Predicting Energy Consumption in University Buildings Using the Regression Learner App in MATLAB
Abstract
This project analyzes the prediction of electric power consumption in three buildings of the Universidad Laica Eloy Alfaro de Manabí using MATLAB's Regression Learner tool. The first phase includes a review of key concepts related to energy consumption and the importance of predictive models for demand management in university infrastructures. Subsequently, electric consumption data were collected from three academic buildings over a 10-month period during the year 2022, with the aim of conducting a detailed analysis of each building's energy behavior. In the modeling phase, regression algorithms available in MATLAB were used to analyze and forecast electric demand curves, considering variables such as time of day and weather conditions. Additionally, an interactive interface was developed using App Designer, allowing users to obtain customized predictions of power and current based on the selected faculty and date. Finally, the study presents its conclusions and the results obtained, which make it possible to evaluate the model’s performance and its effectiveness as a tool to improve energy efficiency and optimize future electricity consumption planning.
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References
[2] M. Ilbeigi, M. Ghomeishi, y Dehghanbanadaki, "Electric load forecasting: Literature survey and classification of methods," 2020.
[3] J. E. Hanke y D. W. Wichern, Business Forecasting, 9ª ed., Pearson International Edition. Prentice Hall, 2009.
[4] N. J. A. Hernández, "Estudio del consumo energético en instalaciones universitarias", 2021.
[5] T. Hastie, R. Tibshirani y J. Friedman, The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2ª ed. Springer, 2009.
[6] L. Breiman, "Bagging predictors", Machine Learning, vol. 24, no. 2, pp. 123–140, 1996.
[7] M. Paluszek y S. Thomas, MATLAB Machine Learning: A Guided Tour to Machine Learning Using MATLAB. Apress, 2022.
[8] O. Marques, A Guided Tour to Machine Learning Using MATLAB, 2021.
[9] DataCamp. “RMSE Explained: A Guide to Regression Prediction Accuracy”. 2022. Fecha de consulta: 29 de junio de 2025. URL: https://www.datacamp.com/tutorial/rmse
[10] J. Frost. “Root Mean Square Error (RMSE)”. 2023. Fecha de consulta: 29 de junio de 2025. URL: https://statisticsbyjim.com/regression/root-mean-square-error-rmse/
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