Renewable Energy Transition in South America: Predictive Analysis of Generation Capacity by 2050
🤖 AI Summary
This study addresses South America’s energy transition and climate governance needs by forecasting renewable energy capacity expansion through 2050 to inform policy design, investment decisions, and sustainable development pathways. Methodologically, it innovatively integrates gradient boosting regression with the Facebook Prophet model into a multi-source data-driven, cross-national long-term collaborative forecasting framework, augmented by GIS-based spatial analysis to quantify regional disparities. Results indicate that South America’s total renewable installed capacity will reach 2.8 times its 2020 level by 2050, with Brazil and Chile serving as dual growth hubs and solar PV and wind power exhibiting the highest growth rates. Spatially, a pronounced “northwest–southeast” gradient of development intensity emerges. The proposed framework significantly enhances the accuracy and interpretability of medium- to long-term, cross-regional renewable energy forecasting—offering a novel paradigm for energy system planning in developing economies.
Application Category
📝 Abstract
In this research, renewable energy expansion in South America up to 2050 is predicted based on machine learning models that are trained on past energy data. The research employs gradient boosting regression and Prophet time series forecasting to make predictions of future generation capacities for solar, wind, hydroelectric, geothermal, biomass, and other renewable sources in South American nations. Model output analysis indicates staggering future expansion in the generation of renewable energy, with solar and wind energy registering the highest expansion rates. Geospatial visualization methods were applied to illustrate regional disparities in the utilization of renewable energy. The results forecast South America to record nearly 3-fold growth in the generation of renewable energy by the year 2050, with Brazil and Chile spearheading regional development. Such projections help design energy policy, investment strategy, and climate change mitigation throughout the region, in helping the developing economies to transition to sustainable energy.
Problem
Research questions and friction points this paper is trying to address.
Predict renewable energy capacity in South America by 2050
Analyze regional disparities in renewable energy utilization
Guide energy policy and investment for sustainable transition
Innovation
Methods, ideas, or system contributions that make the work stand out.
Machine learning models predict renewable energy expansion
Gradient boosting and Prophet forecast future capacities
Geospatial visualization shows regional energy disparities
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