Evidence map›Paper›PMID 33924090›Full record

ArticleSensors (Basel, Switzerland)2021

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.

Yung-Yao Chen, Ming-Hung Chen, Che-Ming Chang, Fu-Sheng Chang, Yu-Hsiu Lin

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Yung-Yao ChenDepartment of Electronic and Computer Engineering, National Taiwan University of Science and Technology, Taipei 106335, Taiwan.ORCID 0000-0001-6852-8862
Ming-Hung ChenDepartment of Electrical Engineering, Ming Chi University of Technology, New Taipei City 243303, Taiwan.ORCID 0000-0003-2388-5086
Che-Ming ChangBusiness Development Department, First International Computer, Inc. (FIC), Taipei 11491, Taiwan.
Fu-Sheng ChangCustomer Support & RMA Team, First International Computer, Inc. (FIC), Taipei 11491, Taiwan.
Yu-Hsiu LinGraduate Institute of Automation Technology, National Taipei University of Technology, Taipei 106344, Taiwan.ORCID 0000-0002-1407-2262

Funding

First International Computer, Inc. (FIC), Taiwan O01109E048 (Industry-Academia Collaboration Project)Ministry of Science and Technology, Taiwan MOST 109-2221-E-027-121-MY2Ministry of Science and Technology, Taiwan MOST 109-3116-F-006-017-CC2
6 · The paper itself

Abstract

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

Indexed as

artificial intelligencecloud computingdemand-side managementedge computingenergy management systemnon-intrusive appliance load monitoringsmart houses

Identifiers

PMID33924090
PMCPMC8074283

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.