Functional Analysis Techniques for Power Grid Forecasting in Rwanda: Regularization and Model Selection Strategies

Functional analysis techniques are used to model and forecast power grid behaviour in Rwanda, leveraging mathematical models for improved accuracy. The article reviews recent studies that apply functional analysis to predict power grid dynamics, emphasising the use of regularization and cross-validated model selection methodologies. A key finding is the successful application of Tikhonov regularization in enhancing the predictive accuracy of models for Rwanda's power grids by reducing overfitting. The review highlights the effectiveness of functional analysis techniques, particularly regularization methods, in improving forecasting accuracy for Rwanda’s power grid operations. Future research should explore more sophisticated model selection criteria and integrated approaches combining multiple regularization strategies to further enhance predictive performance. Functional Analysis, Power Grid Forecasting, Regularization Techniques, Model Selection, Tikhonov Regularization Model selection is formalised as $\hat{\theta}=argmin_{\theta\in\Theta}\{L(\theta)+\lambda\,\Omega(\theta)\}$ with consistency under mild identifiability assumptions.