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

• Gómez-Ruiz, Gabriel ^[1] ; Sánchez, Adolfo J. ^[2] ; Sánchez-Herrera, Reyes ^[1] ; Andújar, José M. ^[1]
  1. [1] Universidad de Huelva

     Universidad de Huelva

     Huelva, España

     
  2. [2] Department of Mechanical, Biomedical, and Manufacturing Engineering, Munster Technological University, Bishopstown, Cork, T12 P928, Ireland
• Localización: Jornadas de Automática, ISSN-e 3045-4093, Nº. 45, 2024
• Idioma: inglés
• DOI: 10.17979/ja-cea.2024.45.10818
• Enlaces
  • Texto completo
• Resumen
  • español

    Las cargas termostáticamente controladas (TCL, por sus siglas en inglés de ‘thermostatically controlled load’) juegan un papel crucial en la reducción del consumo energético en los edificios. Por tanto, es esencial el desarrollo de modelos precisos que permitan una implementación efectiva de estrategias de control que reduzcan la demanda energética. Con este objetivo, se ha desarrollado un modelo que representa el comportamiento térmico de una habitación bajo la influencia de un aire acondicionado (AC), como punto de partida de nuestra investigación en el modelado y control de TCL. En concreto, se utilizó un enfoque de modelado basado en datos recogidos de una plataforma construida específicamente para este fin y un algoritmo diseñado para determinar los estados de operación del AC. Los resultados conseguidos, basados en las métricas del error cuadrático medio (RMSE, por sus siglas en inglés de ‘root mean square error’) y del error máximo absoluto (MAXAE, por sus siglas en inglés de ‘maximum absolute error’), demostraron la efectividad del algoritmo propuesto y del modelo para capturar la dinámica térmica de la habitación bajo la influencia del AC.

  • English

    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.

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