” implementación de un sistema fotovoltaico residencial OFF-GRID con predicción energética”
No Thumbnail Available
Date
2025-07-29
Advisors
Journal Title
Journal ISSN
Volume Title
Publisher
Ecuador: Latacunga: Universidad Técnica de Cotopaxi (UTC)
Abstract
This study presents the implementation of an OFF-GRID residential photovoltaic system for a type D house located in Chimborazo Province, Ecuador, where there is no access to the conventional power grid. Two predictive models were used to estimate solar radiation: second-degree polynomial regression and a decision tree, using hourly data from the NASA POWER portal covering the years 2019 to 2024. The decision tree model showed better performance in terms of accuracy, with a Mean Squared Error (MSE) of 0.212 and a Coefficient of Determination (R²) of 0.8561, compared to the polynomial model which achieved an MSE of 0.6359 and R² of 0.6599. The prediction estimated a maximum solar radiation of 416.21 W/m² at 12:00 PM in January. These results are crucial for designing efficient solar energy systems in rural areas without grid access. The research demonstrates that machine learning models offer significant advantages for predicting complex phenomena such as solar radiation, enabling better planning for renewable energy use and improving the quality of life in isolated communities
Description
Keywords
PREDICTION, RADIATION, REGRESSION, DECISION TREE
Citation
Herrera-Robayo D. Lovato-Caiza W (2025). IMPLEMENTACION DE UN SISTEMA FOTOVOLTAICO RESIDENCIAL OFF-GRID CON PREDICCION ENERGETICA. EIGHTH International Conference SOFT COMPUTING AND SIGNAL PROCESSING (ICSCSP-2025) https://www.icscsp.com/the-conference