  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.
