12.2025
https://doi.org/10.31593/ijeat.1727157

Öz

In order to reduce environmental problems caused by fossil fuels and to build a sustainable future, radical transformations are required in energy systems. In this transition process, energy sources characterized by environmental sustainability and renewability play a critical role. Renewable energy systems such as solar, wind and hydroelectric power are strategically important for reducing carbon emissions and ensuring energy security. This study comprehensively examines current applications of artificial intelligence (AI) techniques in these energy systems. In particular, it analyzes the contributions of AI-based solutions in key areas such as production forecasting, predictive maintenance strategies, system performance optimization and smart grid integration. Numerous studies have shown that machine learning methods, deep learning approaches and optimization algorithms enable accurate predictions and effective decision-making to address challenges such as intermittency, variability and uncertainty inherent in renewable energy sources. Moreover, the advantages offered by these technologies in enhancing operational efficiency, minimizing energy losses and supporting long-term environmental sustainability are emphasized. The findings suggest that AI-driven systems will significantly contribute to the digital transformation of the energy sector and play a decisive role in shaping the sustainable, flexible and intelligent energy infrastructures of the future.

Kaynakça

  • IEA. Global Electricity Generation by Renewable Energy Technology, 2023. [Online]. Available: https://www.iea.org/data-and-statistics/charts/global-electricity-generation-by-renewable-energy-technology-main-case-2023-and-2030 (Accessed: 25 June 2025)
  • IRENA. Future of Solar Photovoltaic: Deployment, investment, technology, grid integration and socio-economic aspects, 2019. [Online]. Available: https://serendipv.eu/media/filer_public/86/a4/86a4e88a-61cc-4b2e-bc31-8d373805c229/irena_future_of_solar_pv_2019.pdf (Accessed: 25 June 2025)
  • Sayed, E.T., Olabi, A.G., Alami, A.H.; Radwan, A., Mdallal, A., Rezk, A., Abdelkareem, M.A. Renewable Energy and Energy Storage Systems. Energies 2023, 16, 1415.
  • Kiasari, M.; Ghaffari, M.; Aly, H.H. A Comprehensive Review of the Current Status of Smart Grid Technologies for Renewable Energies Integration and Future Trends: The Role of Machine Learning and Energy Storage Systems. Energies 2024, 17, 4128.
  • Global Wind Energy Council (GWEC). GWEC Global Wind Report 2025, 2025. GWEC Publications, Lisbon, Portugal, 20.
  • Blaabjerg, F. and Ma, K. 2017. Wind energy systems. IEEE Transactions on Industrial Electronics, 105(11), 2116–2131.
  • Serrano-González, J. and Lacal-Arántegui, R. 2016. Technological evolution of onshore wind turbines: A market-based analysis. Wind Energy, 19(12), 2171–2187.
  • Lu, M.-S., Chang, C.-L., Lee, W.-J., and Wang, L. 2009. Combining the wind power generation system with energy storage equipment. IEEE Transactions on Industry Applications, 45(2), 2019-2115.
  • Bayazıt, Y. 2021. The effect of hydroelectric power plants on the carbon emission: An example of Gokcekaya dam, Turkey. Renewable and Sustainable Energy Reviews,170, 181 – 187.
  • Rahman, M., Farrok O., Haque M., 2022. Environmental impact of renewable energy source based electrical power plants: Solar, wind, hydroelectric, biomass, geothermal, tidal, ocean, and osmotic. Renewable & Sustainable Energy Reviews., 2022, 161, 112279
  • Shahgholian G., 2020. An Overview of Hydroelectric Power Plant: Operation, Modeling, and Control. Journal of Renewable Energy and Environment,7 (3), 14 – 28.
  • Eker, O. F., Hydropower part II: condition monitoring and predictive maintenance, 2022. [Online] Available: https://www.kavaken.com/blog/hydropower-part-ii-condition-monitoring-and-predictive-maintenance (Accessed: 25 June 2025)
  • Özcan E., Danışan T., Yumuşak R., Eren T.. An artificial neural network model supported with multi criteria decision making approaches for maintenance planning in hydroelectric power plants. Eksploatacja i niezawodnosc – Maintenance and Reliability 2020,22 (3), 400–418.
