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Journals / Gazi Mühendislik Bilimleri Dergisi / 2026 / Volume: 12 Issue: 1

A Hybrid CNN–LSTM Model with Attention Mechanism for Photovoltaic Power Estimation

Musab Coskun(BINGOL UNIVERSITY)0000-0001-8323-2330
Onur Polat(BİNGÖL ÜNİVERSİTESİ)0000-0001-9313-4910
Ferdi Doğan(ADIYAMAN UNIVERSITY)0000-0002-9203-697X
Miktat Aktaş(FIRAT UNIVERSITY)0000-0002-0731-5668
Pages
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Publication Date
2026-04-30
DOI
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

The results demonstrate that high-accuracy short-term forecasting can be achieved using only inverter current data, without the need for weather data.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:In this study, a hybrid deep learning model is proposed for short-term active power estimation using PV (Photovoltaic) inverter current data.
📊
Key Findings & Data:The model was developed to better learn time dependencies and achieve better performance in active power estimation.
💡
Conclusion & Impact:The results demonstrate that high-accuracy short-term forecasting can be achieved using only inverter current data, without the need for weather data.
🔬Design:Modeling & Simulation
📍Scope:2024 Verileri
⚙️Variables:Photovoltaic Power Estimation · Deep Learning · CNN-BiLSTM

Abstract

In this study, a hybrid deep learning model is proposed for short-term active power estimation using PV (Photovoltaic) inverter current data. The model was developed to better learn time dependencies and achieve better performance in active power estimation. It combines convolutional neural networks (CNN) with bidirectional long-short-term memory (BiLSTM) architecture and is developed with Multi-Head Attention and Squeeze-and-Excitation (SE) blocks. In this study, 20 inverter current channels (PV1–PV20) and average, sum, and ratio-based features derived from these currents are used. Samples collected at a 5-minute resolution between May 2024 and July 2025 were subjected to data cleaning and rescaling processes; then, the active power of the next step was estimated based on 60 time-steps of historical data. The model was trained using the AdamW optimization algorithm and Huber loss, and performance values of MAE = 2.14 kW, RMSE = 5.30 kW, MAPE = 8.26%, and R² = 0.963 were achieved. The results demonstrate that high-accuracy short-term forecasting can be achieved using only inverter current data, without the need for weather data. The proposed model offers an effective approach for applications such as grid stability, inverter-based monitoring, energy management, and instantaneous production planning.

🔖

Cite This Article

Coskun, M., Polat, O., Doğan, F., Aktaş, M. (2026). A Hybrid CNN–LSTM Model with Attention Mechanism for Photovoltaic Power Estimation. Gazi Mühendislik Bilimleri Dergisi, Volume: 12 Issue: 1. https://ajindex.com/articles/1397339

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References (75)

  1. A. Jäger-Waldau, "Snapshot of photovoltaics− February 2024," EPJ photovoltaics, vol. 15, p. 21, 2024. doi: 10.1051/epjpv/2024018
  2. G. Masson, A. Jäger-Waldau, I. Kaizuka, J. Lindahl, J. Donoso, and M. de L'Epine, "A Snapshot of the Global PV Market and Industry," in 2025 IEEE 53rd Photovoltaic Specialists Conference (PVSC), 2025: IEEE, pp. 508-510. doi: 10.1109/PVSC57443.2024.10749131
  3. A. Jäger-Waldau, M. Szabó, F. Monforti-Ferrario, H. Bloem, T. Huld, and R. L. Arantegui, "Renewable energy snapshots 2011," JRC Report, 2011. [Online]. Available: http://re.jrc.ec. europa.eu/refsys/pdf/RE_Snapshots_2011 pdf. [Accessed: 3 Oct. 2025].
