Accurate wind speed forecasting is vital for optimizing renewable energy deployment and for advancing our understanding of climate dynamics. Traditional machine-learning approaches often neglect the fundamental physical principles driving atmospheric processes, which limit their robustness and ability to extrapolate beyond the training domain. This investigation introduces an innovative PINN architecture that integrates deep learning techniques with established meteorological theory to improve both predictive fidelity and interpretative clarity. The framework embeds a temperature-sensitive physical constraint directly within the optimization objective. This formulation guarantees that the predictions remain consistent with thermodynamic equilibrium. Structured as a four-layer sequential network with 13 inputs, two hidden (64 neurons each), and a single output unit, the PINN outperformed eight competitive baseline architectures ranging from Bayesian ridge regression to gradient boosting and various hybrid architectures trained on a suite of handcrafted covariates, including wind-shear terms, mean humidity, and temperature-interaction derivatives derived from multi-year records spanning the climatically distinct locales of Badin, Dadu, and Rohri. Observed reductions in mean-squared error and mean absolute error were dramatic, and the coefficient of determination rose to an impressive 0.99. Furthermore, the application of XAI techniques, specifically SHAP and LIME, identified temperature and humidity as the dominant predictors, corroborating the physical consistency of the model while ensuring operational transparency for users. This study establishes an integrative linkage between data-driven learning methodologies and established domain expertise, resulting in a robust and interpretable decision-support tool for both energy system planning and climate impact assessment.
Inam et al. (Sat,) studied this question.