  • Velasquez, V., Flores, W., Machine Learning Approach for Predictive Maintenance in Hydroelectric Power Plants. In: Proceedings of the IEEE Biennial Congress of Argentina (ARGENCON), 2022, IEEE, pp. 1– 6.
  • Hassan, A.A., Atia, D.M. and El-Madany, H.T. Machine Learning-Based Medium-Term Power Fore casting of a Grid-Tied Photovoltaic Plant. Smart Grid and Renewable Energy, (2024) 15, 289 – 306.
  • Asiedu, S.T., Nyarko, F.K.A., Boahen, S., Effah, F.B., Asaaga, B.A. Machine learning forecasting of solar PV production using single and hybrid models over different time horizons. Heliyon, 2024 10, e28898.
  • Theocharides, S., Makrides, G., Georghiou, G.E. PV generation forecasting utilizing a classification-only approach. EPJ Photovoltaics, 2024, 15(12), 1–11.
  • Soleymani, S., Mohammadzadeh, S. 2023. Comparative Analysis of Machine Learning Algorithms for Solar Irradiance Forecasting in Smart Grids. arXiv preprint. 2310.13791. Available: https://doi.org/10.48550/arXiv.2310.13791 (Accessed: 25 June 2025)
  • Kerkau,S., Sepasi, S., Howlader, H.O.R., Roose,L. Day – Ahead net load forecasting for renewable integrated buildings using XGBoost. Energies 2025, 18, 1518.
  • Zhang, G., Li, H., Wang, L., Wang, W., Guo, J., Qin, H., Ni, X. Research on Medium- and Long-Term Hydropower Generation Forecasting Method Based on LSTMand Transformer, Energies 2024, 17, 5707.
  • Güven, A.F, Yörükerən, N. A comparative study on hybrid GA-PSO performance for stand-alone hybrid energy systems optimization. Sigma Journal of Engineering and Natural Sciences, 2024, 42(5), 1410–1438.
  • Bade,S.O., Tomomewo, O.S., Meenakshisundaram,A., Dey,M., Alamooti, M., Halwany,N. Multi-Criteria Optimization of a Hybrid Renewable Energy System Using Particle Swarm Optimization for Optimal Sizing and Performance Evaluation. Clean Technol. 2025, 7, 23.
  • Dellosa, J.T., Palconit, E.C. Artificial Intelligence (AI) in Renewable Energy Systems: A Condensed Review of its Applications and Techniques, 2021. Department of Electronics Engineering, Caraga State University, and Ateneo de Davao University, Philippines.
  • Di Tommaso, A., Betti, A., Fontanelli, G., Michelozzi, B. A multi-stage model based on YOLOv3 for defect detection in PV panels based on IR and visible imaging by unmanned aerial vehicle. FlySight S.r.l., 2022, Livorno, Italy.
  • Abisoye, B.O., Sun, Y., and Zenghui, W. A survey of artificial intelligence methods for renewable energy forecasting: Methodologies and insights. Renewable Energy Focus, 2024, 48, 100529.
  • Wani, T.A., Channi, H.K. A review of fuzzy logic and artificial neural network technologies used for MPPT. Turkish Journal of Computer and Mathematics Education, . 2021, 12(2), 2912–2918.
  • Alexandru, C. Simulation and Optimization of a Dual-Axis Solar Tracking Mechanism. Mathematics, 2024, 12, 1034.
  • Thajeel, S.M., Atilla, D.Ç. Reinforcement Neural Network-Based Grid-Integrated PV Control and Battery Management System. Energies, 2025, 18, 637.
  • Zafiriakis, D., Tzanes, G., and Kaldellis, J.K. Forecasting of wind power generation with the use of artificial neural networks and support vector regression models. Applied Energy Symposium and Forum, Renewable Energy Integration with Mini/Microgrids (REM 2018), 29–30 September 2018, Rhodes, Greece. Energy Procedia, 2019, 159, 509–514.
  • Xiao, Z., Tang, F., Wang, M. Wind Power Short-Term Forecasting Method Based on LSTMandMultiple Error Correction. Sustainability, 2023, 15, 3798.
