This research explores the effectiveness of various Machine Learning (ML) models used to predicting solar radiation at the Central Campus of the State Technical University of Quevedo (UTEQ). The data was obtained from a pyranometer, strategically located in a high area of the campus. This instrument continuously recorded solar irradiance data since 2020, offering a comprehensive dataset encompassing various weather conditions and temporal variations. After a correlation analysis, temperature and the time of day were identified as the relevant meteorological variables that influenced the solar irradiance. Different machine learning algorithms such as Linear Regression, K-Nearest Neighbors, Decision Tree, and Gradient Boosting were compared using the evaluation metrics Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and the Coefficient of Determination ($R^2$). The study revealed that Gradient Boosting Regressor exhibited superior performance, closely followed by the Random Forest Regressor. These models effectively captured the non-linear patterns in solar radiation, as evidenced by their low MSE and high $R^2$ values. With the aim of assess the performance of our ML models, we developed a web-based tool for the Solar Radiation Forecasting in the UTEQ available at http://https://solarradiationforecastinguteq.streamlit.app/. The results obtained demonstrate the effectiveness of our ML models in solar radiation prediction and contribute a practical utility in real-time solar radiation forecasting, aiding in efficient solar energy management.

翻译：本研究探讨了多种机器学习模型在预测克韦多国立技术大学中心校区太阳辐射方面的有效性。数据来源于位于校园高海拔区域的太阳辐射计，该仪器自2020年起连续记录太阳辐照度数据，提供了涵盖多种气象条件与时间变化的综合数据集。通过相关性分析，确定温度和时段是影响太阳辐照度的关键气象变量。研究采用线性回归、K近邻、决策树和梯度提升等不同机器学习算法，并基于均方误差、均方根误差、平均绝对误差及决定系数（$R^2$）等评估指标进行对比。结果显示，梯度提升回归器表现最优，随机森林回归器紧随其后。这些模型有效捕捉了太阳辐射中的非线性模式，其低MSE和高$R^2$值佐证了这一点。为评估模型性能，我们开发了用于UTEQ太阳辐射预测的Web工具，网址为http://https://solarradiationforecastinguteq.streamlit.app/。研究结果表明，所提出的机器学习模型在太阳辐射预测中展现出高效性，为实时太阳辐射预报提供了实用工具，有助于优化太阳能管理。