AI in Solar Forecasting: Advanced Machine Learning Techniques for Photovoltaic Power Prediction

• Author(s): Archana. P. Haral ; Manisha. R. Shiledar; Suriyakala A V
• Paper ID: 1714508
• Page: 1589-1592
• Published Date: 24-02-2026
• Published In: Iconic Research And Engineering Journals
• Publisher: IRE Journals
• e-ISSN: 2456-8880
• Volume/Issue: Volume 9 Issue 8 February-2026

Abstract

Solar energy forecasting is critical for grid stability and renewable energy integration. This paper reviews artificial intelligence techniques applied to solar forecasting, focusing on advances from 2023-2026. We examine deep learning architectures including LSTM networks, CNNs, Transformer-based models, and hybrid approaches. Analysis of 242 studies reveals that hybrid CNN-LSTM models achieve 15-30% MAE reductions compared to standalone models. Deep learning excels for short-term predictions, while ensemble approaches benefit day-ahead forecasts. Key challenges include data quality, computational complexity, and model generalization. This review synthesizes methodologies, performance metrics, and future directions including transfer learning and physics-informed neural networks

Keywords

CNN, Deep Learning, Hybrid Model, LSTM, Photovoltaic Power Prediction, Renewable Energy, Solar Forecasting

Citations

IRE Journals:
Archana. P. Haral , Manisha. R. Shiledar, Suriyakala A V "AI in Solar Forecasting: Advanced Machine Learning Techniques for Photovoltaic Power Prediction " Iconic Research And Engineering Journals Volume 9 Issue 8 2026 Page 1589-1592 https://doi.org/10.64388/IREV9I8-1714508

IEEE:
Archana. P. Haral , Manisha. R. Shiledar, Suriyakala A V "AI in Solar Forecasting: Advanced Machine Learning Techniques for Photovoltaic Power Prediction " Iconic Research And Engineering Journals, 9(8) https://doi.org/10.64388/IREV9I8-1714508