Research Article
BibTex RIS Cite

Deep Learning Methods in Energy Systems: A Renewable Energy Perspective

Year 2026, Volume: 14 , 50 - 62 , 28.03.2026

Neşe Budak Ziyadanoğulları

https://doi.org/10.17694/bajece.1887617
https://izlik.org/JA34MY23TH

Abstract

This paper presents a comprehensive review of deep learning applications in energy systems with a particular focus on renewable-energy-based power systems. The rapid deployment of photovoltaic (PV) and wind generation introduces significant uncertainty into power system operation and planning. Accurate forecasting of renewable generation and load, advanced energy management strategies for renewable-rich microgrids, and reliable fault detection and predictive maintenance schemes for PV plants and wind turbines are essential to guarantee secure and economic operation. In recent years, deep neural networks, convolutional neural networks (CNN), recurrent neural networks (RNN) such as long short-term memory (LSTM) and gated recurrent units (GRU), and deep reinforcement learning (DRL) algorithms have achieved state-of-the-art performance in these tasks. This review first outlines the main deep learning architectures and the characteristics of data in energy and renewable energy systems. It then surveys applications in PV and wind power forecasting, load forecasting in smart grids, DRL-based energy management in renewable-rich microgrids, and fault detection and predictive maintenance in PV and wind plants. Emerging trends such as generative models for data augmentation, physics-informed learning and explainable artificial intelligence (XAI) are also discussed. The paper concludes by highlighting open challenges related to data quality, generalization, computational cost and model interpretability, and by outlining promising directions for future research.

Keywords

Renewable Energy, deep learning photovoltaic power forecasting wind power forecasting load forecasting microgrids deep reinforcement learning fault detection