  • Kusiak, A., Verma, A. A data-mining approach to monitoring wind turbines. IEEE Transactions on Sustainable Energy, 2012, 3(1), 150–157.
  • Rahimlarki, R., Gao, Z., Jin, N., Zhang, A. Convolutional neural network fault classification based on time-series analysis for benchmark wind turbine machine. Renewable Energy, 2022, 185, 916–931.
  • Zhao, R., Yan, R., Chen, Z., Mao, K., Wang, P., and Gao, R.X. 2015. Deep learning and its applications to machine health monitoring: A survey. IEEE Transactions on Neural Networks and Learning Systems, 14(8).
  • Barbalho, P.N., Moraes, A.L., Lacerda, V.A., Barra, P.H.A., Fernandes, R.A.S., Coury, D.V. 2025. Reinforcement learning solutions for microgrid control and management: A survey. IEEE Access, 2025, 13, 356478.
  • Yi, S., Kondolf, G.M., Sandoval-Solís, S., Dale, L. Application of machine learning-based energy use forecasting for inter-basin water transfer project. Water Resources Management, 2022, 36, 5675–5694.
  • Wilbrand K., Taormina R., ten Veldhuis M.C., Visser M., Hrachowitz M., Nuttall J., Dahm R. Predicting streamflow with LSTM networks using global datasets. Frontiers in Water, 2023, 5, 11666124.
  • Kratzer, F., Klotz, D., Brenner, C., Schulz, K., Herrnegger, M. Rainfall–runoff modelling using Long Short-Term Memory (LSTM) networks. Hydrology and Earth System Sciences, 2018, 22, 6005–6022.
  • Ibrahim, N., Ahmad, N., Mat Jan, N.A., Zainudin, Z., Jamil, N.S., Azlan, A. Comparative analysis of ARIMA and LSTM approaches for monthly river flow forecasting in Terengganu. In: Proceedings of the 5th International Conference on Artificial Intelligence and Data Sciences (AiDAS 2024), 2024, 363 – 368.
  • Quaranta, E., Bejarano, M.D., Comoglio, C., Fuentes-Pérez, J.F., Pérez-Díaz, J.I., Sanz-Ronda, F.J., Schlechter, M., Szabo-Meszaros, M., Tuhtan, J.A. Digitalization and real-time control to mitigate environmental impacts along rivers: Focus on artificial barriers, hydropower systems and European priorities. Science of the Total Environment, 2023, 875, 162489.
  • Galvão Filho, A.R., Silva, D.F.C., de Carvalho, R.V., de Souza Lima Ribeiro, F., Coelho, C.J. 2020. Forecasting of water flow in a hydroelectric power plant using LSTM recurrent neural network. In: Proceedings of the 2nd International Conference on Electrical, Communication and Computer Engineering (ICECCE), 12–13 June 2020, Istanbul, Turkey.
  • Velasquez, V., Flores, W. Machine learning approach for predictive maintenance in hydroelectric power plants. IEEE Conference on Sustainable Applications of Electrical Engineering and Energy Management (SAEEE 2022), Tegucigalpa, Honduras, 2022.
  • IEA, Electricity Market Report, 2023. [Online] Available: https://www.iea.org/reports/electricity-market-report-2023 (Accessed: 25 June 2025)
  • EnergySage. Advantages and Disadvantages of Renewable Energy, 2024. [Online]. Available: https://www.energysage.com/about-clean-energy/advantages-and-disadvantages-of-renewable-energy/ (Accessed: 25 June 2025)
  • Ali, S. S., Choi, B. J. State-of-the-art artificial intelligence techniques for distributed smart grids: A review. Electronics, 2020, 9(6), 1030.
  • Wang, X., Rhee, H. S., Ahn, S.H. Off-grid power plant load management system applied in a rural area of Africa. Applied Sciences, 2020, 10(12), 4230.
  • Selvam, C., Srinivas, K., Ayyappan, G. S., Sarma, M. V. Advanced metering infrastructure for smart grid applications, 2020. Central Scientific Instruments Organisation, CSIR Madras Complex, Chennai, India.
  • Katayara, S., Shah, M. A., Chowdhary, B. S., Akhtar, F., Lashari, G. A. Monitoring, control and energy management of smart grid system via WSN technology through SCADA applications. Wireless Personal Communications, 2019, 106(4), 1951–1968.
