Aug 2026· Advanced Electromagnetics· Vol 15, pp. 1755-1766· 0 citations
Abstract
As the global energy mix shifts toward cleaner sources, the large-scale grid integration of photovoltaic (PV) power poses severe challenges to microgrid frequency stability and security. From a fundamental physical perspective, the solar radiation driving photovoltaic conversion consists of electromagnetic waves on the micrometer scale; as these waves traverse the atmosphere, they undergo intense Rayleigh and Mie scattering caused by cloud dynamics and aerosol attenuation. This atmospheric degradation results in highly nonlinear, transient fluctuations in the effective power reaching the ground. Consequently, computationally intensive full-wave simulation models are impractical for real-time dispatch, while existing purely data-driven deep learning algorithms—lacking physical interpretability—are prone to overfitting and prediction failure under non-stationary meteorological conditions. To bridge this gap between physics and algorithms, this study proposes a novel Physics-Informed Temporal Convolutional Network (PI-TCN) architecture. The framework utilizes Global Horizontal Irradiance (GHI) and Diffuse Horizontal Irradiance (DHI) as inputs to implicitly reconstruct the electromagnetic wave's energy attenuation trajectory, employing 1D causal dilated convolutions to eliminate temporal lag. Furthermore, the model innovatively incorporates non-negative electromagnetic energy boundaries and first-order wave derivatives as penalty functionals during backpropagation, thereby constraining model weights to converge within a physically feasible domain. Benchmarking against a three-year high-resolution dataset from the Desert Knowledge Australia Solar Centre (DKASC) demonstrates that the PI-TCN achieves an exceptionally high coefficient of determination (R2) of 0.9524 and an inference latency of merely 0.08 milliseconds; notably, it attains a Matthews Correlation Coefficient (MCC) of 0.8412 in capturing extreme ramp events. By utilizing the Jacobian matrix of partial derivatives to fully deconstruct the network's "black-box" nature, this research establishes a robust and highly interpretable new paradigm for the convergence of computational electromagnetics and artificial intelligence.
Accurate short-term photovoltaic (PV) power forecasting is critical for secure grid operation and economic dispatch, yet its performance is often degraded by non-stationary irradiance fluctuations induced by cloud transients and weather regime shifts. To address this challenge, this paper proposes a physics-guided temporal fusion architecture built on the Temporal Fusion Transformer (TFT) for multi-horizon probabilistic PV forecasting. The model fuses historical plant measurements, Numerical Weather Prediction (NWP) variables, and solar-geometry features, and introduces a cross-attention fusion module to align future meteorological drivers with the most relevant historical context for improved ramp responsiveness. Physical plausibility is promoted by soft physics-consistency regularization, including non-negativity, rated-capacity bounds, clear-sky envelope constraints, and ramp-rate penalties, while uncertainty is quantified via multi-quantile regression to produce calibrated prediction intervals. Experiments on real-world PV datasets across different seasons and weather conditions show that the proposed approach consistently improves deterministic accuracy (MAE and RMSE) and probabilistic performance (PICP and CRPS) over representative baselines, with particularly robust behavior under regime-shift and ramp events. Overall, combining attention-based temporal fusion with physics-guided learning provides a practical and scalable solution for reliable probabilistic PV power forecasting in highly variable atmospheric conditions.
Pei-Xiang Wu· European Conference on Elect...· 0 citations
Accurate short-term wind power prediction plays a critical role in ensuring stable grid operation, effective energy management, and the large-scale integration of renewable energy systems under highly variable wind conditions. Although data-driven and deep learning models have demonstrated promising forecasting capability, many existing approaches suffer from performance degradation during rapid wind fluctuations due to the lack of embedded physical constraints and the high computational complexity associated with recurrent architectures. To address these limitations, this article proposes a Physics-Guided Residual Temporal Convolutional Network (PG-ResTCN) for short-term wind power forecasting. The proposed framework integrates dilated temporal convolutional learning with residual connections to effectively capture multiscale temporal dependencies in wind power time series while avoiding the sequential computation of recurrent neural networks, thereby improving computational efficiency. Furthermore, a physics-based smoothness constraint is incorporated into the training loss function to enforce physically consistent power ramping behavior that reflects the inherent operational dynamics and inertia of wind turbines, reducing unrealistic fluctuations in predicted power outputs. The effectiveness of the proposed model is validated using a large real-world dataset containing approximately 140,161 hourly samples collected from five wind farm locations, including meteorological variables and corresponding turbine power outputs. Comprehensive experiments are conducted by comparing the proposed method with widely used benchmark models, including Support Vector Regression, Random Forest, Extreme Gradient Boosting, and Long Short-Term Memory networks. Results demonstrate that the proposed PG-ResTCN model achieves superior forecasting performance, obtaining a root mean square error of 0.0359, mean absolute error of 0.0296, and R
2
of 0.9759, outperforming all baseline models. The integration of physics-guided constraints with the temporal convolutional architecture significantly enhances prediction accuracy, stability, and generalization capability. In addition, the proposed framework maintains high computational efficiency and robustness, making it suitable for real-time wind energy forecasting applications, intelligent energy management systems, and microgrid power system operations.