References

  • [1] Abdel-Nasser, M. Mahmoud, K. (2019). Accurate photovoltaic power forecasting models using deep LSTM-RNN, Neural Computing and Applications, 31, pp. 2727-2740. https://doi.org/10.1007/s00521-017-3225-z
  • [2] Lim, S.-C.; Huh, J.-H.; Hong, S.-H.; Park, C.-Y.; Kim, J.-C, (2022). Solar power forecasting using CNN-LSTM hybrid model, Energies, 15(21), 8233 https://doi.org/10.3390/en15218233.
  • [3] Hussain, S. et al. (2022). A hybrid deep learning-based network for photovoltaic power forecasting, Complexity Article ID 704060, https://doi.org/10.1155/2022/7040601.
  • [4] Ma, J. et al. (2025).Integrated CNN-LSTM for photovoltaic power prediction based on spatio-temporal feature fusion, Engineering Reports 7:e13088 1 of 10. https://doi.org/10.1002/eng2.13088.
  • [5] Ning Z. Bowen S. Mingming X. Lei P. Guang F. (2024) Enhancing photovoltaic power prediction using a CNN-LSTM-attention hybrid model with Bayesian hyperparameter optimization, Global Energy Interconnection, vol. 7, issue 5, 667 - 681. https://doi.org/10.1016/j.gloei.2024.10.005
  • [6] Kumar, et al. (2025). Enhanced deep-learning-based forecasting of solar photovoltaic generation for critical weather conditions, Clean Energy, 9(2) pp. 150-160. https://doi.org/10.1093/ce/zkae114
  • [7] Sun Y. et al. (2024). CNN-LSTM-AM: A power prediction model for offshore wind turbines, Ocean Engineering, 301(4):117598.https://doi.org/10.1016/j.oceaneng.2024.117598
  • [8] Ren, J. et al. (2022). A CNN-LSTM-LightGBM based short-term wind power prediction method based on attention mechanism, Energy Reports, 8, pp. 437-443. https://doi.org/10.1016/j.egyr.2022.02.206
  • [9] Garg, S. et al. (2023). A CNN encoder-decoder LSTM model for sustainable wind power predictive analytics, Sustainable Computing: Informatics and Systems, 38 100869. https://doi.org/10.1016/j.suscom.2023.100869.
  • [10] Avin, L.M. Vinodha, D. Jenefa, J. Sambandam, R.K. Vetriveeran, D. Swamy, C.M. (2025) Enhancing Reliability and Accuracy in Wind Energy Forecast Using CNN-LSTM Hybrid Model In: Potnuru, S. Behera, A. Han, H. Talpa Sai, P.S. (eds) Intelligent Manufacturing and Energy Sustainability. ICIMES 2024. Smart Innovation, Systems and Technologies, vol 114. Springer, Singapore. https://doi.org/10.1007/978-981-96-4718-7_4
  • [11] Peng et al. (2025)Short-term hydropower generation prediction model for run-of-river hydropower plants considering hydrometeorological factors, Renewable Energy, 254, 123790. https://doi.org/10.1016/j.renene.2025.123790
  • [12] Ayub et al. (2022). Prediction of process parameters for the integrated biomass gasification power plant using artificial neural network, Frontiers in Energy Research. Volume 10, 894875. https://doi.org/10.3389/fenrg.2022.894875
  • [13] Safarian et al. (2020). Artificial neural network integrated with thermodynamic equilibrium modeling of downdraft biomass gasification-power production plant, Energy, vol. 213 118800. https://doi.org/10.1016/j.energy.2020.118800
  • [14] Zhang et al. (2024). Short-Term Prediction Model of Wave Energy Converter Generation Power Based on CNN-BiLSTM-DELA Integration, Electronics 13(21), 4163; https://doi.org/10.3390/electronics13214163.
  • [15] Zheng et al. (2023). A hybrid framework for forecasting power generation of multiple renewable energy sources, Renewable and Sustainable Energy Reviews, vol. 172, 113046. https://doi.org/10.1016/j.rser.2022.113046
  • [16] Tolasa, D.G. (2025) Neural Network Based Micro-grid Integration of Hybrid PV and Wind Energy, American Journal of Electrical Power and Energy Systems, vol. 14, no. 2, pp. 20-27. https://doi.org/10.11648/j.epes.20251402.11
  • [17] Puzhuang et al. (2025). A novel multi-task learning framework TBM with shared and task-specific features fusion in regional wind-solar-load joint forecasting, Energy Conversion and Management: X, vol. 28, 101346. https://doi.org/10.1016/j.ecmx.2025.101346
  • [18] Syed D. et al. (2021). Deep learning-based self-approach in smart grid with clustering and consumption pattern recognition, IEEE Access ,pp. (99):1-1. https://doi.org/10.1109/ACCESS.2021.3071654
  • [19] Kwon, B.S. et al. (2020). Short-term load forecasting based on deep neural networks using LSTM layer, Journal of Electrical Engineering & Technology, 15 pp. 1501-1509. https://doi.org/10.1007/s42835-020-00424-7
  • [20] Yazici, I. et al. (2022). Deep-learning-based short-term electricity load forecasting: A real case study, Engineering Applications of Artificial Intelligence, 109, 104645. https://doi.org/10.1016/j.engappai.2021.104645
  • [21] Xiong, B. et al. (2025). Deep reinforcement learning for optimal microgrid energy management with renewable energy and electric vehicle integration, Applied Soft Computing, 176, 113180. https://doi.org/10.1016/j.asoc.2025.113180
  • [22] Antonio, C.R. et al. (2024). Optimization of a photovoltaic-battery system using deep reinforcement learning and load forecasting, Energy and AI, 16, 100347. https://doi.org/10.1016/j.egyai.2024.100347
  • [23] Lissa, P. et al. (2021). Deep reinforcement learning for home energy management system control, Energy and AI, 3, 100043. https://doi.org/10.1016/j.egyai.2020.100043
  • [24] Seghiour, A. et al. (2023). Deep learning method based on autoencoder neural network applied to faults detection and diagnosis of photovoltaic system, Simulation Modelling Practice and Theory, 123, 102704. https://doi.org/10.1016/j.simpat.2022.102704
  • [25] A. Hadrawi, Syafaruddin, Y. S. Akil and D. Utamidewi, (2024). "A Deep Autoencoder-Based Method for Detecting Mismatch Faults in Photovoltaic Modules," 2024 International Conference on Electrical Engineering and Computer Science (ICECOS), Palembang, Indonesia pp. 1-6. https://doi.org/10.1109/ICECOS63900.2024.10791197
  • [26] Masita, K. Hasan, A. Shongwe, T. & Hilal, H. A. (2025). Deep learning in defects detection of PV modules: A review. Solar Energy Advances, 5, Article 100090. https://doi.org/10.1016/j.seja.2025.100090
  • [27] Xiang, L. et al. (2021). Fault detection of wind turbine based on SCADA data analysis using CNN and LSTM with attention mechanism, Measurement, 175, 109094. https://doi.org/10.1016/j.measurement.2021.109094
  • [28] Wang, H. et al. (2025). Wind turbine fault diagnosis with imbalanced SCADA data using generative adversarial networks, Energies, 18(5), 1158. https://doi.org/10.3390/en18051158
  • [29] Zhang, L. et al. (2023). A nearly end-to-end deep learning approach to fault diagnosis of wind turbine gearboxes under nonstationary conditions, Engineering Applications of Artificial Intelligence, 119, 105735. https://doi.org/10.1016/j.engappai.2023.105735
  • [30] Boucher, A. et al. (2023). Assessment of machine and deep learning approaches for fault detection and diagnosis in photovoltaic systems using infrared thermography, Remote Sensing, 15 (6) ,1686. https://doi.org/10.3390/rs15061686
  • [31] Chang, Z. Han, T. (2024). Prognostics and health management of photovoltaic systems based on deep learning: A state-of-the-art review and future perspectives, Renewable and Sustainable Energy Reviews, 205, 114861. https://doi.org/10.1016/j.rser.2024.114861
  • [32] Chandrasekaran, R. Paramasivan, S.K. (2025). Advances in Deep Learning Techniques for Short-Term Energy Load Forecasting Applications: A Review, Archives of Computational Methods in Engineering, 32, pp. 663-692. https://doi.org/10.1007/s11831-024-10155-x
  • [33] Yu, J. et al. (2024). Deep Learning Models for PV Power Forecasting: Review, Energies, 17, 16, 3973. https://doi.org/10.3390/en17163973
  • [34] Manzano, E.A. et al. (2025). A Systematic Review of Wind Energy Forecasting Models Based on Deep Neural Networks, Wind, 5 4, 29. https://doi.org/10.3390/wind5040029
  • [35] Haq, I.U. et al. (2025). Machine Learning Approaches for Wind Power Forecasting: A Comparative Analysis, SN Applied Sciences, https://doi.org/10.1007/s42452-025-07675-x
