by Leonardo Alves Messias  , José Luis Domingos  , Thiago Augusto Mendes  , Bruno Barzellay Ferreira da Costa  , Ana Carolina Fernandes Maciel  , Saymon Fonseca Santos Mendes  and Raphael de Aquino Gomes

Received: 17 October 2025;  Published: 03 April 2026;  doi: 10.21926/jept.2602007

The increasing demand for renewable energy sources has intensified the adoption of photovoltaic systems. This study proposes predictive models for solar power generation that operate without dependence on on-site meteorological stations. The proposed approach integrates generation data from geographically distributed plants and accounts for the distance to meteorological stations when constructing climatic variables. Two machine learning techniques, Random Forest (RF) and Long Short-Term Memory [...]