What question did this study set out to answer?

The aim is to review AI techniques for forecasting solar energy and their effectiveness in prediction accuracy.

February 27, 2026Open Access

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

Key Points

The aim is to review AI techniques for forecasting solar energy and their effectiveness in prediction accuracy.
Reviewed 242 studies on solar energy forecasting using AI techniques.
Analyzed deep learning architectures like LSTM, CNN, and hybrid models.
Assessed performance metrics such as mean absolute error (MAE).
Hybrid CNN-LSTM models showed 15-30% reductions in MAE compared to standalone models.
Deep learning techniques are particularly effective for short-term solar predictions.
Ensemble methods enhance accuracy for day-ahead forecasts.

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

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Cite This Study

Haral et al. (Tue,) studied this question.

synapsesocial.com/papers/69a134b8ed1d949a99abe3f5 https://doi.org/https://doi.org/10.64388/irev9i8-1714508

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