Bats: An Appliance Safety Hazards Factors Detection Algorithm with an Improved Nonintrusive Load Disaggregation Method

Wei Wang, Zilin Wang, Yanru Chen, Min Guo, Zhengyu Chen, Yi Niu, Huangeng Liu and Liangyin Chen
Additional contact information
Wei Wang: College of Computer Science, Sichuan University, Chengdu 610065, China
Zilin Wang: College of Computer Science, Sichuan University, Chengdu 610065, China
Yanru Chen: College of Computer Science, Sichuan University, Chengdu 610065, China
Min Guo: College of Computer Science, Sichuan University, Chengdu 610065, China
Zhengyu Chen: College of Computer Science, Sichuan University, Chengdu 610065, China
Yi Niu: College of Computer Science, Sichuan University, Chengdu 610065, China
Huangeng Liu: School of Mechanical Electronic and Information Engineering, China University of Mining and Technology, Beijing 100083, China
Liangyin Chen: College of Computer Science, Sichuan University, Chengdu 610065, China

Energies, 2021, vol. 14, issue 12, 1-18

Abstract: In an electrical safe microenvironment, all kinds of electrical appliances can be operated safely to ensure the safety of life and property. The significance of safety hazard factors detection is to detect safety hazards in advance, to remind the administrators to exclude risk, to reduce the unnecessary loss, and to ensure that the electrical operation is healthy and orderly before the occurrence of accidents. In this paper, batteries are selected as the primary research subject of safety detection because batteries are used more and more in the Internet of Things (IOT), and they often cause fire in the process of discharging and charging. The existing algorithms need to be embedded into the specialized sensor for each important electrical appliance. However, they are limited by the actual deployment, so it is extremely difficult to spread widely. According to the opinions above, an improved load disaggregation algorithm based on dictionary learning and sparse coding with optimal dictionary matrix period is proposed to detect potential safety hazards of battery loads. For safety-related electrical applications, doing so can increase interpretability. Through experiments, we test this algorithm on the REDD dataset, and compare it with the baseline algorithms (combinatorial optimization, factorial hidden Markov model, basic discriminative dictionary sparse coding algorithm) to achieve a degree of trust. The Mean Absolute Error (MAE) value is 8.26, which drops by 70%. The Root Mean Square Error (RMSE) value is 97.75, which is also better than those baseline algorithms.

Keywords: Internet of Things; power supply safety; safety hazard factors detection (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: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1996-1073/14/12/3547/pdf (application/pdf)
https://www.mdpi.com/1996-1073/14/12/3547/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:14:y:2021:i:12:p:3547-:d:574942

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().