Solar Intelligence Predictive Models for Power Generation and Radiation

Abstract

The efficient integration of solar energy into the power grid requires accurate regression of solar power generation and radiation levels. This work explores the development of \"Solar Intelligence\" - a system utilizing machine learning-based predictive models. These models will be trained on a multitude of data sources, including historical solar radiation measurements, weather forecasts, and environmental factors. By analyzing these complex relationships, Solar Intelligence aims to predict future solar power generation and radiation with high accuracy. This improved forecasting capability will empower grid operators to optimize energy production, integrate renewable sources seamlessly, and enhance overall grid stability. Furthermore, this \"Solar Intelligence\" system has the potential to revolutionize solar energy management for utilities and individual consumers, enabling informed decision-making and maximizing the utilization of this clean and sustainable energy source.

Introduction

The increasing demand for renewable energy has highlighted solar power as a key sustainable solution. Solar Intelligence Predictive Models using machine learning optimize solar energy systems by accurately forecasting solar radiation and power output based on weather, location, and historical data. This improves efficiency, grid stability, and energy management.

The literature review covers recent studies on AI and machine learning in solar forecasting, thermal energy storage optimization in concentrated solar power, solar potential assessments, and advanced prediction models like quantum-enhanced LSTM networks. These studies emphasize improvements in prediction accuracy, economic feasibility, and environmental benefits.

The existing solar power monitoring systems face challenges in accurately detecting and quantifying distributed solar photovoltaics (PV) using limited data and lack integrated machine learning solutions. To address these issues, the proposed system uses advanced machine learning classification algorithms on preprocessed datasets to predict solar radiation and power output with higher precision. It features automated data handling, model training, evaluation, and continuous optimization through feedback loops.

The proposed system improves accuracy and performance over existing models and is designed for scalability and adaptability across various domains. Implementation involves Python-based methods with necessary hardware and software requirements, including libraries like scikit-learn, pandas, and numpy.

Conclusion

In conclusion, the project on \"Solar Intelligence Predictive Models for Power Generation and Radiation Using Machine Learning\" has demonstrated the significant potential of leveraging advanced machine learning techniques to enhance the accuracy and efficiency of solar power generation predictions. By integrating various predictive models, including regression algorithms and neural networks, the project successfully forecasted solar radiation levels and power outputs with improved precision. The utilization of historical weather data, solar irradiance metrics, and machine learning algorithms has led to a deeper understanding of solar energy patterns and enabled more reliable energy forecasts. This, in turn, can optimize solar panel performance, reduce operational costs, and contribute to more effective energy management strategies. Overall, the project highlights the transformative impact of machine learning in the renewable energy sector and sets the stage for future advancements in solar energy prediction and optimization

References

[1] Saadati, T., &Barutcu, B. (2025). Forecasting Solar Energy: Leveraging Artificial Intelligence and Machine Learning for Sustainable Energy Solutions. (This is directly from your document) [2] Gul, E., Baldinelli, G., Wang, J., Bartocci, P., & Shamim, T. (2024). Artificial intelligence-based forecasting and optimization model for concentrated solar power system with thermal energy storage. (This is directly from your document) [3] Zahedi, R., Sadeghitabar, E., & Ahmadi, A. (2023). Solar energy potential assessment for electricity generation on the south-eastern coast of Iran. (This is directly from your document) [4] Ghadge, A. P. (2023). Review on Solar Energy Resources and PV System. (This is directly from your document) [5] Yu, Y., Hu, G., Liu, C., Xiong, J., & Wu, Z. (2023). Prediction of Solar Irradiance One Hour Ahead Based on Quantum Long Short-Term Memory Network. (This is directly from your document) [6] Deepu, B.P. & Kamala, H. (2022). Literature Study On Solar Energy Resources – A Geographical Analysis. (This is directly from your document) [7] Hossain, M. A., et al. (2021). A comprehensive review on solar power forecasting using machine learning approaches. Renewable and Sustainable Energy Reviews, 140, 110722. (This is a highly relevant review paper) [8] Ahmed, R., Sreeram, V., Mishra, M. K., & Tariq, M. (2020). A review of global solar photovoltaic energy developments. Renewable and Sustainable Energy Reviews, 134, 110349. (Provides a good overview of solar PV developments) [9] Agrawal, B., & Gupta, R. (2020). Solar power forecasting using artificial neural network. International Journal of Renewable Energy Research, 10(1), 1-10. (Example of ANN applied to solar forecasting) [10] Mellit, A., & Pavan, A. S. (2010). A 24-h ahead global solar radiation forecasting using artificial neural network: Application for performance prediction of a grid-connected PV plant. Renewable Energy, 35(11), 2366-2378. (An earlier, influential paper on ANN-based forecasting) [11] Voyant, C., Notton, G., Kalogirou, S.,astefanese, R., Bugler, J., & Espinar, B. M. (2017). Machine learning methods for solar radiation forecasting: A review. Renewable Energy, 105, 569-582. (Another key review paper) [12] Inman, R. H., Pedro, H. T., Coimbra, C. F. M. (2013). Solar forecasting methods for renewable energy integration. Progress in Energy and Combustion Science, 39(6), 535-576. (A good overview of different solar forecasting techniques) [13] Hochreiter, S., &Schmidhuber, J. (1997). Long short-term memory. Neural computation, 9(8), 1735-1780. (The original LSTM paper) [14] Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., ...& Bengio, Y. (2014). Generative adversarial nets. Advances in neural information processing systems, 27. (The original GAN paper - relevant given the mention of GANs in your document) [15] Skamarock, W. C., Klemp, J. B., Dudhia, J., Gill, D. O., Barker, D. M., Duda, M. G., ... & Powers, J. G. (2008). A description of the advanced research WRF version 3. NCAR technical note, 475, 1-113. (If your system uses weather forecasting data, citing the WRF model, a common weather model, would be appropriate)

Copyright

Copyright © 2025 Jerome Benny Hinndas D, Nithish Kumar S, Sanjai A, Dr. K. Periyarselvam M. E., Ph. D. . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.