Solar Radiation Prediction in the UTEQ based on Machine Learning Models

TL;DR

This study evaluates various machine learning models for predicting solar radiation at UTEQ, demonstrating that Gradient Boosting offers the best accuracy and providing a practical web tool for real-time forecasting to support solar energy management.

Contribution

The paper introduces a comparative analysis of ML models for solar radiation prediction and presents a web-based forecasting tool tailored for UTEQ.

Findings

Gradient Boosting achieved the lowest error metrics.

ML models effectively captured non-linear solar radiation patterns.

The web tool facilitates real-time solar energy management.

Abstract

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.