AI-Based Virtual Assistant for Solar Radiation Prediction and Improvement of Sustainable Energy Systems

Tomás Gavilánez, Néstor Zamora, Josué Navarrete, Nino Vega () and Gabriela Vergara
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Tomás Gavilánez: Industrial Processes Research Group, Universidad Politécnica Salesiana, Guayaquil 090204, Ecuador
Néstor Zamora: Electronics Faculty of Technical Education for Development, Universidad Católica de Santiago de Guayaquil, Guayaquil 090504, Ecuador
Josué Navarrete: Industrial Processes Research Group, Universidad Politécnica Salesiana, Guayaquil 090204, Ecuador
Nino Vega: Industrial Processes Research Group, Universidad Politécnica Salesiana, Guayaquil 090204, Ecuador
Gabriela Vergara: A Career of Risks and Disasters, Escuela Superior Politécnica Agropecuaria de Manabí Manuel Félix López, Calceta 1701518, Ecuador

Sustainability, 2025, vol. 17, issue 19, 1-20

Abstract: Advances in machine learning have improved the ability to predict critical environmental conditions, including solar radiation levels that, while essential for life, can pose serious risks to human health. In Ecuador, due to its geographical location and altitude, UV radiation reaches extreme levels. This study presents the development of a chatbot system driven by a hybrid artificial intelligence model, combining Random Forest, CatBoost, Gradient Boosting, and a 1D Convolutional Neural Network. The model was trained with meteorological data, optimized using hyperparameters (iterations: 500–1500, depth: 4–8, learning rate: 0.01–0.3), and evaluated through MAE, MSE, R 2 , and F1-Score. The hybrid model achieved superior accuracy (MAE = 13.77 W/m 2 , MSE = 849.96, R 2 = 0.98), outperforming traditional methods. A 15% error margin was observed without significantly affecting classification. The chatbot, implemented via Telegram and hosted on Heroku, provided real-time personalized alerts, demonstrating an effective, accessible, and scalable solution for health safety and environmental awareness. Furthermore, it facilitates decision-making in the efficient generation of renewable energy and supports a more sustainable energy transition. It offers a tool that strengthens the relationship between artificial intelligence and sustainability by providing a practical instrument for integrating clean energy and mitigating climate change.

Keywords: chatbot; artificial intelligence; solar radiation; energy sustainability; sustainable development (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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