  • Gold, R., Waters, C., York, D. Leveraging Advanced Metering Infrastructure to Save Energy, 2020. American Council for an Energy-Efficient Economy, Report U2001.
  • Marimuthu, K. P., Durairaj, D., Srinivasan, S. K. Development and implementation of advanced metering infrastructure for efficient energy utilization in smart grid environment. International Transactions on Electrical Energy Systems, 2017, Wiley.
  • Khan, S., Khan, R., Al-Bayatti, A. H. Secure communication architecture for dynamic energy management in smart grid. IEEE Power and Energy Technology Systems Journal, 2019.
  • Dileep, G. A survey on smart grid technologies and applications. Renewable Energy, 2020, 146, 2589–2625.
  • Chaves, T. R., Martins, M. A. I., Martins, K. A., Macedo, A. F. Development of an Automated Distribution Grid With the Application of New Technologies. IEEE Access, 2022, 10, 13736–13748.
  • Balamurugan, M., Narayanan, K., Raghu, N., Arjun Kumar, G.B.,Trupti, V.N. Role of artificial intelligence in smart grid – a mini review. Frontiers in Artificial Intelligence, 2025, 8, 1551661.
  • Noor, S., Yang, W., Guo, M., van Dam, K. H., Wang, X. Energy demand side management within micro-grid networks enhanced by blockchain. Applied Energy, 2018, 228, 1385–1398.
  • Logenthiran, T., Srinivasan, D., Shun, T. Z. Demand side management in smart grid using heuristic optimization. IEEE Transactions on Smart Grid, 2012, 3(3), 1244–1252.
  • World Economic Forum. How AI is accelerating the transition to net-zero energy systems, 2025. [Online]. Available: https://www.weforum.org/stories/2025/01/energy-ai-net-zero/ (Accessed: 24 June 2025)
  • U.S. Department of Energy. Artificial Intelligence and Energy, 2024. [Online] Available: https://www.energy.gov/topics/artificial-intelligence-energy
  • Ali, D.M.T.E., Motuzien˙ e, V., Džiugait˙ e-Tum˙enien˙ e, R. AI-Driven Innovations in Building Energy Management Systems: A Review of Potential Applications and Energy Savings. Energies 2024, 17, 4277.
  • Rane, N. L., Choudhary, S. P., Rane, J. Artificial Intelligence and Machine Learning in Renewable and Sustainable Energy Strategies: A Critical Review and Future Perspectives. Partners Universal International Innovation Journal (PUIIJ), 2024, 2(3).
  • Entezari, A., Aslani, A., Zahedi, R., Noorollahi, Y. Artificial intelligence and machine learning in energy systems: A bibliographic perspective. Energy Strategy Reviews, 2023, 45, 101017.
  • Raihan, A. A comprehensive review of artificial intelligence and machine learning applications in the energy sector. Journal of Technology Innovations and Energy, 2023, 2(4), 608.

Applications of artificial intelligence methods in renewable energy systems

Yıl 2025, Cilt: 11 Sayı: 1, 86 - 99, 22.12.2025
https://doi.org/10.31593/ijeat.1727157

Öz

In order to reduce environmental problems caused by fossil fuels and to build a sustainable future, radical transformations are required in energy systems. In this transition process, energy sources characterized by environmental sustainability and renewability play a critical role. Renewable energy systems such as solar, wind and hydroelectric power are strategically important for reducing carbon emissions and ensuring energy security. This study comprehensively examines current applications of artificial intelligence (AI) techniques in these energy systems. In particular, it analyzes the contributions of AI-based solutions in key areas such as production forecasting, predictive maintenance strategies, system performance optimization and smart grid integration. Numerous studies have shown that machine learning methods, deep learning approaches and optimization algorithms enable accurate predictions and effective decision-making to address challenges such as intermittency, variability and uncertainty inherent in renewable energy sources. Moreover, the advantages offered by these technologies in enhancing operational efficiency, minimizing energy losses and supporting long-term environmental sustainability are emphasized. The findings suggest that AI-driven systems will significantly contribute to the digital transformation of the energy sector and play a decisive role in shaping the sustainable, flexible and intelligent energy infrastructures of the future.

