Digital Twin-Augmented Multifeature Fusion Ensemble for Accurate and Uncertain PV Power Forecasting
This article presents a short-term (1-h ahead) physics-guided multirepresentation feature fusion framework for photovoltaic (PV) power prediction with uncertainty quantification based on quantile regression for interval forecasting. The proposed hybrid methodology integrates feature-representation fusion, deep learning architectures, including long short-term memory (LSTM) and gated recurrent unit (GRU), and machine learning models, such as support vector regression (SVR) and random forest (RF), along with dimensionality reduction techniques, namely principal component analysis (PCA) and autoencoder (AE), and a fully defined physics-based digital twin (DT) model with explicit irradiance-to-power equations and parameter calibration. The DT residual, computed causally using only training data, is incorporated as a physics-informed feature to capture unmodeled nonlinearities. PIs are learned using the pinball (quantile) loss function, replacing heuristic assumptions, and feature-representation fusion reduces uncertainty while enhancing robustness. Validation on real meteorological datasets from Izki and Manah stations (2017–2023) shows that conventional feature sets achieve root mean squared error (RMSE) values between 0.14–0.17 with coefficient of determination (<inline-formula><tex-math notation="LaTeX">$R^{2}$</tex-math></inline-formula>) ranging from 0.47–0.61. Incorporating DT residuals significantly improves performance, while maintaining realistic generalization performance, with LSTM achieving RMSE <inline-formula><tex-math notation="LaTeX">$\approx$</tex-math></inline-formula> 0.0105 and <inline-formula><tex-math notation="LaTeX">$R^{2}$</tex-math></inline-formula> up to 0.9979. SVR and RF models also benefit, achieving RMSE <inline-formula><tex-math notation="LaTeX">$\leq 0.0525$</tex-math></inline-formula> and <inline-formula><tex-math notation="LaTeX">$R^{2} \geq 0.9467$</tex-math></inline-formula>. Comprehensive evaluation including ensemble comparisons, ablation studies, and uncertainty metrics, namely prediction interval coverage probability and mean prediction interval width, confirms high coverage and sharpness, while feature-representation fusion ensures consistent, low-uncertainty predictions. Further experiments on a physically inspired synthetic dataset with explicitly defined generation process and temporal resolution of 1000 samples demonstrate similar trends. Using DT residual features, LSTM and GRU achieve <inline-formula><tex-math notation="LaTeX">$R^{2} > 0.998$</tex-math></inline-formula> with RMSE <inline-formula><tex-math notation="LaTeX">$\approx 0.042$</tex-math></inline-formula>, while RF reaches <inline-formula><tex-math notation="LaTeX">$R^{2} = 0.897$</tex-math></inline-formula> and a relatively higher RMSE <inline-formula><tex-math notation="LaTeX">$\approx 0.326$</tex-math></inline-formula>. PCA and AE representations improve computational efficiency but provide comparatively lower predictive accuracy. Direct comparison between individual models and the proposed ensemble highlights the robustness and stability of the fusion strategy, particularly in high-noise conditions. In summary, the framework offers a reproducible, physically grounded, and uncertainty-aware solution for next-generation solar energy forecasting, explicitly quantifying prediction uncertainty and leveraging physics-informed residuals to enhance predictive reliability and interpretability across both real and synthetic scenarios.