Complex artificial intelligence models for energy sustainability in educational buildings.

Autor: Tariq R; Institute for the Future of Education, Tecnologico de Monterrey, Ave. Eugenio Garza Sada 2501, 64849, Monterrey, NL, Mexico. rasikhtariq@tec.mx., Mohammed A; Architectural Engineering and Construction Management Department, King Fahd University of Petroleum and Minerals, 31261, Dhahran, Saudi Arabia. awsan.mohammed@kfupm.edu.sa.; Interdisciplinary Research Center for Smart Mobility and Logistics, King Fahd University of Petroleum and Minerals, 31261, Dhahran, Saudi Arabia. awsan.mohammed@kfupm.edu.sa., Alshibani A; Architectural Engineering and Construction Management Department, King Fahd University of Petroleum and Minerals, 31261, Dhahran, Saudi Arabia.; Interdisciplinary Research Center of Construction and Building Materials, King Fahd University of Petroleum and Minerals, 34463, Dhahran, Saudi Arabia., Ramírez-Montoya MS; Institute for the Future of Education, Tecnologico de Monterrey, Ave. Eugenio Garza Sada 2501, 64849, Monterrey, NL, Mexico.; EGADE Business School, Tecnologico de Monterrey, 64849, Monterrey, NL, Mexico.
Jazyk: angličtina
Zdroj: Scientific reports [Sci Rep] 2024 Jul 01; Vol. 14 (1), pp. 15020. Date of Electronic Publication: 2024 Jul 01.
DOI: 10.1038/s41598-024-65727-5
Abstrakt: Energy consumption of constructed educational facilities significantly impacts economic, social and environment sustainable development. It contributes to approximately 37% of the carbon dioxide emissions associated with energy use and procedures. This paper aims to introduce a study that investigates several artificial intelligence-based models to predict the energy consumption of the most important educational buildings; schools. These models include decision trees, K-nearest neighbors, gradient boosting, and long-term memory networks. The research also investigates the relationship between the input parameters and the yearly energy usage of educational buildings. It has been discovered that the school sizes and AC capacities are the most impact variable associated with higher energy consumption. While 'Type of School' is less direct or weaker correlation with 'Annual Consumption'. The four developed models were evaluated and compared in training and testing stages. The Decision Tree model demonstrates strong performance on the training data with an average prediction error of about 3.58%. The K-Nearest Neighbors model has significantly higher errors, with RMSE on training data as high as 38,429.4, which may be indicative of overfitting. In contrast, Gradient Boosting can almost perfectly predict the variations within the training dataset. The performance metrics suggest that some models manage this variability better than others, with Gradient Boosting and LSTM standing out in terms of their ability to handle diverse data ranges, from the minimum consumption of approximately 99,274.95 to the maximum of 683,191.8. This research underscores the importance of sustainable educational buildings not only as physical learning spaces but also as dynamic environments that contribute to informal educational processes. Sustainable buildings serve as real-world examples of environmental stewardship, teaching students about energy efficiency and sustainability through their design and operation. By incorporating advanced AI-driven tools to optimize energy consumption, educational facilities can become interactive learning hubs that encourage students to engage with concepts of sustainability in their everyday surroundings.
(© 2024. The Author(s).)
Databáze: MEDLINE
Externí odkaz: