An Autonomic Cycle of Data Analysis Tasks for the Supervision of HVAC Systems of Smart Building

Jose Aguilar, Douglas Ardila, Andrés Avendaño, Felipe Macias, Camila White, José Gomez-Pulido, José Gutierrez de Mesa and Alberto Garces-Jimenez
Additional contact information
Jose Aguilar: Centro de Microcomputación y Sistemas Distribuidos (CEMISID), Universidad de Los Andes, 5101 Mérida, Spain
Douglas Ardila: Grupo de Investigación, Desarrollo e Innovación en Tecnologías de Informacion y Comunicación (GIDITIC), Universidad EAFIT, Medellín 50022, Colombia
Andrés Avendaño: Grupo de Investigación, Desarrollo e Innovación en Tecnologías de Informacion y Comunicación (GIDITIC), Universidad EAFIT, Medellín 50022, Colombia
Felipe Macias: Grupo de Investigación, Desarrollo e Innovación en Tecnologías de Informacion y Comunicación (GIDITIC), Universidad EAFIT, Medellín 50022, Colombia
Camila White: Grupo de Investigación, Desarrollo e Innovación en Tecnologías de Informacion y Comunicación (GIDITIC), Universidad EAFIT, Medellín 50022, Colombia
José Gomez-Pulido: Departamento Ciencias de la Computación, Universidad de Alcalá, 28805 Alcalá de Henares, Spain
José Gutierrez de Mesa: Departamento Ciencias de la Computación, Universidad de Alcalá, 28805 Alcalá de Henares, Spain
Alberto Garces-Jimenez: Centro de Innovación Experimental del Conocimiento (CEIEC), Universidad Francisco de Vitoria, 28223 Pozuelo de Alarcón, Spain

Energies, 2020, vol. 13, issue 12, 1-24

Abstract: Early fault detection and diagnosis in heating, ventilation and air conditioning (HVAC) systems may reduce the damage of equipment, improving the reliability and safety of smart buildings, generating social and economic benefits. Data models for fault detection and diagnosis are increasingly used for extracting knowledge in the supervisory tasks. This article proposes an autonomic cycle of data analysis tasks (ACODAT) for the supervision of the building’s HVAC systems. Data analysis tasks incorporate data mining models for extracting knowledge from the system monitoring, analyzing abnormal situations and automatically identifying and taking corrective actions. This article shows a case study of a real building’s HVAC system, for the supervision with our ACODAT, where the HVAC subsystems have been installed over the years, providing a good example of a heterogeneous facility. The proposed supervisory functionality of the HVAC system is capable of detecting deviations, such as faults or gradual increment of energy consumption in similar working conditions. The case study shows this capability of the supervisory autonomic cycle, usually a key objective for smart buildings.

Keywords: HVAC system; supervisory system; building management systems; autonomic computing (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

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