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@article{207430,
        author = {Arpita Swamy and Shruti Modi and Dr. P. Sripal Reddy},
        title = {Deep Learning-Based Solar Irradiance Forecasting},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {13},
        number = {3},
        pages = {833-836},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=207430},
        abstract = {The increasing demand for renewable energy has made accurate solar irradiance forecasting an essential requirement for improving the performance of photovoltaic (PV) systems. Solar irradiance is highly influenced by changing weather conditions such as temperature, humidity, cloud cover, wind speed, and atmospheric pressure, making its prediction a challenging task. This paper presents a deep learning-based approach for forecasting solar irradiance using historical meteorological and solar energy data. The proposed model employs deep neural networks to capture complex nonlinear relationships between environmental parameters and solar irradiance. By learning from large datasets, the model provides accurate and reliable predictions that support efficient energy generation, grid management, and optimal utilization of solar power systems. The forecasting results can assist energy providers in reducing power fluctuations, improving operational planning, and increasing the overall efficiency of renewable energy resources. Experimental analysis indicates that the proposed deep learning model achieves better prediction accuracy than conventional machine learning methods, demonstrating its effectiveness for real-world solar energy forecasting applications.},
        keywords = {Deep Learning, Photovoltaic Systems, Renewable Energy, Solar Energy Forecasting, Solar Irradiance, Weather Prediction.},
        month = {August},
        }