Design of Multi-Information Fusion Based Intelligent Electrical Fire Detection System for Green Buildings
Top Cited Papers
Open Access
- 18 March 2021
- journal article
- research article
- Published by MDPI AG in Sustainability
- Vol. 13 (6), 3405
- https://doi.org/10.3390/su13063405
Abstract
Building management systems are costly for small- to medium-sized buildings. A massive volume of data is collected on different building contexts by the Internet of Things (IoT), which is then further monitored. This intelligence is integrated into building management systems (BMSs) for energy consumption management in a cost-effective manner. Electric fire safety is paramount in buildings, especially in hospitals. Facility managers focus on fire protection strategies and identify where system upgrades are needed to maintain existing technologies. Furthermore, BMSs in hospitals should minimize patient disruption and be immune to nuisance alarms. This paper proposes an intelligent detection technology for electric fires based on multi-information fusion for green buildings. The system model was established by using fuzzy logic reasoning. The extracted multi-information fusion was used to detect the arc fault, which often causes electrical fires in the low-voltage distribution system of green buildings. The reliability of the established multi-information fusion model was verified by simulation. Using fuzzy logic reasoning and the membership function in fuzzy set theory to solve the uncertain relationship between faults and symptoms is a widely applied method. In order to realize the early prediction and precise diagnosis of faults, a fuzzy reasoning system was applied to analyze the arcs causing electrical fires in the lines. In order to accurately identify the fault arcs that easily cause electrical fires in low-voltage distribution systems for building management, this paper introduces in detail a fault identification method based on multi-information fusion, which can consolidate the complementary advantages of different types of judgment. The results demonstrate that the multi-information fusion method reduces the deficiency of a single criterion in fault arc detection and prevents electrical fires in green buildings more comprehensively and accurately. For the real-time dataset, the data results are presented, showing disagreements among the testing methods.Keywords
This publication has 31 references indexed in Scilit:
- System design of the internet of things for residential smart gridIEEE Wireless Communications, 2016
- SSD: Single Shot MultiBox DetectorPublished by Springer Science and Business Media LLC ,2016
- Smart Random Neural Network Controller for HVAC Using Cloud Computing TechnologyIEEE Transactions on Industrial Informatics, 2016
- Big data driven smart energy management: From big data to big insightsRenewable and Sustainable Energy Reviews, 2016
- Optimizing the selection of sustainability measures to minimize life-cycle cost of existing buildingsCanadian Journal of Civil Engineering, 2016
- Optimizing watchtower locations for forest fire monitoring using location modelsFire Safety Journal, 2015
- Multi-Sensor Building Fire Alarm System with Information Fusion Technology Based on D-S Evidence TheoryAlgorithms, 2014
- Effective and Comfortable Power Control Model Using Kalman Filter for Building Energy ManagementWireless Personal Communications, 2013
- Distributed sensing based on intelligent sensor networksIEEE Circuits and Systems Magazine, 2008
- New Technology for Preventing Residential Electrical Fires: Arc-Fault Circuit Interrupters (AFCIs)Fire Technology, 2000