Physics-Informed Temporal Convolutional Network for Ultra-Fast Short-Term PV Power Forecasting Mitigating Atmospheric Electromagnetic Extinction
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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.
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