  • [36] Huang, S. et al. (2022). Deep Learning Model-Transformer Based Wind Power Forecasting Approach, Frontiers in Energy Research, 10, 1055683. https://doi.org/10.3389/fenrg.2022.1055683
  • [37] El-Banby, G.M. et al. (2023). Photovoltaic System Fault Detection Techniques: A Review, Neural Computing and Applications, https://doi.org/10.1007/s00521-023-09041-7
  • [38] Yem Souhe, F.G. et al. (2024). Optimized Forecasting of Photovoltaic Power Generation Using a Hybrid SVM-GRU Model, Electrical Engineering, https://doi.org/10.1007/s00202-024-02492-8
  • [39] Namoune, A. et al. (2025). A Dual-Approach Machine Learning and Deep Learning Framework for Enhanced Fault Detection in Photovoltaic Systems: Incorporating SDM Parameter Analysis and Thermal Imaging, Journal of Renewable and Sustainable Energy, 17, 3, 033502. https://doi.org/10.1063/5.0264116
  • [40] Bao, G. Xu, R. A (2023). Data-Driven Energy Management Strategy Based on Deep Reinforcement Learning for Microgrid Systems, Cognitive Computation, 15, pp. 739-750. https://doi.org/10.1007/s12559-022-10106-3
  • [41] Chandrasekaran, K. et al. (2020). Deep Learning and Reinforcement Learning Approach on Microgrid, International Transactions on Electrical Energy Systems, 30, 10, e12531. https://doi.org/10.1002/2050-7038.12531
  • [42] Si, C. et al. (2021). Deep Reinforcement Learning Based Home Energy Management System with Devices Operational Dependencies, International Journal of Machine Learning and Cybernetics, 12, pp. 1687-1703. https://doi.org/10.1007/s13042-020-01266-5
  • [43] Amiri, A.F. et al. (2024). Fault Detection and Diagnosis of a Photovoltaic System Based on Deep Learning Using the Combination of a Convolutional Neural Network (CNN) and Bidirectional Gated Recurrent Unit (Bi-GRU), Sustainability, 16 ,3, 1012. https://doi.org/10.3390/su16031012
  • [44] Bougoffa, M. et al. (2025). Hybrid Deep Learning for Fault Diagnosis in Photovoltaic Systems, Machines, 13, 5, 378. https://doi.org/10.3390/machines13050378
  • [45] Abdelsattar, M. et al. (2024). Evaluating Machine Learning and Deep Learning Models for Predicting and Forecasting Wind and Solar Energy Power Generation, PLOS ONE, 19 e0317619. https://doi.org/10.1371/journal.pone.0317619
  • [46] Oliveira-Filho, A. et al. (2025). Wind Turbine SCADA Data Imbalance: A Review of Its Impact on Health Condition Analyses and Mitigation Strategies, Energies, 18, 1, 59. https://doi.org/10.3390/en18010059
  • [47] Santolamazza, A. et al. (2021). A Data-Mining Approach for Wind Turbine Fault Detection Based on SCADA Data Analysis Using Artificial Neural Networks, Energies, 14, 7, 1845. https://doi.org/10.3390/en14071845
  • [48] Karniadakis, G.E. Kevrekidis, I.G. Lu, L. Perdikaris, P. Wang, S. Yang, L. (2021). Physics-informed machine learning, Nature Reviews Physics, 3, 422-440. https://doi.org/10.1038/s42254-021-00314-5
  • [49] LeCun, Y. Bengio, Y. Hinton, G. (2015). Deep learning, Nature, 521, 436-444. https://doi.org/10.1038/nature14539
  • [50] Hochreiter, S. Schmidhuber, J. (1997). Long short-term memory, Neural Computation, 9(8), 1735-1780. https://doi.org/10.1162/neco.1997.9.8.173
  • [51] Rumelhart, D.E. Hinton, G.E. Williams, R.J. (1986). Learning representations by back-propagating errors, Nature, 323, 533-536. https://doi.org/10.1038/323533a0
  • [52] LeCun, Y. Bottou, L. Bengio, Y. Haffner, P. (1998). Gradient-based learning applied to document recognition, Proceedings of the IEEE, 86(11), 2278-2324. https://doi.org/10.1109/5.726791
  • [53] He, K. Zhang, X. Ren, S. Sun, J. 2016. Deep residual learning for image recognition, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), https://doi.org/10.1109/CVPR.2016.90
  • [54] Hinton, G.E. Salakhutdinov, R.R. (2006). Reducing the dimensionality of data with neural networks, Science, 313(5786) ,504-507. https://doi.org/10.1126/science.1127647
  • [55] Mnih, V. et al. (2015). Human-level control through deep reinforcement learning, Nature, 518, 529-533. https://doi.org/10.1038/nature14236 [56] Silver, D. et al. (2016). Mastering the game of Go with deep neural networks and tree search, Nature, 529, 484-489. https://doi.org/10.1038/nature16961
  • [57] Raissi, M. Perdikaris, P. Karniadakis, G.E. (2019). Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations, Journal of Computational Physics, 378, 686-707. https://doi.org/10.1016/j.jcp.2018.10.045
  • [58] Arrieta, A.B. et al. (2020). Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI, Information Fusion, 58, 82-115. https://doi.org/10.1016/j.inffus.2019.12.012
  • [59] Ribeiro, M.T. Singh, S. Guestrin, C. (2016). "Why should I trust you?" Explaining the predictions of any classifier, Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, https://doi.org/10.1145/2939672.2939778
  • [60] Wu, Z. Pan, S. Chen, F. Long, G. Zhang, C. Yu, P.S. (2021). A comprehensive survey on graph neural networks, IEEE Transactions on Neural Networks and Learning Systems, 32(1), 4-24. https://doi.org/10.1109/TNNLS.2020.2978386
  • [61] Scarselli, F. Gori, M. Tsoi, A.C. Hagenbuchner, M. Monfardini, G. (2009). The graph neural network model, IEEE Transactions on Neural Networks, 20(1), 61-80. https://doi.org/10.1109/TNN.2008.2005605
  • [62] Pan, S.J. Yang, Q. (2010). A survey on transfer learning, IEEE Transactions on Knowledge and Data Engineering, 22(10) 1345-1359. https://doi.org/10.1109/TKDE.2009.191 [63] Kairouz, P. et al. (2021). Advances and open problems in federated learning, Foundations and Trends in Machine Learning, 14(1-2), 1-210. https://doi.org/10.1561/22000083 [64] Breiman, L. Random Forests, (2001). Machine Learning, 45, 5-32. https://doi.org/10.1023/A:1010933404324
  • [65] Cortes, C. Vapnik, V. (1995). Support-vector networks, Machine Learning, 20, 273-297. https://doi.org/10.1007/BF00994018
  • [66] Jordan, M.I. Mitchell, T.M. (2015). Machine learning: Trends, perspectives, and prospects, Science, 349(6245), 255-260. https://doi.org/10.1126/science.aaa8415
  • [67] Schuster, M. Paliwal, K.K. (1997). Bidirectional recurrent neural networks, IEEE Transactions on Signal Processing, 45(11) ,2673-2681. https://doi.org/10.1109/78.650093
  • [68] Chen, T. Guestrin, C. (2016). XGBoost: A scalable tree boosting system, Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, https://doi.org/10.1145/2939672.2939785
  • [69] Zeng, P. et al. (2019). Dynamic Energy Management of a Microgrid Using Approximate Dynamic Programming and Deep Recurrent Neural Network Learning, IEEE Transactions on Smart Grid, 10, 4, pp. 4435-4445. https://doi.org/10.1109/TSG.2018.2859821
  • [70] Cybenko, G. (1989). Approximation by superpositions of a sigmoidal function, Mathematics of Control, Signals and Systems, 2, 303-314. https://doi.org/10.1007/BF02551274
  • [71] Hornik, K. Stinchcombe, M. White, H. (1989). Multilayer feedforward networks are universal approximators, Neural Networks, 2(5), 359-366. https://doi.org/10.1016/0893-6080(89)90020-8
  • [72] Bengio, Y. Simard, P. Frasconi, P. (1994). Learning long-term dependencies with gradient descent is difficult, IEEE Transactions on Neural Networks, 5(2), 157-166. https://doi.org/10.1109/72.279181
  • [73] Liu, D. et al. (2023). Deep Reinforcement Learning for Real-Time Economic Energy Management of Microgrid Systems Considering Uncertainty, Frontiers in Energy Research, 11, 1163053. https://doi.org/10.3389/fenrg.2023.1163053
  • [74] Caruana, R. Multitask Learning, (1997). Machine Learning, 28, 41-75. https://doi.org/10.1023/A:1007379606734
  • [75] Szegedy, C. Liu, W. Jia, Y. Sermanet, P. Reed, S. Anguelov, D. Erhan, D. Vanhoucke, V. Rabinovich, A. (2015). Going deeper with convolutions, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), https://doi.org/10.1109/CVPR.2015.7298594.

