Akademik Veri Yönetim Sistemi
Journal of Machine Intelligence and Data Science, cilt.6, sa.No information , ss.50-62, 2025 (Hakemsiz Dergi)
Technological advancements, supportive legislation, and environmental goals are driving the rapid adoption of renewable energy sources, preparing to transform global energy systems. We examine the contribution and growth of wind, hydro, solar, and bioenergy production in various countries. While our time series analysis utilizes statistical and machine learning techniques, we employ Monte Carlo simulation to account for uncertainties in energy demand forecasts. We found that hydroelectric power maintains its dominance in regions rich in water resources, whereas solar and wind energy have recorded impressive growth over recent years. We also use visual indicators such as distribution heat maps, correlation graphs, and time series trends to illustrate the current state of renewable energy use. These data-driven insights help clarify the ongoing global energy transition, enabling informed planning for renewable energy investments and policy decisions.