Energy Production-Consumption Ratio Prediction using Machine Learning

Mitigating climate change and improving global energy security require a shift to renewable energy. United Nations has declared, switching to renewable energy as one of its sustainable development goals. To achieve this, renewable energy production and consumption, has to be analyzed.. To estimate the ratio of renewable energy output to consumption, we use a variety of machine learning models, including XGBoost, CatBoost etc and evaluation metrics like R2, MSE etc. for performance evaluation. According to our findings, several important variables can be used to forecast the energy efficiency of renewable sources, including production capacity, grid integration, levels of energy use etc. With high R-squared values of 0.9291 and 0.9483, the Gradient Boosting and XGBoost Regressor respectively, exhibit excellent performance, underscoring their resilience in terms of prediction accuracy. The findings provide actionable insights for policymakers and stakeholders to optimize renewable energy strategies globally, contributing to a more sustainable energy future.