Enerji Sistemlerinde Derin Öğrenme Yöntemleri: Yenilenebilir Enerji Odaklı Bir İnceleme

Year 2026, Volume: 14 , 50 - 62 , 28.03.2026

Neşe Budak Ziyadanoğulları

https://doi.org/10.17694/bajece.1887617
https://izlik.org/JA34MY23TH

Abstract

Bu makale, özellikle yenilenebilir enerjiye dayalı güç sistemlerine odaklanarak, enerji sistemlerinde derin öğrenme uygulamalarının kapsamlı bir incelemesini sunmaktadır. Fotovoltaik (PV) ve rüzgar enerjisi üretiminin hızlı bir şekilde yaygınlaşması, güç sistemi işletimi ve planlamasına önemli belirsizlikler getirmektedir. Yenilenebilir enerji üretimi ve yükünün doğru tahmin edilmesi, yenilenebilir enerji açısından zengin mikro şebekeler için gelişmiş enerji yönetim stratejileri ve PV santralleri ve rüzgar türbinleri için güvenilir arıza tespiti ve öngörücü bakım şemaları, güvenli ve ekonomik işletimi garanti etmek için gereklidir. Son yıllarda, derin sinir ağları, evrişimsel sinir ağları (CNN), uzun kısa süreli bellek (LSTM) ve geçitli tekrarlayan birimler (GRU) gibi tekrarlayan sinir ağları (RNN) ve derin pekiştirmeli öğrenme (DRL) algoritmaları bu görevlerde en iyi performansı elde etmiştir. Bu inceleme öncelikle temel derin öğrenme mimarilerini ve enerji ve yenilenebilir enerji sistemlerindeki verilerin özelliklerini özetlemektedir. Ardından, fotovoltaik ve rüzgar enerjisi tahminleri, akıllı şebekelerde yük tahminleri, yenilenebilir enerji açısından zengin mikro şebekelerde DRL tabanlı enerji yönetimi ve fotovoltaik ve rüzgar santrallerinde arıza tespiti ve öngörücü bakım uygulamaları incelenmektedir. Veri artırma için üretken modeller, fiziksel bilgiye dayalı öğrenme ve açıklanabilir yapay zeka (XAI) gibi ortaya çıkan trendler de ele alınmaktadır. Makale, veri kalitesi, genelleme, hesaplama maliyeti ve model ile ilgili açık zorlukları vurgulayarak sona ermektedir.

Keywords

yenilenebilir enerji derin öğrenme fotovoltaik enerji tahmini rüzgar enerjisi tahmini yük tahmini mikroşebekeler derin pekiştirmeli öğrenme arıza tespiti

