Capturing Thermal Dynamics in Air-Conditioned Rooms A Data-Driven Approach

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Publicado: jul. 16, 2024

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Gabriel Gómez-Ruiz
Research Centre CITES, University of Huelva, 21007 Huelva, Spain
España
https://orcid.org/0000-0002-0532-6486
Adolfo J. Sánchez
Department of Mechanical, Biomedical, and Manufacturing Engineering, Munster Technological University, Bishopstown, Cork, T12 P928, Ireland
Irlanda
https://orcid.org/0000-0003-3942-847X
Reyes Sánchez-Herrera
Research Centre CITES, University of Huelva, 21007 Huelva, Spain
España
https://orcid.org/0000-0003-3099-7262
José M. Andújar
Research Centre CITES, University of Huelva, 21007 Huelva, Spain
España
https://orcid.org/0000-0002-0631-0021
Núm. 45 (2024), Modelado, Simulación y Optimización
DOI: https://doi.org/10.17979/ja-cea.2024.45.10818
Recibido: may. 30, 2024 Aceptado: jul. 5, 2024 Publicado: jul. 16, 2024
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Resumen

Thermostatically controlled loads (TCLs) play a crucial role in reducing energy consumption in buildings. Thus, developing accurate models that enable the effective implementation of energy control strategies is essential. With this goal in mind, a model of a room influenced by an air conditioning (AC) unit was developed as an initial starting point for our research into TCL systems modeling and control. In this work, a data-driven modeling approach was utilized, employing data collected from an ad-hoc data collection platform. In addition, an algorithm was developed to determine the AC’s operational states. The results, based on RMSE (Root Mean Square Error) and MAXAE (Maximum Absolute Error) metrics, demonstrate the effectiveness of the proposed algorithm and data-driven modeling approach in capturing the thermal dynamics of the room under the influence of the AC unit.

Palabras clave:

filtering and smoothing, identifiability, identification for control, software for system identification, model validation

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