Enhancing Photovoltaic Power Forecasting through Hybrid Deep Learning Models: A CNN-RNN Approach for Grid Stability and Renewable Energy Optimization
Main Article Content
Abstract
This paper addresses the critical need for accurate photovoltaic (PV) power generation predictions to ensure efficient grid integration and management, especially considering the variability and intermittency of solar power. By exploring advanced deep learning techniques, including Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), and a hybrid CNN-RNN model, the study aims to enhance the accuracy and reliability of solar power forecasts. The CNN model achieved an accuracy of 0.84, while the RNN reached 0.94, with the highest accuracy of 0.99 attained by the hybrid CNN-RNN model. These models provide vital tools for mitigating fluctuations in solar power output, improving grid stability, and optimizing energy distribution. The study contributes to the advancement of renewable energy forecasting, helping to ensure a more sustainable and reliable energy future, while also supporting efforts to reduce CO2 emissions and combat climate change.
Article Details
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
-
Attribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
-
ShareAlike — If you remix, transform, or build upon the material, you must distribute your contributions under the same license as the original.
- No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.
References
Abuella, M., & Chowdhury, B. (2015). Solar power probabilistic forecasting by using multiple linear regression analysis. SoutheastCon 2015,
Bouvrie, J. (2006). Notes on convolutional neural networks.
Elsaraiti, M., & Merabet, A. (2022). Solar power forecasting using deep learning techniques. IEEE access, 10, 31692-31698.
Graves, A., & Schmidhuber, J. (2005). Framewise phoneme classification with bidirectional LSTM networks. Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005.,
Gu, J., Wang, Z., Kuen, J., Ma, L., Shahroudy, A., Shuai, B., Liu, T., Wang, X., Wang, G., & Cai, J. (2018). Recent advances in convolutional neural networks. Pattern recognition, 77, 354-377.
Hoffmann, J., Navarro, O., Kastner, F., Janßen, B., & Hubner, M. (2017). A survey on CNN and RNN implementations. PESARO 2017: The Seventh International Conference on Performance, Safety and Robustness in Complex Systems and Applications,
Hopfield, J. J. (1982). Neural networks and physical systems with emergent collective computational abilities. Proceedings of the national academy of sciences, 79(8), 2554-2558.
Lee, W., Kim, K., Park, J., Kim, J., & Kim, Y. (2018). Forecasting solar power using long-short term memory and convolutional neural networks. IEEE access, 6, 73068-73080.
Liu, Y., Dong, H., Wang, X., & Han, S. (2019). Time series prediction based on temporal convolutional network. 2019 IEEE/ACIS 18th International conference on computer and information science (ICIS),
Long, H., Zhang, Z., & Su, Y. (2014). Analysis of daily solar power prediction with data-driven approaches. Applied Energy, 126, 29-37.
Perera, M., De Hoog, J., Bandara, K., Senanayake, D., & Halgamuge, S. (2024). Day-ahead regional solar power forecasting with hierarchical temporal convolutional neural networks using historical power generation and weather data. Applied Energy, 361, 122971.
Yesilbudak, M., Çolak, M., & Bayindir, R. (2016). A review of data mining and solar power prediction. 2016 IEEE International Conference on Renewable Energy Research and Applications (ICRERA),
Zeng, J., & Qiao, W. (2013). Short-term solar power prediction using a support vector machine. Renewable energy, 52, 118-127.