Kaynakça

  • IEA. Global Electricity Generation by Renewable Energy Technology, 2023. [Online]. Available: https://www.iea.org/data-and-statistics/charts/global-electricity-generation-by-renewable-energy-technology-main-case-2023-and-2030 (Accessed: 25 June 2025)
  • IRENA. Future of Solar Photovoltaic: Deployment, investment, technology, grid integration and socio-economic aspects, 2019. [Online]. Available: https://serendipv.eu/media/filer_public/86/a4/86a4e88a-61cc-4b2e-bc31-8d373805c229/irena_future_of_solar_pv_2019.pdf (Accessed: 25 June 2025)
  • Sayed, E.T., Olabi, A.G., Alami, A.H.; Radwan, A., Mdallal, A., Rezk, A., Abdelkareem, M.A. Renewable Energy and Energy Storage Systems. Energies 2023, 16, 1415.
  • Kiasari, M.; Ghaffari, M.; Aly, H.H. A Comprehensive Review of the Current Status of Smart Grid Technologies for Renewable Energies Integration and Future Trends: The Role of Machine Learning and Energy Storage Systems. Energies 2024, 17, 4128.
  • Global Wind Energy Council (GWEC). GWEC Global Wind Report 2025, 2025. GWEC Publications, Lisbon, Portugal, 20.
  • Blaabjerg, F. and Ma, K. 2017. Wind energy systems. IEEE Transactions on Industrial Electronics, 105(11), 2116–2131.
  • Serrano-González, J. and Lacal-Arántegui, R. 2016. Technological evolution of onshore wind turbines: A market-based analysis. Wind Energy, 19(12), 2171–2187.
  • Lu, M.-S., Chang, C.-L., Lee, W.-J., and Wang, L. 2009. Combining the wind power generation system with energy storage equipment. IEEE Transactions on Industry Applications, 45(2), 2019-2115.
  • Bayazıt, Y. 2021. The effect of hydroelectric power plants on the carbon emission: An example of Gokcekaya dam, Turkey. Renewable and Sustainable Energy Reviews,170, 181 – 187.
  • Rahman, M., Farrok O., Haque M., 2022. Environmental impact of renewable energy source based electrical power plants: Solar, wind, hydroelectric, biomass, geothermal, tidal, ocean, and osmotic. Renewable & Sustainable Energy Reviews., 2022, 161, 112279
  • Shahgholian G., 2020. An Overview of Hydroelectric Power Plant: Operation, Modeling, and Control. Journal of Renewable Energy and Environment,7 (3), 14 – 28.
  • Eker, O. F., Hydropower part II: condition monitoring and predictive maintenance, 2022. [Online] Available: https://www.kavaken.com/blog/hydropower-part-ii-condition-monitoring-and-predictive-maintenance (Accessed: 25 June 2025)
  • Özcan E., Danışan T., Yumuşak R., Eren T.. An artificial neural network model supported with multi criteria decision making approaches for maintenance planning in hydroelectric power plants. Eksploatacja i niezawodnosc – Maintenance and Reliability 2020,22 (3), 400–418.
  • Velasquez, V., Flores, W., Machine Learning Approach for Predictive Maintenance in Hydroelectric Power Plants. In: Proceedings of the IEEE Biennial Congress of Argentina (ARGENCON), 2022, IEEE, pp. 1– 6.
  • Hassan, A.A., Atia, D.M. and El-Madany, H.T. Machine Learning-Based Medium-Term Power Fore casting of a Grid-Tied Photovoltaic Plant. Smart Grid and Renewable Energy, (2024) 15, 289 – 306.
  • Asiedu, S.T., Nyarko, F.K.A., Boahen, S., Effah, F.B., Asaaga, B.A. Machine learning forecasting of solar PV production using single and hybrid models over different time horizons. Heliyon, 2024 10, e28898.
  • Theocharides, S., Makrides, G., Georghiou, G.E. PV generation forecasting utilizing a classification-only approach. EPJ Photovoltaics, 2024, 15(12), 1–11.