References

  • [1] Abdel-Nasser, M. Mahmoud, K. (2019). Accurate photovoltaic power forecasting models using deep LSTM-RNN, Neural Computing and Applications, 31, pp. 2727-2740. https://doi.org/10.1007/s00521-017-3225-z
  • [2] Lim, S.-C.; Huh, J.-H.; Hong, S.-H.; Park, C.-Y.; Kim, J.-C, (2022). Solar power forecasting using CNN-LSTM hybrid model, Energies, 15(21), 8233 https://doi.org/10.3390/en15218233.
  • [3] Hussain, S. et al. (2022). A hybrid deep learning-based network for photovoltaic power forecasting, Complexity Article ID 704060, https://doi.org/10.1155/2022/7040601.
  • [4] Ma, J. et al. (2025).Integrated CNN-LSTM for photovoltaic power prediction based on spatio-temporal feature fusion, Engineering Reports 7:e13088 1 of 10. https://doi.org/10.1002/eng2.13088.
  • [5] Ning Z. Bowen S. Mingming X. Lei P. Guang F. (2024) Enhancing photovoltaic power prediction using a CNN-LSTM-attention hybrid model with Bayesian hyperparameter optimization, Global Energy Interconnection, vol. 7, issue 5, 667 - 681. https://doi.org/10.1016/j.gloei.2024.10.005
  • [6] Kumar, et al. (2025). Enhanced deep-learning-based forecasting of solar photovoltaic generation for critical weather conditions, Clean Energy, 9(2) pp. 150-160. https://doi.org/10.1093/ce/zkae114
  • [7] Sun Y. et al. (2024). CNN-LSTM-AM: A power prediction model for offshore wind turbines, Ocean Engineering, 301(4):117598.https://doi.org/10.1016/j.oceaneng.2024.117598
  • [8] Ren, J. et al. (2022). A CNN-LSTM-LightGBM based short-term wind power prediction method based on attention mechanism, Energy Reports, 8, pp. 437-443. https://doi.org/10.1016/j.egyr.2022.02.206
  • [9] Garg, S. et al. (2023). A CNN encoder-decoder LSTM model for sustainable wind power predictive analytics, Sustainable Computing: Informatics and Systems, 38 100869. https://doi.org/10.1016/j.suscom.2023.100869.
  • [10] Avin, L.M. Vinodha, D. Jenefa, J. Sambandam, R.K. Vetriveeran, D. Swamy, C.M. (2025) Enhancing Reliability and Accuracy in Wind Energy Forecast Using CNN-LSTM Hybrid Model In: Potnuru, S. Behera, A. Han, H. Talpa Sai, P.S. (eds) Intelligent Manufacturing and Energy Sustainability. ICIMES 2024. Smart Innovation, Systems and Technologies, vol 114. Springer, Singapore. https://doi.org/10.1007/978-981-96-4718-7_4
  • [11] Peng et al. (2025)Short-term hydropower generation prediction model for run-of-river hydropower plants considering hydrometeorological factors, Renewable Energy, 254, 123790. https://doi.org/10.1016/j.renene.2025.123790
  • [12] Ayub et al. (2022). Prediction of process parameters for the integrated biomass gasification power plant using artificial neural network, Frontiers in Energy Research. Volume 10, 894875. https://doi.org/10.3389/fenrg.2022.894875
  • [13] Safarian et al. (2020). Artificial neural network integrated with thermodynamic equilibrium modeling of downdraft biomass gasification-power production plant, Energy, vol. 213 118800. https://doi.org/10.1016/j.energy.2020.118800
  • [14] Zhang et al. (2024). Short-Term Prediction Model of Wave Energy Converter Generation Power Based on CNN-BiLSTM-DELA Integration, Electronics 13(21), 4163; https://doi.org/10.3390/electronics13214163.
  • [15] Zheng et al. (2023). A hybrid framework for forecasting power generation of multiple renewable energy sources, Renewable and Sustainable Energy Reviews, vol. 172, 113046. https://doi.org/10.1016/j.rser.2022.113046
  • [16] Tolasa, D.G. (2025) Neural Network Based Micro-grid Integration of Hybrid PV and Wind Energy, American Journal of Electrical Power and Energy Systems, vol. 14, no. 2, pp. 20-27. https://doi.org/10.11648/j.epes.20251402.11
  • [17] Puzhuang et al. (2025). A novel multi-task learning framework TBM with shared and task-specific features fusion in regional wind-solar-load joint forecasting, Energy Conversion and Management: X, vol. 28, 101346. https://doi.org/10.1016/j.ecmx.2025.101346
  • [18] Syed D. et al. (2021). Deep learning-based self-approach in smart grid with clustering and consumption pattern recognition, IEEE Access ,pp. (99):1-1. https://doi.org/10.1109/ACCESS.2021.3071654
  • [19] Kwon, B.S. et al. (2020). Short-term load forecasting based on deep neural networks using LSTM layer, Journal of Electrical Engineering & Technology, 15 pp. 1501-1509. https://doi.org/10.1007/s42835-020-00424-7
  • [20] Yazici, I. et al. (2022). Deep-learning-based short-term electricity load forecasting: A real case study, Engineering Applications of Artificial Intelligence, 109, 104645. https://doi.org/10.1016/j.engappai.2021.104645
  • [21] Xiong, B. et al. (2025). Deep reinforcement learning for optimal microgrid energy management with renewable energy and electric vehicle integration, Applied Soft Computing, 176, 113180. https://doi.org/10.1016/j.asoc.2025.113180
  • [22] Antonio, C.R. et al. (2024). Optimization of a photovoltaic-battery system using deep reinforcement learning and load forecasting, Energy and AI, 16, 100347. https://doi.org/10.1016/j.egyai.2024.100347
  • [23] Lissa, P. et al. (2021). Deep reinforcement learning for home energy management system control, Energy and AI, 3, 100043. https://doi.org/10.1016/j.egyai.2020.100043
  • [24] Seghiour, A. et al. (2023). Deep learning method based on autoencoder neural network applied to faults detection and diagnosis of photovoltaic system, Simulation Modelling Practice and Theory, 123, 102704. https://doi.org/10.1016/j.simpat.2022.102704
  • [25] A. Hadrawi, Syafaruddin, Y. S. Akil and D. Utamidewi, (2024). "A Deep Autoencoder-Based Method for Detecting Mismatch Faults in Photovoltaic Modules," 2024 International Conference on Electrical Engineering and Computer Science (ICECOS), Palembang, Indonesia pp. 1-6. https://doi.org/10.1109/ICECOS63900.2024.10791197
  • [26] Masita, K. Hasan, A. Shongwe, T. & Hilal, H. A. (2025). Deep learning in defects detection of PV modules: A review. Solar Energy Advances, 5, Article 100090. https://doi.org/10.1016/j.seja.2025.100090
  • [27] Xiang, L. et al. (2021). Fault detection of wind turbine based on SCADA data analysis using CNN and LSTM with attention mechanism, Measurement, 175, 109094. https://doi.org/10.1016/j.measurement.2021.109094
  • [28] Wang, H. et al. (2025). Wind turbine fault diagnosis with imbalanced SCADA data using generative adversarial networks, Energies, 18(5), 1158. https://doi.org/10.3390/en18051158
  • [29] Zhang, L. et al. (2023). A nearly end-to-end deep learning approach to fault diagnosis of wind turbine gearboxes under nonstationary conditions, Engineering Applications of Artificial Intelligence, 119, 105735. https://doi.org/10.1016/j.engappai.2023.105735
  • [30] Boucher, A. et al. (2023). Assessment of machine and deep learning approaches for fault detection and diagnosis in photovoltaic systems using infrared thermography, Remote Sensing, 15 (6) ,1686. https://doi.org/10.3390/rs15061686
  • [31] Chang, Z. Han, T. (2024). Prognostics and health management of photovoltaic systems based on deep learning: A state-of-the-art review and future perspectives, Renewable and Sustainable Energy Reviews, 205, 114861. https://doi.org/10.1016/j.rser.2024.114861
  • [32] Chandrasekaran, R. Paramasivan, S.K. (2025). Advances in Deep Learning Techniques for Short-Term Energy Load Forecasting Applications: A Review, Archives of Computational Methods in Engineering, 32, pp. 663-692. https://doi.org/10.1007/s11831-024-10155-x
  • [33] Yu, J. et al. (2024). Deep Learning Models for PV Power Forecasting: Review, Energies, 17, 16, 3973. https://doi.org/10.3390/en17163973
  • [34] Manzano, E.A. et al. (2025). A Systematic Review of Wind Energy Forecasting Models Based on Deep Neural Networks, Wind, 5 4, 29. https://doi.org/10.3390/wind5040029
  • [35] Haq, I.U. et al. (2025). Machine Learning Approaches for Wind Power Forecasting: A Comparative Analysis, SN Applied Sciences, https://doi.org/10.1007/s42452-025-07675-x
