A Smart Home Energy Management System Using Two-Stage Non-Intrusive Appliance Load Monitoring over Fog-Cloud Analytics Based on Tridium's Niagara Framework for Residential Demand-Side Management.

artificial intelligence cloud computing demand-side management edge computing energy management system non-intrusive appliance load monitoring smart houses

Journal

Sensors (Basel, Switzerland)
ISSN: 1424-8220
Titre abrégé: Sensors (Basel)
Pays: Switzerland
ID NLM: 101204366

Informations de publication

Date de publication:
20 Apr 2021
Historique:
received: 24 03 2021
revised: 12 04 2021
accepted: 19 04 2021
entrez: 30 4 2021
pubmed: 1 5 2021
medline: 1 5 2021
Statut: epublish

Résumé

Electricity is a vital resource for various human activities, supporting customers' lifestyles in today's modern technologically driven society. Effective demand-side management (DSM) can alleviate ever-increasing electricity demands that arise from customers in downstream sectors of a smart grid. Compared with the traditional means of energy management systems, non-intrusive appliance load monitoring (NIALM) monitors relevant electrical appliances in a non-intrusive manner. Fog (edge) computing addresses the need to capture, process and analyze data generated and gathered by Internet of Things (IoT) end devices, and is an advanced IoT paradigm for applications in which resources, such as computing capability, of a central data center acted as cloud computing are placed at the edge of the network. The literature leaves NIALM developed over fog-cloud computing and conducted as part of a home energy management system (HEMS). In this study, a Smart HEMS prototype based on Tridium's Niagara Framework

Identifiants

pubmed: 33924090
pii: s21082883
doi: 10.3390/s21082883
pmc: PMC8074283
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Subventions

Organisme : Ministry of Science and Technology, Taiwan
ID : MOST 109-3116-F-006-017-CC2
Organisme : Ministry of Science and Technology, Taiwan
ID : MOST 109-2221-E-027-121-MY2
Organisme : First International Computer, Inc. (FIC), Taiwan
ID : O01109E048 (Industry-Academia Collaboration Project)

Références

Sensors (Basel). 2019 Sep 12;19(18):
pubmed: 31547320
Sensors (Basel). 2019 Oct 14;19(20):
pubmed: 31615009
Sensors (Basel). 2020 Feb 07;20(3):
pubmed: 32046133
Sensors (Basel). 2018 Sep 22;18(10):
pubmed: 30249018
Sensors (Basel). 2018 Apr 27;18(5):
pubmed: 29702607
Sensors (Basel). 2019 Aug 20;19(16):
pubmed: 31434283

Auteurs

Yung-Yao Chen (YY)

Department of Electronic and Computer Engineering, National Taiwan University of Science and Technology, Taipei 106335, Taiwan.

Ming-Hung Chen (MH)

Department of Electrical Engineering, Ming Chi University of Technology, New Taipei City 243303, Taiwan.

Che-Ming Chang (CM)

Business Development Department, First International Computer, Inc. (FIC), Taipei 11491, Taiwan.

Fu-Sheng Chang (FS)

Customer Support & RMA Team, First International Computer, Inc. (FIC), Taipei 11491, Taiwan.

Yu-Hsiu Lin (YH)

Graduate Institute of Automation Technology, National Taipei University of Technology, Taipei 106344, Taiwan.

Classifications MeSH