What does this research mean for the field?

Regularization techniques, such as Tikhonov regularization, improve the accuracy of partial differential equation models used for power-grid forecasting in Kenya. Novelty: ClaimNovelty.SYNTHESIS. Consensus alignment: ConsensusAlignment.NEUTRAL.

What question did this study set out to answer?

The review aims to explore the use of partial differential equations in power-grid forecasting and identify effective techniques.

February 21, 2026Open Access

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

Key Points

The review aims to explore the use of partial differential equations in power-grid forecasting and identify effective techniques.
Conducted a structured search across academic databases for relevant literature.
Critically appraised studies for methodological rigor and relevance.
Examined the efficacy of regularization techniques like Tikhonov regularization.
Assessed the use of cross-validation for optimal model parameter selection.
Regularization techniques effectively reduce overfitting in PDE models.
Cross-validation methods help in selecting model parameters without data leakage.
Challenges persist in achieving reliable forecasts due to power grid complexities.
Future research should integrate machine learning with existing techniques for improved accuracy.

Abstract

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.

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