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Solar Irradiance Prediction for Efficient Solar Power Generation Using Artificial Intelligence
Abstract
Solar energy is one of the most promising and promising renewable energy sources to meet the world’s energy demand while minimizing greenhouse gas emissions. However, the intermittent and variable nature of solar irradiance as a result of meteorological factors presents serious challenges in grid integration and planning of energy. Accurate prediction of the solar irradiance is a must to optimize photovoltaic (PV) system performance, maintain grid stability and to enable efficient energy resources management. The present paper gives a comprehensive review of the artificial intelligence (AI) techniques for prediction of solar irradiance and the machine learning and deep learning approaches are considered. We analyze some of the basic AI approaches like Artificial Neural Networks (ANN), Support Vector Machine (SVM), Random Forest and on the deep learning, we explore Convolutional Neural Network (CNN), Long Short Term Memory (LSTM) architectures among other types. Additionally, we examine hybrid models of AI that utilize differ- ent techniques together to try and improve the accuracy of the predictions. The various approaches to the forecasting process are compared in the study, according to the performance measures such as the Root Mean Squared Error, RMSE, Mean Absolute Error, MAE and the coefficient of determination R2. Our results indicate that deep learning models or specifically CNN-LSTM based models have efficient predictive capability as compared to conventional machine learning approaches. This type of research helps to give practical insights to researchers and energy planners and grid operators about the choice and implementation of the best AI-based forecasting models. The combination of advanced artificial intelligence (AI) with meteorological data from real-time observations holds great potential for the better integration of renewable energy into energy supply systems and for facilitating the shift to a sustainable energy system.
References
A. Barik, L. Jain, and M. Vyas, “A multi-component hybrid AI model for prediction of solar power generation,” IEEE Access, 2026, doi: 10.1109/EPREC66546.2026.11412181.
M. S. Naveed, I. Iqbal, M. F. Hanif, J. Xiao, X. Liu, and J. Mi, “Enhanced accuracy in solar irradiance forecasting through machine learning stack-based ensemble approach,” Renewable Energy, 2025, doi: 10.1080/15435075.2025.2450468.
M. Baloch, M. S. Honnurvali, A. Kabbani, T. Ahmed, S. T. Chauhdary, and M. S. Saeed, “Solar energy forecasting framework using Prophet based machine learning model,” Energies, vol. 18, no. 1, p. 205, 2025.
D. El-Shahat and A. Tolba, “A hybridized CNN-LSTM-MLP-KNN model for short-term solar irradiance forecasting,” Smart and Intelligent Journal, 2025, doi: 10.61356/smij.2025.10448.
V. Gupta, “Performance analysis of artificial intelligence models for solar radiation forecasting,” Int. J. Intelligent Robotics and Applications, vol. 5, no. 4, 2025.
A. Tandon, A. Awasthi, K. Pattnayak, A. Tandon, T. Choudhury, and K. Kotecha, “Machine learning-driven solar irradiance prediction: Advancing renewable energy in Rajasthan,” SN Applied Sciences, vol. 7, p. 106, 2025.
S. Mishra, D. Pandey, and S. Bhardwaj, “Forecasting of solar irradiance using machine learning techniques,” in Proc. IEEE SPARC Conf., 2024,pp. 1–8.
T. Magesh, R. Thangavelu, M. Nivasini, R. Abishek, K. Yeshwanth, and F. Gladwin, “Prediction of solar power irradiance using machine learning and deep learning for smart grid integration,” in Proc. IEEE RAEEUCCI, 2024, pp. 1–8.
O. O. Apeh and N. I. Nwulu, “Machine learning approach for short- and long-term global solar irradiance prediction,” J. Emerging and Experimental Electricity, vol. 7, no. 1, 2024.
U. Ercan and A. Koc¸er, “Prediction of solar irradiance with machine learning methods using satellite data,” Renewable Energy Focus, 2024, doi: 10.1080/15435075.2024.2305857.
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