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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2503.17771 |
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| _version_ | 1866917966450262016 |
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| author | Magadum, Triveni Murgod, Sanjana Garg, Kartik Yadav, Vivek Mittal, Harshit Kushwaha, Omkar |
| author_facet | Magadum, Triveni Murgod, Sanjana Garg, Kartik Yadav, Vivek Mittal, Harshit Kushwaha, Omkar |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_17771 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Renewable Energy Transition in South America: Predictive Analysis of Generation Capacity by 2050 Magadum, Triveni Murgod, Sanjana Garg, Kartik Yadav, Vivek Mittal, Harshit Kushwaha, Omkar Machine Learning 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. |
| title | Renewable Energy Transition in South America: Predictive Analysis of Generation Capacity by 2050 |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2503.17771 |