Open Access

Intelligent Decision Algorithm for Photovoltaic Power Generation Control Based on Feature Neural Computing Modeling

   | May 05, 2024

Cite

Alrifaey, M., Lim, W. H., Ang, C. K., et al. (2022). Hybrid deep learning model for fault detection and classification of grid-connected photovoltaic system. *IEEE Access, 10*, 13852-13869. Search in Google Scholar

Gaviria, J. F., Narváez, G., Guillen, C., et al. (2022). Machine learning in photovoltaic systems: A review. *Renewable Energy, 196*, 298-318. Search in Google Scholar

Akhter, M. N., Mekhilef, S., Mokhlis, H., et al. (2022). A hybrid deep learning method for an hour ahead power output forecasting of three different photovoltaic systems. *Applied Energy, 307*, 118185. Search in Google Scholar

Pei, J., Li, S., Yu, Z., et al. (2023). Federated learning encounters 6g wireless communication in the scenario of internet of things. *IEEE Communications Standards Magazine, 7*(1), 94-100. Search in Google Scholar

Mellit, A., & Kalogirou, S. (2022). Assessment of machine learning and ensemble methods for fault diagnosis of photovoltaic systems. *Renewable Energy, 184*, 1074-1090. Search in Google Scholar

Abualigah, L., Zitar, R. A., Almotairi, K. H., et al. (2022). Wind, solar, and photovoltaic renewable energy systems with and without energy storage optimization: A survey of advanced machine learning and deep learning techniques. *Energies, 15*(2), 578. Search in Google Scholar

Pei, J., Liu, W., Li, J., Wang, L., & Liu, C. (2024). A Review of Federated Learning Methods in Heterogeneous scenarios. *IEEE Transactions on Consumer Electronics.* Search in Google Scholar

Van Gompel, J., Spina, D., & Develder, C. (2022). Satellite based fault diagnosis of photovoltaic systems using recurrent neural networks. *Applied Energy, 305*, 117874. Search in Google Scholar

Eskandari, A., Aghaei, M., Milimonfared, J., et al. (2023). A weighted ensemble learning-based autonomous fault diagnosis method for photovoltaic systems using genetic algorithm. *International Journal of Electrical Power & Energy Systems, 144*, 108591. Search in Google Scholar

Agga, A., Abbou, A., Labbadi, M., et al. (2022). CNN-LSTM: An efficient hybrid deep learning architecture for predicting short-term photovoltaic power production. *Electric Power Systems Research, 208*, 107908. Search in Google Scholar

Mansouri, M., Trabelsi, M., Nounou, H., et al. (2021). Deep learning-based fault diagnosis of photovoltaic systems: A comprehensive review and enhancement prospects. *IEEE Access, 9*, 126286-126306. Search in Google Scholar

Rasoulian, A., Saghafi, H., Abbasian, M., et al. (2023). Deep learning based model predictive control of active filter inverter as interface for photovoltaic generation. *IET Renewable Power Generation, 17*(13), 3151-3162. Search in Google Scholar

Hammoudi, Y., Idrissi, I., Boukabous, M., et al. (2022). Review on maintenance of photovoltaic systems based on deep learning and internet of things. *Indonesian Journal of Electrical Engineering and Computer Science, 26*(2), 1060-1072. Search in Google Scholar

eISSN:
2444-8656
Language:
English
Publication timeframe:
Volume Open
Journal Subjects:
Life Sciences, other, Mathematics, Applied Mathematics, General Mathematics, Physics