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.
Highlights
Buildings consume above one-third of the total electrical energy supplied to the city
This Section describes the last task of the autonomous cycle that displays a dashboard, where the actual data stream is steadily monitored with its corresponding expected ranges
This study proposes a novel supervisory module for the management of building HVAC systems
Summary
Buildings consume above one-third of the total electrical energy supplied to the city. Previous studies propose an autonomous management architecture that operates on a multi-HVAC model based on the autonomic cycle of data analysis tasks (ACODAT) concept [1], leading to improving the energy efficiency and reducing costs. This management system gathers the data read from the system and environment sensors and regulate the controllers, following the multi-HVAC model predictions. Another article proposes the LAMDA (learning algorithm for multivariant data analysis) robust fuzzy-based control method for HVAC, being susceptible to be incorporated in the management system as an ACODAT [4] This study keeps this line of research and proves the idea of ACODAT for the supervision of building HVAC systems.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.