  • Soleymani, S., Mohammadzadeh, S. 2023. Comparative Analysis of Machine Learning Algorithms for Solar Irradiance Forecasting in Smart Grids. arXiv preprint. 2310.13791. Available: https://doi.org/10.48550/arXiv.2310.13791 (Accessed: 25 June 2025)
  • Kerkau,S., Sepasi, S., Howlader, H.O.R., Roose,L. Day – Ahead net load forecasting for renewable integrated buildings using XGBoost. Energies 2025, 18, 1518.
  • Zhang, G., Li, H., Wang, L., Wang, W., Guo, J., Qin, H., Ni, X. Research on Medium- and Long-Term Hydropower Generation Forecasting Method Based on LSTMand Transformer, Energies 2024, 17, 5707.
  • Güven, A.F, Yörükerən, N. A comparative study on hybrid GA-PSO performance for stand-alone hybrid energy systems optimization. Sigma Journal of Engineering and Natural Sciences, 2024, 42(5), 1410–1438.
  • Bade,S.O., Tomomewo, O.S., Meenakshisundaram,A., Dey,M., Alamooti, M., Halwany,N. Multi-Criteria Optimization of a Hybrid Renewable Energy System Using Particle Swarm Optimization for Optimal Sizing and Performance Evaluation. Clean Technol. 2025, 7, 23.
  • Dellosa, J.T., Palconit, E.C. Artificial Intelligence (AI) in Renewable Energy Systems: A Condensed Review of its Applications and Techniques, 2021. Department of Electronics Engineering, Caraga State University, and Ateneo de Davao University, Philippines.
  • Di Tommaso, A., Betti, A., Fontanelli, G., Michelozzi, B. A multi-stage model based on YOLOv3 for defect detection in PV panels based on IR and visible imaging by unmanned aerial vehicle. FlySight S.r.l., 2022, Livorno, Italy.
  • Abisoye, B.O., Sun, Y., and Zenghui, W. A survey of artificial intelligence methods for renewable energy forecasting: Methodologies and insights. Renewable Energy Focus, 2024, 48, 100529.
  • Wani, T.A., Channi, H.K. A review of fuzzy logic and artificial neural network technologies used for MPPT. Turkish Journal of Computer and Mathematics Education, . 2021, 12(2), 2912–2918.
  • Alexandru, C. Simulation and Optimization of a Dual-Axis Solar Tracking Mechanism. Mathematics, 2024, 12, 1034.
  • Thajeel, S.M., Atilla, D.Ç. Reinforcement Neural Network-Based Grid-Integrated PV Control and Battery Management System. Energies, 2025, 18, 637.
  • Zafiriakis, D., Tzanes, G., and Kaldellis, J.K. Forecasting of wind power generation with the use of artificial neural networks and support vector regression models. Applied Energy Symposium and Forum, Renewable Energy Integration with Mini/Microgrids (REM 2018), 29–30 September 2018, Rhodes, Greece. Energy Procedia, 2019, 159, 509–514.
  • Xiao, Z., Tang, F., Wang, M. Wind Power Short-Term Forecasting Method Based on LSTMandMultiple Error Correction. Sustainability, 2023, 15, 3798.
  • Kusiak, A., Verma, A. A data-mining approach to monitoring wind turbines. IEEE Transactions on Sustainable Energy, 2012, 3(1), 150–157.
  • Rahimlarki, R., Gao, Z., Jin, N., Zhang, A. Convolutional neural network fault classification based on time-series analysis for benchmark wind turbine machine. Renewable Energy, 2022, 185, 916–931.
  • Zhao, R., Yan, R., Chen, Z., Mao, K., Wang, P., and Gao, R.X. 2015. Deep learning and its applications to machine health monitoring: A survey. IEEE Transactions on Neural Networks and Learning Systems, 14(8).
  • Barbalho, P.N., Moraes, A.L., Lacerda, V.A., Barra, P.H.A., Fernandes, R.A.S., Coury, D.V. 2025. Reinforcement learning solutions for microgrid control and management: A survey. IEEE Access, 2025, 13, 356478.
  • Yi, S., Kondolf, G.M., Sandoval-Solís, S., Dale, L. Application of machine learning-based energy use forecasting for inter-basin water transfer project. Water Resources Management, 2022, 36, 5675–5694.