  4. O. O. Apeh, E. L. Meyer, and O. K. Overen, "Contributions of solar photovoltaic systems to environmental and socioeconomic aspects of national development—A review," Energies, vol. 15, no. 16, p. 5963, 2022. doi: 10.3390/en15165963
  5. F. Doğan, M. Aktaş, N. Olgun, M. Çalışan, and İ. Türkoğlu, "Detection of Cell Damage and Types in Solar Panels: A Comprehensive Review," Dicle Üniversitesi Mühendislik Fakültesi Mühendislik Dergisi, vol. 17, no. 1, 2026. doi: 10.24012/dumf.1704433
  6. M. Aktaş, F. Doğan, and İ. Türkoğlu, "Classification of Solar Cells EL Images with Different Busbars Via Deep Learning Models," Sakarya University Journal of Computer and Information Sciences, vol. 7, no. 2, pp. 217-226, 2024. doi: 10.35377/saucis.1463788
  7. G. C. Mahato, T. R. Choudhury, B. Nayak, D. Debnath, S. B. Santra, and B. Misra, "A review on high PV penetration on smart grid: Challenges and its mitigation using FPPT," in 2021 1st international conference on power electronics and energy (ICPEE), 2021: IEEE, pp. 1-6. doi: 10.1109/ICPEE50452.2021.9358474
  8. K. Liu, Y. Liu, J. Han, W. Zhang, and H. Guo, "Challenges and Solutions in Integrating Large-Scale Photovoltaic Systems with Weak Electrical Grids," in 2023 IEEE 4th China International Youth Conference On Electrical Engineering (CIYCEE), 2023: IEEE, pp. 1-5. doi: 10.1109/CIYCEE59789.2023.10401776
  9. A. Meflah, F. Chekired, N. Drir, and L. Canale, "Accurate Method for Solar Power Generation Estimation for Different PV (Photovoltaic Panels) Technologies," Resources, vol. 13, no. 12, p. 166, 2024. doi: 10.3390/resources13120166
  10. O. Taşdemir, "Photovoltaic Power Prediction with Teaching Learning Based Optimization Algorithm," Gazi University Journal of Science Part A: Engineering and Innovation, vol. 11, no. 4, pp. 780-791, 2024. doi: 10.54287/gujsa.1581828
  11. M. Gao and Y. Fan, "Sigmoid Kernel Canonical Correlation Analysis (SCKCCA) for PV Output Power and Meteorological Factors," in 2023 5th International Conference on Electrical Engineering and Control Technologies (CEECT), 2023: IEEE, pp. 65-69. doi: 10.1109/CEECT59667.2023.10420605
  12. Z. Hou, Y. Zhang, Q. Liu, and X. Ye, "A hybrid machine learning forecasting model for photovoltaic power," Energy Reports, vol. 11, pp. 5125-5138, 2024. doi: 10.1016/j.egyr.2024.04.065
  13. D. Feng, H. Zhang, and Z. Wang, "Hourly photovoltaic power prediction based on signal decomposition and deep learning," in Journal of Physics: Conference Series, 2024, vol. 2728, no. 1: IOP Publishing, p. 012011.
  14. A. B. Owolabi et al., "Hybrid Deep Learning Models for Power Output Forecasting of Grid‐Connected Solar PV Systems: A Monocrystalline and Polycrystalline PV Panel Analysis," International Journal of Energy Research, vol. 2025, no. 1, p. 9925615, 2025. doi: 10.1155/er/9925615
  15. Y. Zhang, C. Qin, A. K. Srivastava, C. Jin, and R. K. Sharma, "Data-driven day-ahead PV estimation using autoencoder-LSTM and persistence model," IEEE Transactions on Industry Applications, vol. 56, no. 6, pp. 7185-7192, 2020. doi: 10.1109/TIA.2020.302574
  16. K. Ait Chaoui, H. EL Fadil, O. Choukai, and O. Ait Omar, "A Wavelet–Attention–Convolution Hybrid Deep Learning Model for Accurate Short-Term Photovoltaic Power Forecasting," Forecasting, vol. 7, no. 3, p. 45, 2025. doi: 10.3390/forecast7030045
  17. L. Rui, W. Ming, S. Wan-Xing, W. Min-Jie, and Z. Qian, "Prediction of Distributed Photovoltaic Power Based on CNN-LSTM-Attention Fusion Model," in 2024 International Symposium on Electrical, Electronics and Information Engineering (ISEEIE), 2024: IEEE, pp. 541-545. doi: 10.1109/ISEEIE62461.2024.00104
  18. F. Jafari, J. Moerschell, and K. Riesen, "Comparison of PV Power Prediction Techniques: From Regression to LSTM Models," in 2025 International Conference on Control, Automation and Diagnosis (ICCAD), 2025: IEEE, pp. 1-6. doi: 10.1109/ICCAD64771.2025.11099459
  19. M. Kayri, I. Kayri, and M. T. Gencoglu, "The performance comparison of Multiple Linear Regression, Random Forest and Artificial Neural Network by using photovoltaic and atmospheric data," in 2017 14th International Conference on Engineering of Modern Electric Systems (EMES), 2017: IEEE, pp. 1-4. doi: 10.1109/EMES.2017.7980368
  20. B. A. Bhatti and R. Broadwater, "Solar photovoltaic output prediction using jackknife regression," in 2018 North American Power Symposium (NAPS), 2018: IEEE, pp. 1-6. doi: 10.1109/NAPS.2018.8600676.
