Partial Differential Equations for Power-Grid Forecasting in Kenya: A Scoping Review of Regularization Techniques and Model Selection Approaches

Partial differential equations (PDEs) are fundamental in modelling power-grid behaviour for forecasting purposes. In Kenya, accurate and reliable forecasts are essential for grid stability and energy management. A structured search strategy was employed across academic databases to identify relevant studies published between -. Studies were critically appraised for methodological rigor and relevance to the review objectives. Regularization techniques, such as Tikhonov regularization, have shown efficacy in mitigating overfitting of PDE models used in power-grid forecasting. Cross-validation methods are commonly applied to select optimal model parameters without data leakage. Despite methodological advancements, challenges remain in achieving robust and accurate forecasts due to the complexity and variability of real-world power grids. Future research should explore hybrid approaches combining regularization with advanced machine learning techniques to enhance forecasting accuracy. Further empirical studies are needed to validate findings across diverse grid conditions.