S. Marisargunam, T. Mariprasath, Mohit Bajaj et al.· Energy Exploration & Exp...· 0 citations
Accurate high-resolution photovoltaic power forecasting remains challenging because rapid weather changes and heterogeneous plant configurations produce nonlinear variations and extreme errors. Existing data-driven methods capture complex meteorological relationships but may generate physically inconsistent forecasts, whereas simplified physical models cannot fully represent site specific behavior. This study developed a physics-informed temporal fusion transformer framework combining a transformer–bidirectional long short-term memory forecasting branch with soft irradiance–power and temperature–power constraints. The framework was evaluated using 70,176 observations from Solar Station Site 5, recorded at 15-minute intervals, and independently trained and calibrated across eight solar stations. On the Site 5 test set, it achieved a mean absolute error of 1.4253 megawatts, a root mean squared error of 3.8612 megawatts, and a coefficient of determination of 0.9734. It outperformed standalone temporal fusion transformer, gated recurrent unit, extreme gradient boosting, and random forest; relative to standalone temporal fusion transformer, mean absolute and root mean squared errors decreased by 29.7% and 15.1%, respectively. Sensitivity analysis selected 0.05 as the best nonzero physics weight for Site 5 and revealed a trade-off between predictive accuracy and physical consistency. Residual analysis identified transient and measurement-related outliers. Across the eight stations, coefficients of determination exceeded 0.84 at six sites and reached 0.9804 at Site 6, supporting generalizability across heterogeneous installations following station-specific calibration. Deployment latency remained 0.0864 milliseconds per sample, supporting operational use for renewable-energy integration, grid scheduling, and low-carbon power management.
M. Nasir, Muhammad Farhan Hanif, Muhammad Tahir Hassan et al.· Clean Energy· 0 citations
Accurate solar radiation forecasting is essential for photovoltaic energy generation, smart grid stability, and renewable energy management. This study proposes a lightweight hybrid deep learning framework that combines a transformer encoder and Gated Rrecurrent Uunit (GRU) network for short-term solar radiation forecasting in Makkah and Madinah, Saudi Arabia. Hourly meteorological data from the NASA POWER dataset (2020–2025) were utilized, including solar radiation intensity, temperature, humidity, wind speed, cloud amount, rainfall, surface pressure, and dew point temperature. A preprocessing pipeline consisting of missing value treatment, outlier removal, normalization, timestamp alignment, and data cleaning was applied to improve data quality. Feature engineering techniques were incorporated to capture temporal dependency, meteorological interactions, weather dynamics, and solar variability patterns. The transformer encoder was used to learn long-range temporal dependencies through multi-head self-attention, while the GRU layer modeled sequential temporal dynamics efficiently. Hyperparameter optimization was performed using Bayesian optimization with Optuna. The experimental results demonstrate that the proposed transformer GRU framework achieved a Mean Absolute Error (MAE) of 0.014, Root Mean Square Error (RMSE) of 0.0219, and a coefficient of determination (R2) of 0.98. The proposed model outperformed ARIMA, LSTM, GRU, and XGBoost models while maintaining stable performance across varying weather conditions and forecasting horizons.
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.
Accurate short-term photovoltaic (PV) power forecasting is essential for secure grid operation, economic dispatch, and efficient utilization of renewable energy. However, PV output exhibits strong nonlinearity and nonstationarity due to rapidly varying meteorological conditions, cloud movements, and system uncertainties. Conventional statistical models and single-source deep learning approaches often fail to fully exploit the rich multi-source information available in modern PV plants, such as historical power, on-site meteorological measurements, and clear-sky or numerical weather prediction (NWP) features. In this paper, we propose MSF-TransPV, a Multi-Source Fusion Transformer framework for short-term PV power forecasting. The model adopts a multi-branch temporal encoder that separately processes historical PV output, meteorological variables, and optional clear-sky/NWP-derived features, mapping them into a shared latent space. A cross-variable multi-head attention module is then introduced to explicitly capture the dependencies between PV dynamics and atmospheric conditions, enabling fine-grained interaction across different feature sources. On top of the fused representation, a temporal Transformer encoder models long-range temporal dependencies and feeds a lightweight decoder that supports both point forecasting and probabilistic forecasting via quantile regression. Experimental results on real-world PV datasets demonstrate that MSF-TransPV consistently outperforms persistence, statistical baselines, recurrent neural networks, and vanilla Transformer models in terms of RMSE, MAE, and normalized error metrics, while also providing reliable prediction intervals. These results indicate that explicit multi-source fusion and cross-variable attention are highly effective for PV forecasting under complex and rapidly changing weather conditions.
Xiao-Mei Wang, Pei-Xuan Xu, Xiao-Hui Wang· European Conference on Elect...· 0 citations
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