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Article

A Predictive Approach for Energy Efficiency and Emission Reduction in University Campuses

by
Alberto Rey-Hernández
1,2,*,
Julio San José-Alonso
1,2,3,
Ana Picallo-Perez
1,4,
Francisco J. Rey-Martínez
1,2,3,*,
A. O. Elgharib
1,5,
Javier M. Rey-Hernández
1,6,7 and
Khaled M. Salem
1,5
1
GIRTER Research Group, Consolidated Research Unit (UIC053) of Castile and Leon, 47002 Valladolid, Spain
2
Department of Energy and Fluid Mechanics, Engineering School (EII), University of Valladolid (UVa), 47002 Valladolid, Spain
3
Institute of Advanced Production Technologies (ITAP), University of Valladolid (Uva), 47002 Valladolid, Spain
4
Department of Thermal Engineering, Engineering School, University of the Basque Country (UPV/EHU), 01006 Vitoria, Spain
5
Department of Basic and Applied Science Engineering, Arab Academy for Science, Technology and Maritime Transport, Smart Village Campus, Giza 12577, Egypt
6
Department of Mechanical Engineering, Fluid Mechanics and Thermal Engines, Engineering School, University of Malaga (UMa), 29016 Málaga, Spain
7
RE+ Research Group (TEP1003), University of Málaga (UMa), 29010 Málaga, Spain
*
Authors to whom correspondence should be addressed.
Appl. Sci. 2025, 15(17), 9419; https://doi.org/10.3390/app15179419 (registering DOI)
Submission received: 24 July 2025 / Revised: 12 August 2025 / Accepted: 26 August 2025 / Published: 27 August 2025
(This article belongs to the Special Issue Energy Transition in Sustainable Buildings)

Abstract

This study proposes a comprehensive artificial intelligence (AI)-based framework to predict, disaggregate, and optimize energy consumption and associated CO2 emissions across a multi-building university campus. Leveraging real-world data from 27 buildings at the University of Valladolid (Spain), six AI models—artificial neural networks (ANN), radial basis function (RBF), autoencoders, random forest (RF), XGBoost, and decision trees—were trained on heat exchanger performance metrics and contextual building parameters. The models were validated using an extensive set of key performance indicators (MAPE, RMSE, R2, KGE, NSE) to ensure both predictive accuracy and generalizability. The ANN, RBF, and autoencoder models exhibited the highest correlation with actual data (R > 0.99) and lowest error rates, indicating strong suitability for operational deployment. A detailed analysis at building level revealed heterogeneity in energy demand patterns and model sensitivities, emphasizing the need for tailored forecasting approaches. Forecasts for a 5-year horizon further demonstrated that, without intervention, energy consumption and CO2 emissions are projected to increase significantly, underscoring the relevance of predictive control strategies. This research establishes a robust and scalable methodology for campus-wide energy planning and offers a data-driven pathway for CO2 mitigation aligned with European climate targets.
Keywords: energy management; CO2 emissions; environmental impact; sustainability; Artificial Intelligence (AI); machine learning energy management; CO2 emissions; environmental impact; sustainability; Artificial Intelligence (AI); machine learning

Share and Cite

MDPI and ACS Style

Rey-Hernández, A.; San José-Alonso, J.; Picallo-Perez, A.; Rey-Martínez, F.J.; Elgharib, A.O.; Rey-Hernández, J.M.; Salem, K.M. A Predictive Approach for Energy Efficiency and Emission Reduction in University Campuses. Appl. Sci. 2025, 15, 9419. https://doi.org/10.3390/app15179419

AMA Style

Rey-Hernández A, San José-Alonso J, Picallo-Perez A, Rey-Martínez FJ, Elgharib AO, Rey-Hernández JM, Salem KM. A Predictive Approach for Energy Efficiency and Emission Reduction in University Campuses. Applied Sciences. 2025; 15(17):9419. https://doi.org/10.3390/app15179419

Chicago/Turabian Style

Rey-Hernández, Alberto, Julio San José-Alonso, Ana Picallo-Perez, Francisco J. Rey-Martínez, A. O. Elgharib, Javier M. Rey-Hernández, and Khaled M. Salem. 2025. "A Predictive Approach for Energy Efficiency and Emission Reduction in University Campuses" Applied Sciences 15, no. 17: 9419. https://doi.org/10.3390/app15179419

APA Style

Rey-Hernández, A., San José-Alonso, J., Picallo-Perez, A., Rey-Martínez, F. J., Elgharib, A. O., Rey-Hernández, J. M., & Salem, K. M. (2025). A Predictive Approach for Energy Efficiency and Emission Reduction in University Campuses. Applied Sciences, 15(17), 9419. https://doi.org/10.3390/app15179419

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