  21. L. Massidda, F. Bettio, and M. Marrocu, "Probabilistic day-ahead prediction of PV generation. A comparative analysis of forecasting methodologies and of the factors influencing accuracy," Solar Energy, vol. 271, p. 112422, 2024. doi: 10.1016/j.solener.2024.112422
  22. P. Ingenhoven, G. Belluardo, and D. Moser, "Comparison of statistical and deterministic smoothing methods to reduce the uncertainty of performance loss rate estimates," IEEE Journal of Photovoltaics, vol. 8, no. 1, pp. 224-232, 2017. doi: 10.1109/JPHOTOV.2017.2762523
  23. W. Zhang, Q. Li, and Q. He, "Application of machine learning methods in photovoltaic output power prediction: A review," Journal of Renewable and Sustainable Energy, vol. 14, no. 2, 2022. doi: 10.1063/5.0082629
  24. X. Jiang, Y. Gou, M. Jiang, L. Luo, and Q. Zhou, "Photovoltaic Power Forecasting with Weather Conditioned Attention Mechanism," Big Data Mining and Analytics, vol. 8, no. 2, pp. 326-345, 2025. doi: 10.26599/BDMA.2024.9020066
  25. A. Ali, R. Jayaraman, E. Azar, and M. Maalouf, "A comparative analysis of machine learning and statistical methods for evaluating building performance: A systematic review and future benchmarking framework," Building and Environment, vol. 252, p. 111268, 2024. doi: 10.1016/j.buildenv.2024.111268
  26. J. Mann, M. Lyons, J. O'Rourke, and S. Davies, "Machine learning or traditional statistical methods for predictive modelling in perioperative medicine: a narrative review," Journal of Clinical Anesthesia, vol. 102, p. 111782, 2025. doi: 10.1016/j.jclinane.2025.111782
  27. A. Buscemi, S. Guarino, V. Di Dio, V. L. Brano, M. Bonomolo, and C. Baglivo, "A Novel ML Approach for Real-Time Prediction of PV Production: Detection and Impact of Shading," in 2025 International Conference on Clean Electrical Power (ICCEP), 2025: IEEE, pp. 223-228. doi: 10.1109/ICCEP65222.2025.11143660
  28. T. A. Nguyen et al., "A Study on Novel Solar Power Forecasting using an XGB-LiGBM-RF Hybrid Model and the L-BFGS-B Optimization Algorithm," Engineering, Technology & Applied Science Research, vol. 15, no. 4, pp. 24516-24522, 2025. doi: 10.48084/etasr.11308
  29. K. Mohamed et al., "Machine Learning-Based Short-Term Photovoltaic Power Forecasting Using Exogenous Meteorological Data," in Advanced Computation Solutions for Energy Efficiency: IGI Global Scientific Publishing, 2025, pp. 167-182. doi: 10.4018/979-8-3373-1220-0.ch008
  30. M. Mamun, A. A. H. Newaz, Y. Arafat, A. Hossain, and M. M. H. Melon, "Utilizing Machine Learning Algorithms to Improve the Accuracy of Solar Energy Yield Predictions Based on Various Input Variables," Nanotechnology in Renewable Energy, vol. 20, no. S15, 2024. doi: 10.62441/nano-ntp.vi.3466
  31. A. A. Yaghoubi, M. Gandomzadeh, A. Gholami, R. Gavagsaz-Ghoachani, M. Zandi, and M. Phattanasak, "Harnessing Machine Learning with Advanced Linear Regression Models to Forecast PV System," in 2024 International Conference on Materials and Energy: Energy in Electrical Engineering (ICOME-EE), 2024: IEEE, pp. 1-4. doi: 10.1109/ICOME-EE64119.2024.10845673
  32. F. Demir, "The performance comparison of machine learning methods for solar PV power prediction," World Journal of Engineering, 2024. doi: 10.1108/WJE-01-2024-0001
  33. M. Tucci, A. Piazzi, and D. Thomopulos, "Machine learning models for regional photovoltaic power generation forecasting with limited plant-specific data," Energies, vol. 17, no. 10, p. 2346, 2024. doi: 10.3390/en17102346
  34. B. Zhou, X. Chen, G. Li, P. Gu, J. Huang, and B. Yang, "Xgboost–sfs and double nested stacking ensemble model for photovoltaic power forecasting under variable weather conditions," Sustainability, vol. 15, no. 17, p. 13146, 2023. doi: 10.3390/su151713146
