Work

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

Year: 2025

Type: article

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.

Source: Zenodo (CERN European Organization for Nuclear Research)

DOI: 10.5281/zenodo.18700530

Authors: Okelo, Chester Ochieng, Kinyanjui, Oscar Muthomi

Institutions: Technical University of Kenya, Kenya Medical Research Institute

Language: English

Topic: Energy Load and Power Forecasting

Subfield: Electrical and Electronic Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: green

OpenAlex ID: W7130544426

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