Monte Carlo Simulations and Renewable Energy Scenarios: Dealing with Uncertainty Using Statistical Estimations

Abstract

The rapid adoption of renewable energy sources will influence global energy systems in the future due to technological advancements, legislative incentives, and environmental objectives. This study examines the contributions and growth rates of wind, hydro, solar, and bioenergy production trends in various countries. This time series data science heavily relates the concepts of statistical and machine-learning approaches to understanding correlations, variabilities, and regional preference patterns for different forms of renewable energy. While hydropower continues to dominate in wetter regimes, the data suggests an impressive surge in solar and wind energy. Additional visual hints about the state of renewable energy in our day and age would be generated by heatmap indicators such as distribution plots, correlation of related data, and time-series analysis. Therefore, the above statistics help clarify the global energy transition, enabling us to make knowledge-driven plans for renewable energy investments and policy.