  35. A. Patel and O. G. Swathika, "Analyzing the Predictive Power of Solar Photovoltaics Using Three Machine Learning Models," in 2024 3rd Odisha International Conference on Electrical Power Engineering, Communication and Computing Technology (ODICON), 2024: IEEE, pp. 1-6. doi: 10.1109/ODICON62106.2024.10797527
  36. B. Girdhani and M. Agrawal, "Prediction of daily photovoltaic plant energy output using machine learning," Energy Sources, Part A: Recovery, Utilization, and Environmental Effects, vol. 46, no. 1, pp. 5904-5924, 2024. doi: 10.1080/15567036.2024.2341975
  37. M. S. S. Islam et al., "Optimizing short-term photovoltaic power forecasting: A novel approach with gaussian process regression and bayesian hyperparameter tuning," Processes, vol. 12, no. 3, p. 546, 2024. doi: 10.3390/pr12030546
  38. K. Ferkous, M. Guermoui, A. Bellaour, T. boulmaiz, and N. Bailek, "Enhancing photovoltaic energy forecasting: a progressive approach using wavelet packet decomposition," Clean Energy, vol. 8, no. 3, pp. 95-108, 2024. doi: 10.1093/ce/zkae027
  39. K. Lv et al., "An integrated method for day-ahead photovoltaic power prediction considering error correction strategy," in 2024 9th Asia Conference on Power and Electrical Engineering (ACPEE), 2024: IEEE, pp. 1477-1483. doi: 10.1109/ACPEE60788.2024.10532561
  40. A. Abdellatif et al., "Forecasting photovoltaic power generation with a stacking ensemble model," Sustainability, vol. 14, no. 17, p. 11083, 2022. doi: 10.3390/su141711083
  41. F. Doğan, S. Oyucu, D. B. Unsal, A. Aksöz, and M. Vafaeipour, "Impact of Environmental Conditions on Renewable Energy Prediction: An Investigation Through Tree-Based Community Learning," Applied Sciences, vol. 15, no. 1, p. 336, 2025. doi: 10.3390/app15010336
  42. F. Doğan, İ. Türkoğlu, "Comparison of deep learning models in terms of multiple object detection on satellite images," Journal of Engineering Research, vol. 10, no. 3, pp. 89-108, 2022. doi: 10.36909/jer.12843
  43. F. Doğan, M. Aktaş, and M. İ. Gürsoy, "Classification of Skin Diseases with Different Deep Learning Models and Comparison of the Performances of the Models," Türk Doğa ve Fen Dergisi, vol. 13, no. 3, pp. 117-123, 2024. doi: 10.46810/tdfd.1502471
  44. K. Yuan, L. Yi, S. Rao, L. Tang, Y. Yu, and J. Jin, "Research on Photovoltaic Power Generation Energy Forecasting Model Driven by Deep Learning," International Journal of Energy, vol. 7, no. 2, pp. 23-27, 2025. doi: 10.54097/6z0p1a57
  45. D. Salman, C. Direkoglu, M. Kusaf, and M. Fahrioglu, "Hybrid deep learning models for time series forecasting of solar power," Neural Computing and Applications, vol. 36, no. 16, pp. 9095-9112, 2024. doi: 10.1007/s00521-024-09558-5
  46. J. Yan, Y. Sha, Y. Zhang, T. Li, and J. Zhang, "An advanced CNN-LSTM-BiLSTM model leveraging attention mechanisms for accurate distributed photovoltaic output prediction," in 2023 3rd International Conference on Energy, Power and Electrical Engineering (EPEE), 2023: IEEE, pp. 261-265. doi: 10.1109/EPEE59859.2023.10351880
  47. D. H. AN and K. N. THANH, "The comparison of GRU and LSTM in solar power generation forecasting application," INTERNATIONAL JOURNAL, vol. 13, no. 1, pp. 1360-1370, 2024. doi: 10.30574/ijsra.2024.13.1.1831
  48. F. Amirteimoury, G. Memarzadeh, H. Noori, F. Keynia, A. Heydari, and A. Fekih, "A Novel Multi-Scale Feature Attention Network-Based Prediction Model for the Accurate Forecasting of Photovoltaic Power Generation," IEEE Access, 2025. doi: 10.1109/ACCESS.2025.3611168
  49. C. Chen, Y. Luo, Y. Wang, and H. Fang, "Photovoltaic Power Prediction Model Based on Dual Domain Decomposition and Dual Network Structure," in 2024 6th International Conference on Energy, Power and Grid (ICEPG), 2024: IEEE, pp. 595-600. doi: 10.1109/ICEPG63230.2024.10776007.
