Africa Renewable Energy Development to 2050: Forecast Analysis through a Machine Learning Perspective

This research utilizes sophisticated machine learning models to predict African growth and consumption of renewable energy by the year 2050. Utilizing historical time-series data for energy production, consumption trends, technology adoption rates, policy interventions, economic metrics, and climatic variables, we develop models that capture the intricate interdependencies driving Africa's energy transition. Our approach combines Random Forests, Long Short-Term Memory networks, Gradient Boosting Machines, and Gaussian Process Regression to make robust predictions with uncertainty quantified. Evidence indicates tremendous growth for all renewable technologies, with solar photovoltaic and distributed systems showing the greatest growth. Our models project that renewables will supply 65-80% of the electricity generated in Africa by 2050, with huge regional variations. These projections, therefore call for a targeted investment in infrastructure, off-grid technology, and regional integration to make an African energy system sustainable and resilient enough to serve the aspirations of the continent's economic development without taking a detour on carbon intensive development paths.