IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v17y2025i19p8909-d1766477.html

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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Tomás Gavilánez & Néstor Zamora & Josué Navarrete & Nino Vega & Gabriela Vergara, 2025. "AI-Based Virtual Assistant for Solar Radiation Prediction and Improvement of Sustainable Energy Systems," Sustainability, MDPI, vol. 17(19), pages 1-20, October.
    • Handle: RePEc:gam:jsusta:v:17:y:2025:i:19:p:8909-:d:1766477
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/17/19/8909/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/17/19/8909/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Richard Guanoluisa & Diego Arcos-Aviles & Marco Flores-Calero & Wilmar Martinez & Francesc Guinjoan, 2023. "Photovoltaic Power Forecast Using Deep Learning Techniques with Hyperparameters Based on Bayesian Optimization: A Case Study in the Galapagos Islands," Sustainability, MDPI, vol. 15(16), pages 1-18, August.
    2. Mahmudul Islam & Masud Rana Rashel & Md Tofael Ahmed & A. K. M. Kamrul Islam & Mouhaydine Tlemçani, 2023. "Artificial Intelligence in Photovoltaic Fault Identification and Diagnosis: A Systematic Review," Energies, MDPI, vol. 16(21), pages 1-18, November.
    3. Wassila Tercha & Sid Ahmed Tadjer & Fathia Chekired & Laurent Canale, 2024. "Machine Learning-Based Forecasting of Temperature and Solar Irradiance for Photovoltaic Systems," Energies, MDPI, vol. 17(5), pages 1-20, February.
    4. Edna S. Solano & Carolina M. Affonso, 2023. "Solar Irradiation Forecasting Using Ensemble Voting Based on Machine Learning Algorithms," Sustainability, MDPI, vol. 15(10), pages 1-19, May.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Latif Bukari Rashid & Shahzada Zaman Shuja & Shafiqur Rehman, 2025. "Machine Learning Forecasting of Direct Solar Radiation: A Multi-Model Evaluation with Trigonometric Cyclical Encoding," Forecasting, MDPI, vol. 7(4), pages 1-25, October.
    2. Zhang, Haoran & Yu, Xiaohong & Gao, Zixuan, 2025. "Is the end of AI in photovoltaic power? Evidence from China," Energy Economics, Elsevier, vol. 145(C).
    3. Mehmet Das & Erhan Arslan & Sule Kaya & Bilal Alatas & Ebru Akpinar & Burcu Özsoy, 2024. "Performance Evaluation of Photovoltaic Panels in Extreme Environments: A Machine Learning Approach on Horseshoe Island, Antarctica," Sustainability, MDPI, vol. 17(1), pages 1-34, December.
    4. Francisco A. Ramírez-Rivera & Néstor F. Guerrero-Rodríguez, 2024. "Ensemble Learning Algorithms for Solar Radiation Prediction in Santo Domingo: Measurements and Evaluation," Sustainability, MDPI, vol. 16(18), pages 1-27, September.
    5. Chibuike Chiedozie Ibebuchi, 2025. "Day-Ahead Energy Price Forecasting with Machine Learning: Role of Endogenous Predictors," Forecasting, MDPI, vol. 7(2), pages 1-16, April.
    6. Lin, Peijie & Guo, Feng & Lin, Yaohai & Cheng, Shuying & Lu, Xiaoyang & Chen, Zhicong & Wu, Lijun, 2025. "Fault diagnosis of photovoltaic arrays with different degradation levels based on cross-domain adaptive generative adversarial network," Applied Energy, Elsevier, vol. 386(C).
    7. Tomasz Grudniewski & Zbigniew Ciekanowski & Slawomir Żurawski & Valeriy Kuznetsov & Petro Hubskyi & Wlodzimierz Kruczek, 2025. "Energy Security of Photovoltaic Systems in the Context of Artificial Intelligence Development," European Research Studies Journal, European Research Studies Journal, vol. 0(3), pages 786-806.
    8. Boris I. Evstatiev & Dimitar T. Trifonov & Katerina G. Gabrovska-Evstatieva & Nikolay P. Valov & Nicola P. Mihailov, 2024. "PV Module Soiling Detection Using Visible Spectrum Imaging and Machine Learning," Energies, MDPI, vol. 17(20), pages 1-20, October.
    9. Thiago Conte & Roberto Oliveira, 2024. "Comparative Analysis between Intelligent Machine Committees and Hybrid Deep Learning with Genetic Algorithms in Energy Sector Forecasting: A Case Study on Electricity Price and Wind Speed in the Brazi," Energies, MDPI, vol. 17(4), pages 1-31, February.
    10. Saman Abolghasemi Moghaddam & Nuno Simões & Michael Brett & Manuel Gameiro da Silva & Joana Prata, 2025. "Dynamic Behavior of a Glazing System and Its Impact on Thermal Comfort: Short-Term In Situ Assessment and Machine Learning-Based Predictive Modeling," Energies, MDPI, vol. 18(17), pages 1-22, September.
    11. Aissa Meflah & Fathia Chekired & Nadia Drir & Laurent Canale, 2024. "Accurate Method for Solar Power Generation Estimation for Different PV (Photovoltaic Panels) Technologies," Resources, MDPI, vol. 13(12), pages 1-18, November.
    12. Grothe, Oliver & Kächele, Fabian & Wälde, Mira, 2025. "High-resolution working layouts and time series for renewable energy generation in Europe," Renewable Energy, Elsevier, vol. 239(C).
    13. Tymoteusz Miller & Irmina Durlik & Ewelina Kostecka & Polina Kozlovska & Marek Staude & Sylwia Sokołowska, 2025. "The Role of Lightweight AI Models in Supporting a Sustainable Transition to Renewable Energy: A Systematic Review," Energies, MDPI, vol. 18(5), pages 1-29, February.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:17:y:2025:i:19:p:8909-:d:1766477. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.