  50. J. Liu, "Power Prediction Based on Improved SSA-CNN-BiLSTM," in 2024 IEEE 7th International Conference on Information Systems and Computer Aided Education (ICISCAE), 2024: IEEE, pp. 636-641. doi: 10.1109/ICISCAE62304.2024.10761436
  51. D. Kunalan, P. S. Krishnan, and N. Permal, "Single Step Multivariate Solar Power Forecasting using Adaptive Learning Rate LSTM Model with Optimized Window Size," in 2023 International Electronics Symposium (IES), 2023: IEEE, pp. 70-76. doi: 10.1109/IES59143.2023.10242461
  52. M. Chiranjeevi, S. Karlamangal, T. Moger, and D. Jena, "Solar irradiation forecast enhancement using hybrid architecture," in 2023 5th International Conference on Energy, Power and Environment: Towards Flexible Green Energy Technologies (ICEPE), 2023: IEEE, pp. 1-6. doi: 10.1109/ICEPE57949.2023.10201489
  53. N. Thipwangmek, N. Suetrong, A. Taparugssanagorn, S. Tangparitkul, and N. Promsuk, "Enhancing short-term solar photovoltaic power forecasting using a hybrid deep learning approach," IEEE Access, 2024. doi: 10.1109/ACCESS.2024.3440035
  54. A. Bhatia, A. Kaul, R. Kumar, and A. Chadda, "Hybrid Model of LSTM and CNN (2D) for Solar Radiation Prediction," in 2025 7th International Conference on Energy, Power and Environment (ICEPE), 2025: IEEE, pp. 1-6. doi: 10.1109/ICEPE65965.2025.11139799
  55. F. Huang, Z. Pan, Z. Huang, Z. Li, L. Chen, and L. Zhang, "Photovoltaic Power Generation Forecasting Based on Attention-CNN-BiLSTM Model," in 2024 4th International Conference on Energy Engineering and Power Systems (EEPS), 2024: IEEE, pp. 368-372. doi: 10.1109/EEPS63402.2024.10804398
  56. Y. Cuan and C. Bai, "Hybrid optimization of photovoltaic power prediction with PSO-CNN-GRU considering seasonal partitioning," in Fourth International Conference on Mechanical, Electronics, and Electrical and Automation Control (METMS 2024), 2024, vol. 13163: SPIE, pp. 577-582. doi: 10.1117/12.3030120
  57. A. Ahsan, A. Zafar, M. A. Afzal, M. Javed, D. Zafar, and M. Ali, "Improved solar power prediction using cnn-lstm models for optimized smart grid performance," Journal of Engineering, Science and Technological Trends, vol. 1, no. 2, 2024. doi: 10.48112/jestt.v1i2c.5
  58. M. Abdelsattar, M. A. Azim, A. AbdelMoety, and A. Emad-Eldeen, "Comparative analysis of deep learning architectures in solar power prediction," Scientific Reports, vol. 15, no. 1, p. 31729, 2025. doi: 10.1038/s41598-025-14908-x
  59. F. Doğan and İ. Türkoğlu, "Derin öğrenme modelleri ve uygulama alanlarına ilişkin bir derleme," Dicle Üniversitesi Mühendislik Fakültesi Mühendislik Dergisi, vol. 10, no. 2, pp. 409-445, 2019. doi: 10.24012/dumf.411130
  60. F. Doğan and İ. Türkoğlu, "Derin öğrenme algoritmalarının yaprak sınıflandırma başarımlarının karşılaştırılması," Sakarya University Journal of Computer and Information Sciences, vol. 1, no. 1, pp. 10-21, 2018.
