Artificial Intelligence in Data Science: Evaluating Forecasting Models for Solar Energy in the Amazon Basin

Abstract

Forecasting models employing machine learning (ML) and deep learning (DL) have become fundamental for assessing the technical feasibility of renewable energy systems. Among these, solar energy stands out as a renewable energy option, particularly relevant for supporting the preservation of the Amazon rainforest. This study introduces a novel approach using ML and DL methods—integrated with Universal Kriging and Holt-Winters (time series) models — to forecast solar irradiance (kWh/m2) in cities across the state of Amazonas. The analysis is grounded in the Data Science cycle, with input data sourced from both ground stations and satellite products. Forecasting performance was evaluated for short-term horizons (one to three days ahead) across three representative cities. The hybrid SARIMAX-CNN-LSTM, SARIMAX-CNN-Transformer, and SARIMAX-TCN models achieved MAPE values ranging from 18.1% to 26.6% for the different forecast horizons and cities. These results are consistent with existing literature and reinforce the suitability of advanced ML/DL approaches for solar energy forecasting in highly variable and challenging environments such as the Amazon Basin.