  • [36] Huang, S. et al. (2022). Deep Learning Model-Transformer Based Wind Power Forecasting Approach, Frontiers in Energy Research, 10, 1055683. https://doi.org/10.3389/fenrg.2022.1055683
  • [37] El-Banby, G.M. et al. (2023). Photovoltaic System Fault Detection Techniques: A Review, Neural Computing and Applications, https://doi.org/10.1007/s00521-023-09041-7
  • [38] Yem Souhe, F.G. et al. (2024). Optimized Forecasting of Photovoltaic Power Generation Using a Hybrid SVM-GRU Model, Electrical Engineering, https://doi.org/10.1007/s00202-024-02492-8
  • [39] Namoune, A. et al. (2025). A Dual-Approach Machine Learning and Deep Learning Framework for Enhanced Fault Detection in Photovoltaic Systems: Incorporating SDM Parameter Analysis and Thermal Imaging, Journal of Renewable and Sustainable Energy, 17, 3, 033502. https://doi.org/10.1063/5.0264116
  • [40] Bao, G. Xu, R. A (2023). Data-Driven Energy Management Strategy Based on Deep Reinforcement Learning for Microgrid Systems, Cognitive Computation, 15, pp. 739-750. https://doi.org/10.1007/s12559-022-10106-3
  • [41] Chandrasekaran, K. et al. (2020). Deep Learning and Reinforcement Learning Approach on Microgrid, International Transactions on Electrical Energy Systems, 30, 10, e12531. https://doi.org/10.1002/2050-7038.12531
  • [42] Si, C. et al. (2021). Deep Reinforcement Learning Based Home Energy Management System with Devices Operational Dependencies, International Journal of Machine Learning and Cybernetics, 12, pp. 1687-1703. https://doi.org/10.1007/s13042-020-01266-5
  • [43] Amiri, A.F. et al. (2024). Fault Detection and Diagnosis of a Photovoltaic System Based on Deep Learning Using the Combination of a Convolutional Neural Network (CNN) and Bidirectional Gated Recurrent Unit (Bi-GRU), Sustainability, 16 ,3, 1012. https://doi.org/10.3390/su16031012
  • [44] Bougoffa, M. et al. (2025). Hybrid Deep Learning for Fault Diagnosis in Photovoltaic Systems, Machines, 13, 5, 378. https://doi.org/10.3390/machines13050378
  • [45] Abdelsattar, M. et al. (2024). Evaluating Machine Learning and Deep Learning Models for Predicting and Forecasting Wind and Solar Energy Power Generation, PLOS ONE, 19 e0317619. https://doi.org/10.1371/journal.pone.0317619
  • [46] Oliveira-Filho, A. et al. (2025). Wind Turbine SCADA Data Imbalance: A Review of Its Impact on Health Condition Analyses and Mitigation Strategies, Energies, 18, 1, 59. https://doi.org/10.3390/en18010059
  • [47] Santolamazza, A. et al. (2021). A Data-Mining Approach for Wind Turbine Fault Detection Based on SCADA Data Analysis Using Artificial Neural Networks, Energies, 14, 7, 1845. https://doi.org/10.3390/en14071845
  • [48] Karniadakis, G.E. Kevrekidis, I.G. Lu, L. Perdikaris, P. Wang, S. Yang, L. (2021). Physics-informed machine learning, Nature Reviews Physics, 3, 422-440. https://doi.org/10.1038/s42254-021-00314-5
  • [49] LeCun, Y. Bengio, Y. Hinton, G. (2015). Deep learning, Nature, 521, 436-444. https://doi.org/10.1038/nature14539
  • [50] Hochreiter, S. Schmidhuber, J. (1997). Long short-term memory, Neural Computation, 9(8), 1735-1780. https://doi.org/10.1162/neco.1997.9.8.173
  • [51] Rumelhart, D.E. Hinton, G.E. Williams, R.J. (1986). Learning representations by back-propagating errors, Nature, 323, 533-536. https://doi.org/10.1038/323533a0
  • [52] LeCun, Y. Bottou, L. Bengio, Y. Haffner, P. (1998). Gradient-based learning applied to document recognition, Proceedings of the IEEE, 86(11), 2278-2324. https://doi.org/10.1109/5.726791
  • [53] He, K. Zhang, X. Ren, S. Sun, J. 2016. Deep residual learning for image recognition, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), https://doi.org/10.1109/CVPR.2016.90
  • [54] Hinton, G.E. Salakhutdinov, R.R. (2006). Reducing the dimensionality of data with neural networks, Science, 313(5786) ,504-507. https://doi.org/10.1126/science.1127647
  • [55] Mnih, V. et al. (2015). Human-level control through deep reinforcement learning, Nature, 518, 529-533. https://doi.org/10.1038/nature14236 [56] Silver, D. et al. (2016). Mastering the game of Go with deep neural networks and tree search, Nature, 529, 484-489. https://doi.org/10.1038/nature16961
  • [57] Raissi, M. Perdikaris, P. Karniadakis, G.E. (2019). Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations, Journal of Computational Physics, 378, 686-707. https://doi.org/10.1016/j.jcp.2018.10.045
  • [58] Arrieta, A.B. et al. (2020). Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI, Information Fusion, 58, 82-115. https://doi.org/10.1016/j.inffus.2019.12.012
  • [59] Ribeiro, M.T. Singh, S. Guestrin, C. (2016). "Why should I trust you?" Explaining the predictions of any classifier, Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, https://doi.org/10.1145/2939672.2939778
  • [60] Wu, Z. Pan, S. Chen, F. Long, G. Zhang, C. Yu, P.S. (2021). A comprehensive survey on graph neural networks, IEEE Transactions on Neural Networks and Learning Systems, 32(1), 4-24. https://doi.org/10.1109/TNNLS.2020.2978386
  • [61] Scarselli, F. Gori, M. Tsoi, A.C. Hagenbuchner, M. Monfardini, G. (2009). The graph neural network model, IEEE Transactions on Neural Networks, 20(1), 61-80. https://doi.org/10.1109/TNN.2008.2005605
  • [62] Pan, S.J. Yang, Q. (2010). A survey on transfer learning, IEEE Transactions on Knowledge and Data Engineering, 22(10) 1345-1359. https://doi.org/10.1109/TKDE.2009.191 [63] Kairouz, P. et al. (2021). Advances and open problems in federated learning, Foundations and Trends in Machine Learning, 14(1-2), 1-210. https://doi.org/10.1561/22000083 [64] Breiman, L. Random Forests, (2001). Machine Learning, 45, 5-32. https://doi.org/10.1023/A:1010933404324
  • [65] Cortes, C. Vapnik, V. (1995). Support-vector networks, Machine Learning, 20, 273-297. https://doi.org/10.1007/BF00994018
  • [66] Jordan, M.I. Mitchell, T.M. (2015). Machine learning: Trends, perspectives, and prospects, Science, 349(6245), 255-260. https://doi.org/10.1126/science.aaa8415
  • [67] Schuster, M. Paliwal, K.K. (1997). Bidirectional recurrent neural networks, IEEE Transactions on Signal Processing, 45(11) ,2673-2681. https://doi.org/10.1109/78.650093
  • [68] Chen, T. Guestrin, C. (2016). XGBoost: A scalable tree boosting system, Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, https://doi.org/10.1145/2939672.2939785
  • [69] Zeng, P. et al. (2019). Dynamic Energy Management of a Microgrid Using Approximate Dynamic Programming and Deep Recurrent Neural Network Learning, IEEE Transactions on Smart Grid, 10, 4, pp. 4435-4445. https://doi.org/10.1109/TSG.2018.2859821
  • [70] Cybenko, G. (1989). Approximation by superpositions of a sigmoidal function, Mathematics of Control, Signals and Systems, 2, 303-314. https://doi.org/10.1007/BF02551274
  • [71] Hornik, K. Stinchcombe, M. White, H. (1989). Multilayer feedforward networks are universal approximators, Neural Networks, 2(5), 359-366. https://doi.org/10.1016/0893-6080(89)90020-8
  • [72] Bengio, Y. Simard, P. Frasconi, P. (1994). Learning long-term dependencies with gradient descent is difficult, IEEE Transactions on Neural Networks, 5(2), 157-166. https://doi.org/10.1109/72.279181
  • [73] Liu, D. et al. (2023). Deep Reinforcement Learning for Real-Time Economic Energy Management of Microgrid Systems Considering Uncertainty, Frontiers in Energy Research, 11, 1163053. https://doi.org/10.3389/fenrg.2023.1163053
  • [74] Caruana, R. Multitask Learning, (1997). Machine Learning, 28, 41-75. https://doi.org/10.1023/A:1007379606734
  • [75] Szegedy, C. Liu, W. Jia, Y. Sermanet, P. Reed, S. Anguelov, D. Erhan, D. Vanhoucke, V. Rabinovich, A. (2015). Going deeper with convolutions, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), https://doi.org/10.1109/CVPR.2015.7298594.
There are 72 citations in total.