  • Wilbrand K., Taormina R., ten Veldhuis M.C., Visser M., Hrachowitz M., Nuttall J., Dahm R. Predicting streamflow with LSTM networks using global datasets. Frontiers in Water, 2023, 5, 11666124.
  • Kratzer, F., Klotz, D., Brenner, C., Schulz, K., Herrnegger, M. Rainfall–runoff modelling using Long Short-Term Memory (LSTM) networks. Hydrology and Earth System Sciences, 2018, 22, 6005–6022.
  • Ibrahim, N., Ahmad, N., Mat Jan, N.A., Zainudin, Z., Jamil, N.S., Azlan, A. Comparative analysis of ARIMA and LSTM approaches for monthly river flow forecasting in Terengganu. In: Proceedings of the 5th International Conference on Artificial Intelligence and Data Sciences (AiDAS 2024), 2024, 363 – 368.
  • Quaranta, E., Bejarano, M.D., Comoglio, C., Fuentes-Pérez, J.F., Pérez-Díaz, J.I., Sanz-Ronda, F.J., Schlechter, M., Szabo-Meszaros, M., Tuhtan, J.A. Digitalization and real-time control to mitigate environmental impacts along rivers: Focus on artificial barriers, hydropower systems and European priorities. Science of the Total Environment, 2023, 875, 162489.
  • Galvão Filho, A.R., Silva, D.F.C., de Carvalho, R.V., de Souza Lima Ribeiro, F., Coelho, C.J. 2020. Forecasting of water flow in a hydroelectric power plant using LSTM recurrent neural network. In: Proceedings of the 2nd International Conference on Electrical, Communication and Computer Engineering (ICECCE), 12–13 June 2020, Istanbul, Turkey.
  • Velasquez, V., Flores, W. Machine learning approach for predictive maintenance in hydroelectric power plants. IEEE Conference on Sustainable Applications of Electrical Engineering and Energy Management (SAEEE 2022), Tegucigalpa, Honduras, 2022.
  • IEA, Electricity Market Report, 2023. [Online] Available: https://www.iea.org/reports/electricity-market-report-2023 (Accessed: 25 June 2025)
  • EnergySage. Advantages and Disadvantages of Renewable Energy, 2024. [Online]. Available: https://www.energysage.com/about-clean-energy/advantages-and-disadvantages-of-renewable-energy/ (Accessed: 25 June 2025)
  • Ali, S. S., Choi, B. J. State-of-the-art artificial intelligence techniques for distributed smart grids: A review. Electronics, 2020, 9(6), 1030.
  • Wang, X., Rhee, H. S., Ahn, S.H. Off-grid power plant load management system applied in a rural area of Africa. Applied Sciences, 2020, 10(12), 4230.
  • Selvam, C., Srinivas, K., Ayyappan, G. S., Sarma, M. V. Advanced metering infrastructure for smart grid applications, 2020. Central Scientific Instruments Organisation, CSIR Madras Complex, Chennai, India.
  • Katayara, S., Shah, M. A., Chowdhary, B. S., Akhtar, F., Lashari, G. A. Monitoring, control and energy management of smart grid system via WSN technology through SCADA applications. Wireless Personal Communications, 2019, 106(4), 1951–1968.
  • Gold, R., Waters, C., York, D. Leveraging Advanced Metering Infrastructure to Save Energy, 2020. American Council for an Energy-Efficient Economy, Report U2001.
  • Marimuthu, K. P., Durairaj, D., Srinivasan, S. K. Development and implementation of advanced metering infrastructure for efficient energy utilization in smart grid environment. International Transactions on Electrical Energy Systems, 2017, Wiley.
  • Khan, S., Khan, R., Al-Bayatti, A. H. Secure communication architecture for dynamic energy management in smart grid. IEEE Power and Energy Technology Systems Journal, 2019.
  • Dileep, G. A survey on smart grid technologies and applications. Renewable Energy, 2020, 146, 2589–2625.
  • Chaves, T. R., Martins, M. A. I., Martins, K. A., Macedo, A. F. Development of an Automated Distribution Grid With the Application of New Technologies. IEEE Access, 2022, 10, 13736–13748.