  61. F. Doğan, "Bitki Hastalıklarının Tespiti için Ön İşlemli Görsel Dönüştürücü Tabanlı bir Yaklaşım," Bilgisayar Bilimleri ve Mühendisliği Dergisi, no. Advanced Online Publication, pp. 10-23, 2026. doi: 10.54525/bbmd.1640557
  62. W. Liu and Z. Mao, "Short-term photovoltaic power forecasting with feature extraction and attention mechanisms," Renewable Energy, vol. 226, p. 120437, 2024. doi: 10.1016/j.renene.2024.120437
  63. L. Jiaoyue, L. Longlong, and M. Xuelian, "Ultra-short term photovoltaic power prediction combining attention mechanism and deep reinforcement learning," in 2024 IEEE 4th International Conference on Electronic Technology, Communication and Information (ICETCI), 2024: IEEE, pp. 759-764. doi: 10.1109/ICETCI61221.2024.10594333
  64. M. Z. Hameed and B. Lamichhane, "Transductive-Transfer Learning Based Deep Neural Networks for Day-Ahead PV Power Forecasting in Smart Grid Application: A Comparative Analysis," in 2023 North American Power Symposium (NAPS), 2023: IEEE, pp. 1-6. doi: 10.1109/NAPS58826.2023.10318736
  65. H. Yu and S. Song, "Ultra-short-term Single-step Photovoltaic Power Prediction based on VMD-Attention-BiLSTM Combined Model," 2024. doi: 10.21203/rs.3.rs-4909901/v1
  66. M. Aslam, S.-J. Lee, S.-H. Khang, and S. Hong, "Two-stage attention over LSTM with Bayesian optimization for day-ahead solar power forecasting," IEEE Access, vol. 9, pp. 107387-107398, 2021. doi: 10.1109/ACCESS.2021.3100105
  67. Y. Zhong, T. He, and Z. Mao, "Enhanced Solar Power Prediction Using Attention-Based DiPLS-BiLSTM Model," Electronics, vol. 13, no. 23, p. 4815, 2024. doi: 10.3390/electronics13234815
  68. J. Li et al., "Photovoltaic Power Prediction Model Based on Temporal Attention," in 2024 4th International Conference on Energy Engineering and Power Systems (EEPS), 2024: IEEE, pp. 444-448. doi: 10.1109/EEPS63402.2024.10804403
  69. Z. Chu and Y. Ruan, "Dynamic Multi-Scale Convolution and BiTCN Enhanced by CPO and Attention for Accurate Ultra-Short-Term PV Power Forecasting," in 2025 2nd International Conference on Artificial Intelligence and Digital Technology (ICAIDT), 2025: IEEE, pp. 498-502. doi: 10.1109/ICAIDT66272.2025.00104
  70. D. Zheng and R. Zhu, "Attention-Enhanced Fusion Network for Precise Ultra-Short-Term PV Power Forecasting," in 2025 IEEE 20th Conference on Industrial Electronics and Applications (ICIEA), 2025: IEEE, pp. 1-6. doi: 10.1109/ICIEA65512.2025.11149151
  71. S.-C. Lim, J.-H. Huh, S.-H. Hong, C.-Y. Park, and J.-C. Kim, "Solar power forecasting using CNN-LSTM hybrid model," Energies, vol. 15, no. 21, p. 8233, 2022. doi: 10.3390/en15218233
  72. Y. Wang, Y. Yao, Q. Zou, K. Zhao, and Y. Hao, "Forecasting a short-term photovoltaic power model based on improved snake optimization, convolutional neural network, and bidirectional long short-term memory network," Sensors, vol. 24, no. 12, p. 3897, 2024. doi: 10.3390/s24123897
  73. J. Yu, X. Li, L. Yang, L. Li, Z. Huang, K. Shen … and D. Du, "Deep learning models for PV power forecasting," Energies, vol. 17, no. 16, p. 3973, 2024. doi: 10.3390/en17163973.
  74. N. Ye, C. Zhi, Y. Yu, S. Lin, and F. Liu, "TCN-LSTM-AM Short-Term Photovoltaic Power Forecasting Model Based on Improved Feature Selection and APO," Sensors, vol. 25, no. 24, p. 7607, 2025. doi: 10.3390/s25247607
  75. M. Aktaş, F. Doğan, and İ. Türkoğlu, "Classification of Solar Cells EL Images with Different Busbars Via Deep Learning Models," Sakarya University Journal of Computer and Information Sciences, vol. 7, no. 2, pp. 217-226, 2024. doi: 10.35377/saucis.1463788