Details

Primary Language English
Subjects Software Engineering (Other)
Journal Section Research Article
Authors

Neşe Budak Ziyadanoğulları 0000-0002-2203-0177

Submission Date February 12, 2026
Acceptance Date March 27, 2026
Publication Date March 28, 2026
DOI https://doi.org/10.17694/bajece.1887617
IZ https://izlik.org/JA34MY23TH
Published in Issue Year 2026 Volume: 14

Cite

APA Budak Ziyadanoğulları, N. (2026). Deep Learning Methods in Energy Systems: A Renewable Energy Perspective. Balkan Journal of Electrical and Computer Engineering, 14, 50-62. https://doi.org/10.17694/bajece.1887617

Aim & Scope

Balkan Journal of Electrical and Computer Engineering - (BAJECE)
(An International Peer Reviewed, indexed and Open Access Journal)

The journal publishes original papers in the extensive field of Electrical-electronics and Computer engineering. It accepts contributions which are fundamental for the development of electrical engineering and its applications, including overlaps to physics. Manuscripts on both theoretical and experimental work are welcome. Review articles and letters to the editors are also included.

Application areas include (but are not limited to):


Electrical & Electronics Engineering

Computer Engineering

Software Engineering

Biomedical Engineering

Electrical Power Engineering

Electric analysis & Mathematical models.

Electrical Control Engineering

Signal and Image Processing

Communications

Networking

Sensors, Actuators, Remote sensing, Consumer electronics, Fiber optics

Radar and Sonar systems

Artificial Intelligence and its Applications, Expert Systems

Medical imaging, Biomedical analysis and its applications

Computer vision, Pattern recognition,

Robotics, industrial automation

Renewable Energy


IMPORTANT INFORMATION FOR AUTHORS




Manuscript will be considered only on the understanding that it is not currently being submitted to other journals.

Submission of an article implies that the results described has not been published previously (except in the form of an abstract or as part of a published lecture or academic thesis), that it is not under consideration for publication elsewhere, that its publication is approved by all authors and that, if accepted, it will not be published elsewhere in the same form, in English or in any other language, without the written consent of the Publisher.

Authors are obliged to follow remarks and comments of the reviewers, as well as to follow Instructions for preparing manuscripts and technical remarks and corrections of the Editorial Board.

Papers will be published in English, and authors are obliged to submit papers free of typing errors (please use Spell Checking).

Editorial Board has right to make small lector corrections and to shorten text of the paper, which would not influence author's ideas and presentation. After computer lay-out of the paper, authors will obtain text as .pdf file for approval.

The manuscripts, not exceeding 12 pages (for review papers maximum 20 pages), including figures and tables, should be submitted in electronic form only in MS Word (.doc file) or LaTex format (.tex file), and submit to submission system.

For each author full name, personal (or first) name(s) and family (or last) name(s), and affiliation, have to be given. Family or last name(s) has to be written in capital letters. In the case of more than one author, the name of corresponding author should be indicated, with full postal address, e-mail address, and affiliation.

Each paper has to be written according to following order: title, author(s), affiliation(s), abstract, key words, body of the text with numerated sections and subsections including introduction, conclusions, acknowledgment (if necessary), nomenclature, and references. Pages must have page numbers.

Each author and coauthor have the possibility to subscribe for 10 reprints of their paper (for details, see our subscription page).

