This study presents a physically constrained robustness evaluation framework, using virtual PV power as a controlled response variable to isolate the propagation of input uncertainty from confounders at the plant level, and shows that sequence models provide stronger noise filtering and temporal resilience than a strong tabular baseline under medium to high disturbance regimes.
Abstract
Engineering use of AI forecasting models requires not only high nominal accuracy but also predictable behavior under uncertain inputs. In photovoltaic (PV) forecasting, this requirement is especially challenging because numerical weather prediction (NWP) errors are temporally correlated, state dependent, and physically coupled across variables. Existing evaluations, however, often rely on perfect forecast assumptions or simplistic perturbations that do not reflect these characteristics. This study presents a physically constrained robustness evaluation framework based on simulation, using virtual PV power as a controlled response variable to isolate the propagation of input uncertainty from confounders at the plant level. Six representative machine learning and deep sequence models, including PatchTST, GRU, N-HITS, and LightGBM, are evaluated under dynamic NWP perturbations with heteroscedasticity modulated by clear-sky conditions and Erbs reconstruction that preserves radiation consistency. The results show that sequence models provide stronger noise filtering and temporal resilience than a strong tabular baseline under medium to high disturbance regimes. SHapley Additive exPlanations (SHAP) and Integrated Gradients (IG) further support a feature reallocation tendency at the case level, in which predictive reliance shifts from corrupted future forecasts toward more stable historical observations and deterministic physical priors. A Pareto analysis of accuracy under clean conditions, robustness, and computational latency then translates these findings into engineering implications for robustness assessment and model selection under forecast uncertainty.
This study provides an in-depth comparative analysis of four state-of-the-art neural architectures, confirming that high-fidelity point forecasts and rigorously quantified uncertainty can be achieved simultaneously, providing a clear path toward more dependable PV dispatch, reserve allocation, and market participation.
Saloni Dhingra, G. Gruosso, G. Storti Gajani· Neural computing & applicati...· 0 citations
Reliable short-term PV forecasting is essential to ensure the reliability of energy management based on renewable sources, but the lack of interpretability of deep learning models and the environmental uncertainty still prevent their operation. In this research, an uncertainty-aware transformer framework is proposed which is explainable when forecasting the short-term PV power under dynamically changing environmental conditions. It introduces a framework which combines a Temporal Fusion Transformer (TFT), quantile-based uncertainty estimation, dual explainability mechanism based on attention analysis, and SHAP based feature attribution. The measurements from a real solar system having capacity of 1.005 kW were taken at the site of Sitapura, Jaipur, India and were synchronously matched with the POWER meteorological measurements taken by NASA. The framework was tested on the baseline models of ARIMA, LSTM and CNN-LSTM. These experimental results showed that the approach achieved better forecasting results compared to all the baseline approaches with RMSE of 0.067, MAE of 0.051 and MAPE of 4.38%. The probabilistic forecasting module was able to attain a Prediction Interval Coverage Probability (PICP) of 94.8% and narrow interval width, thus demonstrating good uncertainty estimation abilities. Irradiance and PV output of the past were found to be the most important factors for forecasting in this explainability analysis. The proposed framework is based on the transformer model, probabilistic forecasting, and then explains the renewable energy forecasting system in the entire system for photovoltaic uncertainty-aware and interpretable prediction.
Udit Mamodiya, I. Kishor, P. Mudholkar et al.· International Journal of Dat...· 0 citations
Extreme rainfall events (ERES) are among the most challenging hydro-meteorological phenomena to forecast because the complex, nonlinear atmospheric processes involved span multiple spatial and temporal scales and are further intensified by rising climate variability. While Numerical Weather Prediction (NWP) models offer physically consistent representations of atmospheric dynamics, they still struggle to resolve localized convection, rapidly evolving storm systems, and rare, high-intensity precipitation events. This study systematically reviews recent advances in Artificial Intelligence (AI) for extreme rainfall prediction published between 2020 and 2026, using a transparent literature search strategy, predefined screening criteria, quality assessment, and a structured literature review matrix. The review synthesizes evidence from a final evidence base of 139 studies across several dimensions, including deep learning (DL) methodologies, multimodal data fusion, forecasting horizons, operational deployment, uncertainty quantification, and emerging intelligent forecasting paradigms. The analysis reveals a transition from deterministic DI models to hybrid, physics-informed, uncertainty-aware, and operational forecasting systems that increasingly integrate AI with physical knowledge and heterogeneous environmental observations. Despite notable progress, persistent challenges remain, including data scarcity, class imbalance, model generalization, physical consistency, uncertainty estimation, transferability, and the lack of standardized evaluation frameworks. This review provides a unified synthesis of current methodologies. It identifies key research gaps, highlighting the need for evaluation frameworks that address physical plausibility, uncertainty quantification, reliability, transferability, and operational relevance. The findings indicate that future extreme rainfall prediction systems should advance beyond accuracy-focused evaluation toward integrated, trustworthy, uncertainty-aware, and physically consistent forecasting approaches.
Braiton U. Mukhalela, S. Viriri, D. Ndzi et al.· Frontiers in Artificial Inte...· 0 citations
An optimized forecasting framework using Long Short-Term Memory (LSTM) networks is introduced, confirming the model’s excellent accuracy and its value for improving power system dispatch and resource planning.
The findings highlight the value of training strategies that allow models to directly learn bias correction during forecast transitions, emphasize the operational potential of combining sequential processing with near real-time discharge observations and identify physiographic catchment characteristics as key modulators of forecast skill improvement across diverse hydroclimatic settings.
O. Konold, Moritz Feigl, Patrick Podest et al.· Hydrology and Earth System S...· 2 citations
Accurate renewable power forecasting is essential for grid operation, reserve scheduling, and renewable integration. This paper proposes a constraint-guided residual forecasting framework that combines a domain-informed baseline with data-driven residual correction for multi-horizon probabilistic forecasting. A Gradient Boosting Regression Tree (GBRT) model is used as a classical residual benchmark, while a Residual PatchTST model captures temporal dependencies from numerical weather prediction features, engineered time variables, and site information. Final forecasts are reconstructed by adding the predicted residual to the baseline and enforcing nonnegative outputs within site-level capacity limits. Across PV sites, GBRT reduces mean RMSE from 0.0909 to 0.0798 and mean MAE from 0.0400 to 0.0358, while wind RMSE decreases from 0.2574 to 0.1479. The deep probabilistic model also delivers stable multi-horizon performance and useful uncertainty intervals. These results show that residual learning with constraint-guided reconstruction provides accurate and operationally meaningful renewable power forecasts.
Naif Nafea J. Alanazi, Q. Lei· 2026 IEEE International Conf...· 0 citations
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