Evidence map›Paper›PMID 30654576›Full record

ArticleSensors (Basel, Switzerland)2019

Smart Meeting Room Usage Information and Prediction by Modelling Occupancy Profiles.

Unai Saralegui, Miguel Ángel Antón, Olatz Arbelaitz, Javier Muguerza

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

4 authors.

Unai SaraleguiTecnalia Research & Innovation, Mikeletegi 2, Donostia 20009, Spain. unai.saralegui@tecnalia.com.ORCID 0000-0002-9239-5737
Miguel Ángel AntónTecnalia Research & Innovation, Mikeletegi 2, Donostia 20009, Spain. mangel.anton@tecnalia.com.ORCID 0000-0002-3775-2906
Olatz ArbelaitzFaculty of Informatics, University of the Basque Country, UPV/EHU, Donostia 20018, Spain. olatz.arbelaitz@ehu.eus.ORCID 0000-0001-7371-2971
Javier MuguerzaFaculty of Informatics, University of the Basque Country, UPV/EHU, Donostia 20018, Spain. j.muguerza@ehu.eus.ORCID 0000-0002-7026-7371

Funding

Department of Education, Universities and Research of the Basque Government IT980-16Ministry of Economy and Competitiveness of the Spanish Government and the European Regional Development fund- ERDF TIN2017-85409-P
6 · The paper itself

Abstract

The monitoring of small houses and rooms has become possible due to the advances in IoT sensors, actuators and low power communication protocols in the last few years. As buildings are one of the biggest energy consuming entities, monitoring them has great interest for trying to avoid non-necessary energy waste. Moreover, human behaviour has been reported as being the main discrepancy source between energy usage simulations and real usage, so the ability to monitor and predict actions as opening windows, using rooms, etc. is gaining attention to develop stronger models which may lead to reduce the overall energy consumption of buildings, considering buildings thermal inertia and additional capabilities. In this paper, a case study is described in which four meeting rooms have been monitored to obtain information about the usage of the rooms and later use it to predict their future usage. The results show the possibility to deploy a simple and non-intrusive sensing system whose output could be used to develop advanced control strategies.

Indexed as

ambient intelligencebehaviour modellingbuildingsInternet of Things (IoT)occupancy detectionsensor networkssmart meeting room

Identifiers

PMID30654576
PMCPMC6359482

What OpenQuestion holds

Textmetadata
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.