Rule-Based Model for Smart Building Supervision and Management

Tamani, Nouredine; Ahvar, Shohreh; Santos, Gabriel; Istasse, Bernard; Praça, Isabel; Brun, Emmanuel; Ghamri, Yacine; Crespi, Noël; Bécue, Adrien

doi:10.1109/scc.2018.00009

Cited by 16 publications

(23 citation statements)

References 6 publications

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“…The supervision system interacts with the controller. The latter regulates the machines and/or processes and the former watches the activity to detect abnormal situations [2,3,23]. FDD is one of these supervision systems indicating abnormal conditions necessary to discover.…”

Section: Supervision Systemmentioning

confidence: 99%

“…Classifying fault severity level has three steps: the detection of the fault, its isolation and its identification. First-generation FDDs were based on rules and statistics and provided simplistic knowledge with a limited set of expected faults, and thus the support of field experts was unavoidable [3]. Today's generation uses ML techniques and stands out for detection and diagnosis [24,25].…”

Section: Supervision Systemmentioning

confidence: 99%

“…(a) The approach is based on the autonomous paradigm for the self-supervision process; (b) The approach considers the integration of several machine learning approaches for the supervision; (c) The approach is easily adaptable and extensible; (d) The approach considers different aspects for a correct supervision: detection, diagnosis, among others. [2] x x [3] x [23] x x x [26] x [32,35] x x Our work…”

Section: Comparison With Other Workmentioning

confidence: 99%

“…The implemented algorithm consists of several steps, including preparation, segmentation, aggregation and knowledge discovery stage, where classes of abstract models representing the energy efficiency constitute an information system representing diagnostic knowledge about the energy efficiency of buildings favorably operating under similar climate conditions and supplied from the same district heating network. The study [3] enables the supervision of buildings by the use of semantic technologies. They define an information base that describes the main physical and conceptual building elements, their characteristics and interrelationships, as well as the constraints that apply to them.…”

Section: Comparison With Other Workmentioning

confidence: 99%

“…The BMS processes the logs coming from the connected devices deployed in the building for controlling the equipment, supervising the system or optimizing the energy efficiency. The energy supervisory system is one of the key components of any BMS, comprising a meter module and an efficiency analyzer that captures abnormal situations [1][2][3]. The supervisory function shows what it is worth in case of unforeseen malfunction, such as hardware failures, voltage fluctuations, insufficient fluid pressure or temperature out of range.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

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

Aguilar¹,

Ardila²,

Avendaño³

et al. 2020

Energies

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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.

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Section: Supervision Systemmentioning

confidence: 99%

Section: Supervision Systemmentioning

confidence: 99%