Table 1.
Comparative analysis of PV forecasting methods.
| Sl. No. | Model/Approach | Problem addressed | Achievement | Limitation | Year | References |
|---|---|---|---|---|---|---|
| 1. | Attention-based RNN | Weather-driven variability | Higher short-term accuracy | Low interpretability | 2023 | 12 |
| 2. | Adaptive XGBoost | Feature relevance variation | Lower MAE than standard XGBoost | Weak under fast cloud changes | 2023 | 13 |
| 3. | Physics-informed ML (clear-sky wiht GB) | Physical inconsistency & overfitting | Improved physical consistency | Degrades under abnormal weather | 2023 | 14 |
| 4. | Satellite data with ML | Spatial generalization of PV forecasts | Improved site-to-site transferability | Dependence on satellite data | 2023 | 15 |
| 5. | Transformer-based DL | Capturing long-term temporal dependencies | Improved accuracy over LSTM/CNN | High data and computational demand | 2024 | 16 |
| 6. | Review (DL & hybrid ML) | Lack of consistency in PV forecasting | Identified trends and research gaps | No standardized benchmarks | 2024 | 17 |
| 7. | Feature-selection-based ML | Redundant meteorological features | Reduced error via optimized features | Added preprocessing overhead | 2024 | 18 |
| 8. | Hybrid ML taxonomy | Fragmented hybrid forecasting methods | Structured hybrid classification | Limited reproducibility | 2024 | 19 |
| 9. | DL architectures (CNN, RNN, GNN) | Spatiotemporal dependency modeling | Strong performance with rich data | Poor results under sparse data | 2024 | 20 |
| 10. | Bayesian ensemble ML | Forecast uncertainty quantification | Well-calibrated prediction intervals | Long training time | 2024 | 21 |
| 11. | Probabilistic deep ensemble | Weather-driven forecast uncertainty | Improved reliability via confidence intervals | High computational cost | 2024 | 22 |
| 12. | Transfer-learning ensemble | Data scarcity at new PV sites | Improved cross-site generalization | Climate similarity dependency | 2024 | 23 |
| 13. | SARIMAX–LSTM–XGBoost hybrid | Seasonal and nonlinear variability | Hybrid outperformed single models | Model complexity and interpretability | 2025 | 24 |
| 14. | CNN–LSTM with clustering | Seasonal forecasting errors | Better low-irradiance prediction | High complexity limits real-time use | 2025 | 25 |
| 15. | RF & XGBoost | Short-term PV forecasting accuracy | XGBoost showed robustness | Sensitive to hyperparameter tuning | 2025 | 26 |
| 16. | Ensemble ML (PV & wind) | Generalization across RES types | Ensemble improved robustness | Performance depends on base learners | 2025 | 27 |
| 17. | Physical with ML ensemble | Morning forecast inaccuracy | Improved early-hour prediction | Reduced midday gains | 2025 | 28 |
| 18. | Multi-horizon LightGBM–LSTM ensemble | Intra-day forecast variability | Lower MAE across horizons | Reduced accuracy at minute-level | 2024 | 30 |
| 19. | Attention-based CNN–LSTM | Nonlinear temporal dependencies | Outperformed conventional LSTM | Limited interpretability, longer training | 2025 | 31 |