Abstract

The study explores the application of advanced machine learning techniques to forecast electricity load and wind generation data, focusing on the optimization and comparative analysis of various models. Given the critical importance of accurate energy forecasting in managing power grids and integrating renewable energy sources, this research seeks to enhance forecasting precision through the application of Bayesian optimization for hyperparameter tuning across multiple models. Utilizing time-series data, this study systematically evaluates the performance of several predictive models. Each model's parameters were meticulously optimized using Bayesian techniques to identify the most effective configurations for handling the complex dynamics of energy data. The research methodology involved a comparison within single datasets to identify the best model. Subsequently, the best-performing models were further analyzed across different datasets to validate their robustness and generalizability. The primary evaluation metric is the Root Mean Squared Error (RMSE), complemented by additional metrics to provide a comprehensive assessment of model accuracy and effectiveness. Key findings demonstrate that while some models excel in capturing overall trends, challenges remain in addressing the volatility and variability inherent in the data. The insights derived from this study not only advance the field of energy forecasting but also offer practical implications for energy policymakers and stakeholders in optimizing grid performance and renewable energy integration.