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

Author

Listed:
  • Jose Aguilar

    (Centro de Microcomputación y Sistemas Distribuidos (CEMISID), Universidad de Los Andes, 5101 Mérida, Spain
    Grupo de Investigación, Desarrollo e Innovación en Tecnologías de Informacion y Comunicación (GIDITIC), Universidad EAFIT, Medellín 50022, Colombia)

  • 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)

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.

Suggested Citation

  • Jose Aguilar & Douglas Ardila & Andrés Avendaño & Felipe Macias & Camila White & José Gomez-Pulido & José Gutierrez de Mesa & Alberto Garces-Jimenez, 2020. "An Autonomic Cycle of Data Analysis Tasks for the Supervision of HVAC Systems of Smart Building," Energies, MDPI, vol. 13(12), pages 1-24, June.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:12:p:3103-:d:371981
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    References listed on IDEAS

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    1. Reynolds, Jonathan & Rezgui, Yacine & Kwan, Alan & Piriou, Solène, 2018. "A zone-level, building energy optimisation combining an artificial neural network, a genetic algorithm, and model predictive control," Energy, Elsevier, vol. 151(C), pages 729-739.
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    Cited by:

    1. Alberto Garces-Jimenez & Jose-Manuel Gomez-Pulido & Nuria Gallego-Salvador & Alvaro-Jose Garcia-Tejedor, 2021. "Genetic and Swarm Algorithms for Optimizing the Control of Building HVAC Systems Using Real Data: A Comparative Study," Mathematics, MDPI, vol. 9(18), pages 1-24, September.
    2. Chen, Zhelun & O’Neill, Zheng & Wen, Jin & Pradhan, Ojas & Yang, Tao & Lu, Xing & Lin, Guanjing & Miyata, Shohei & Lee, Seungjae & Shen, Chou & Chiosa, Roberto & Piscitelli, Marco Savino & Capozzoli, , 2023. "A review of data-driven fault detection and diagnostics for building HVAC systems," Applied Energy, Elsevier, vol. 339(C).
    3. Dhowmya Bhatt & Danalakshmi D & A. Hariharasudan & Marcin Lis & Marlena Grabowska, 2021. "Forecasting of Energy Demands for Smart Home Applications," Energies, MDPI, vol. 14(4), pages 1-19, February.
    4. Aguilar, J. & Garces-Jimenez, A. & R-Moreno, M.D. & García, Rodrigo, 2021. "A systematic literature review on the use of artificial intelligence in energy self-management in smart buildings," Renewable and Sustainable Energy Reviews, Elsevier, vol. 151(C).
    5. Ruijun Guo & Guobin Zhang & Qian Zhang & Lei Zhou & Haicun Yu & Meng Lei & You Lv, 2021. "An Adaptive Early Fault Detection Model of Induced Draft Fans Based on Multivariate State Estimation Technique," Energies, MDPI, vol. 14(16), pages 1-18, August.

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