Authors note that paper can not be withdrawn at any condition once it is accepted. The Authors may not publish his/her The Author may not publish his/her contribution anywhere else without the prior written permission of the publisher unless it has been changed substantially. The Author warrants that his/her contribution is original, except for such excerpts from copyrighted works as may be included with the permission of the copyright holder and author thereof, that it contains no libellous statements, and does not infringe on any copyright, trademark, patent, statutory right, or propriety right of others. The Author signs for and accepts responsibility for releasing this material on behalf of any and all co-authors. In return for these rights:

All proprietary rights other than copyrights, such as patent rights.

The right to use all or part of this article, including tables and figures in future works of their own, provided that the proper acknowledgment is made to the Publisher as copyright holder.

The right to make copies of this article for his/her own use, but not for sale.

It is the responsibility of each author to ensure that papers submitted to the journal are written with ethical standards in mind, concerning plagiarism. Please note that all submissions are thoroughly checked for plagiarism. If an attempt at plagiarism is found in a published paper, the authors will be asked to issue a written apology to the authors of the original material. Any paper which shows obvious signs of plagiarism will be automatically rejected and its authors may be banned for duration of 10 years from publishing in Journal. The authors will receive proper notification if such a situation arises.

This paper has not been published in the same form elsewhere.

It will not be submitted anywhere else for publication prior to acceptance/rejection by BAJECE.

Ethics;

Papers must be submitted on the understanding that they have not been published elsewhere and are not currently under consideration by another journal. The submitting author is responsible for ensuring that the article’s publication has been approved by all the other coauthors. When an author discovers a significant error or inaccuracy in his/her own published work, it is the author's obligation to notify the publisher and cooperate with the editor to retract or correct the paper. It is also the authors’ responsibility to ensure that the articles emanating from a particular institution are submitted with the approval of the necessary institution. Only an acknowledgment from the editorial office officially establishes the date of receipt. Further correspondence and proofs will be sent to the author(s) before publication unless otherwise indicated. It is a condition of submission of a paper that the authors permit editing of the paper for readability.

BAJECE is committed to following the Code of Conduct and Best Practice Guidelines of COPE (Committee on Publication Ethics) . It is a duty of our editors to follow Cope Guidance for Editors and our peer-reviewers must follow COPE Ethical Guidelines for Peer Reviewers .

If you have any questions, please contact the relevant editorial office, or Balkan Journal of Electrical and Computer Engineering (BAJECE)' ethics representative: bajece@hotmail.com

Download a PDF version of the Ethics and Policies [PDF,392KB].

Reviewer Process Information

BAJECE employs a single-blind peer review process to ensure scientific quality, fairness, and transparency. In this review model, reviewers are able to see the authors’ names and affiliations, while authors do not have access to the reviewers’ identities. This approach allows reviewers to provide objective, detailed, and constructive feedback while maintaining their anonymity.

All submitted manuscripts are first evaluated by the Editorial Board for relevance, structure, and adherence to journal guidelines. Papers that meet the initial criteria are then assigned to at least two independent reviewers who are experts in the related research area. Reviewers assess manuscripts based on originality, technical accuracy, clarity, methodology, and scientific contribution.

Authors are required to revise their papers according to reviewers’ comments and suggestions within the given time frame. The final publication decision—acceptance, revision, or rejection—is made by the Editor-in-Chief after considering the reviewers’ recommendations and the scientific merit of the manuscript.

This single-blind review process ensures impartial evaluation, promotes academic integrity, and supports high-quality scientific publication standards in BAJECE.

Papers must be submitted on the understanding that they have not been published elsewhere and are not currently under consideration by another journal. The submitting author is responsible for ensuring that the article’s publication has been approved by all the other coauthors. When an author discovers a significant error or inaccuracy in his/her own published work, it is the author's obligation to notify the publisher and cooperate with the editor to retract or correct the paper. It is also the authors’ responsibility to ensure that the articles emanating from a particular institution are submitted with the approval of the necessary institution. Only an acknowledgment from the editorial office officially establishes the date of receipt. Further correspondence and proofs will be sent to the author(s) before publication unless otherwise indicated. It is a condition of submission of a paper that the authors permit editing of the paper for readability.

BAJECE is committed to following the Code of Conduct and Best Practice Guidelines of COPE (Committee on Publication Ethics) . It is a duty of our editors to follow Cope Guidance for Editors and our peer-reviewers must follow COPE Ethical Guidelines for Peer Reviewers .

If you have any questions, please contact the relevant editorial office, or Balkan Journal of Electrical and Computer Engineering (BAJECE)' ethics representative: bajece@hotmail.com

Download a PDF version of the Ethics and Policies [PDF,392KB].

Reviewer Process Information

BAJECE employs a single-blind peer review process to ensure scientific quality, fairness, and transparency. In this review model, reviewers are able to see the authors’ names and affiliations, while authors do not have access to the reviewers’ identities. This approach allows reviewers to provide objective, detailed, and constructive feedback while maintaining their anonymity.

All submitted manuscripts are first evaluated by the Editorial Board for relevance, structure, and adherence to journal guidelines. Papers that meet the initial criteria are then assigned to at least two independent reviewers who are experts in the related research area. Reviewers assess manuscripts based on originality, technical accuracy, clarity, methodology, and scientific contribution.

Authors are required to revise their papers according to reviewers’ comments and suggestions within the given time frame. The final publication decision—acceptance, revision, or rejection—is made by the Editor-in-Chief after considering the reviewers’ recommendations and the scientific merit of the manuscript.

This single-blind review process ensures impartial evaluation, promotes academic integrity, and supports high-quality scientific publication standards in BAJECE.

Citation Indexes

TR Dizin
International Innovative Journal of Impact Factor (IIJIF)
CrossRef
Open Access Library (oalib)
Index Copernicus
CiteFactor
DRJI
Ulrich's Periodicals Directory

Other Indexes

Index Copernicus

EDITORIAL BOARD MEMBERS

Musa YILMAZ
Tahir Cetin AKINCI
Md SHAFIULLAH
Ümit TERZI
Behnam MOHAMMADI-IVATLOO
Kalpana CHAUHAN
Gouthamkumar NADAKUDITI
Hemant Kumar GIANEY

All articles published by BAJECE are licensed under the Creative Commons Attribution 4.0 International License. This permits anyone to copy, redistribute, remix, transmit and adapt the work provided the original work and source is appropriately cited.Creative Commons Lisansı