  • Balamurugan, M., Narayanan, K., Raghu, N., Arjun Kumar, G.B.,Trupti, V.N. Role of artificial intelligence in smart grid – a mini review. Frontiers in Artificial Intelligence, 2025, 8, 1551661.
  • Noor, S., Yang, W., Guo, M., van Dam, K. H., Wang, X. Energy demand side management within micro-grid networks enhanced by blockchain. Applied Energy, 2018, 228, 1385–1398.
  • Logenthiran, T., Srinivasan, D., Shun, T. Z. Demand side management in smart grid using heuristic optimization. IEEE Transactions on Smart Grid, 2012, 3(3), 1244–1252.
  • World Economic Forum. How AI is accelerating the transition to net-zero energy systems, 2025. [Online]. Available: https://www.weforum.org/stories/2025/01/energy-ai-net-zero/ (Accessed: 24 June 2025)
  • U.S. Department of Energy. Artificial Intelligence and Energy, 2024. [Online] Available: https://www.energy.gov/topics/artificial-intelligence-energy
  • Ali, D.M.T.E., Motuzien˙ e, V., Džiugait˙ e-Tum˙enien˙ e, R. AI-Driven Innovations in Building Energy Management Systems: A Review of Potential Applications and Energy Savings. Energies 2024, 17, 4277.
  • Rane, N. L., Choudhary, S. P., Rane, J. Artificial Intelligence and Machine Learning in Renewable and Sustainable Energy Strategies: A Critical Review and Future Perspectives. Partners Universal International Innovation Journal (PUIIJ), 2024, 2(3).
  • Entezari, A., Aslani, A., Zahedi, R., Noorollahi, Y. Artificial intelligence and machine learning in energy systems: A bibliographic perspective. Energy Strategy Reviews, 2023, 45, 101017.
  • Raihan, A. A comprehensive review of artificial intelligence and machine learning applications in the energy sector. Journal of Technology Innovations and Energy, 2023, 2(4), 608.
Toplam 61 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Yenilenebilir Enerji Sistemleri
Bölüm Derleme
Yazarlar

Ervanur Midilli 0009-0006-2895-8925

Gönderilme Tarihi 25 Haziran 2025
Kabul Tarihi 17 Aralık 2025
Yayımlanma Tarihi 22 Aralık 2025
Yayımlandığı Sayı Yıl 2025 Cilt: 11 Sayı: 1

Kaynak Göster

APA Midilli, E. (2025). Applications of artificial intelligence methods in renewable energy systems. International Journal of Energy Applications and Technologies, 11(1), 86-99. https://doi.org/10.31593/ijeat.1727157
AMA Midilli E. Applications of artificial intelligence methods in renewable energy systems. International Journal of Energy Applications and Technologies. Aralık 2025;11(1):86-99. doi:10.31593/ijeat.1727157
Chicago Midilli, Ervanur. “Applications of artificial intelligence methods in renewable energy systems”. International Journal of Energy Applications and Technologies 11, sy. 1 (Aralık 2025): 86-99. https://doi.org/10.31593/ijeat.1727157.
EndNote Midilli E (01 Aralık 2025) Applications of artificial intelligence methods in renewable energy systems. International Journal of Energy Applications and Technologies 11 1 86–99.
IEEE E. Midilli, “Applications of artificial intelligence methods in renewable energy systems”, International Journal of Energy Applications and Technologies, c. 11, sy. 1, ss. 86–99, 2025, doi: 10.31593/ijeat.1727157.
ISNAD Midilli, Ervanur. “Applications of artificial intelligence methods in renewable energy systems”. International Journal of Energy Applications and Technologies 11/1 (Aralık2025), 86-99. https://doi.org/10.31593/ijeat.1727157.
JAMA Midilli E. Applications of artificial intelligence methods in renewable energy systems. International Journal of Energy Applications and Technologies. 2025;11:86–99.
MLA Midilli, Ervanur. “Applications of artificial intelligence methods in renewable energy systems”. International Journal of Energy Applications and Technologies, c. 11, sy. 1, 2025, ss. 86-99, doi:10.31593/ijeat.1727157.
Vancouver Midilli E. Applications of artificial intelligence methods in renewable energy systems. International Journal of Energy Applications and Technologies. 2025;11(1):86-99.