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CO2 Concentration Forecasting in an Office Using Artificial Neural Network

2019Conference object Open access
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dc.contributor.authorKhorram Ghahfarrokhi, Mahsa
dc.contributor.authorFaria, Pedro
dc.contributor.authorAbrishambaf, Omid
dc.contributor.authorVale, Zita
dc.contributor.authorSoares, João
dc.date.accessioned2021-09-22T15:18:49Z
dc.date.available2021-09-22T15:18:49Z
dc.date.issued2019
dc.description.abstractUncertainty is the state of all operation, components, and objective environment that makes impossible to describe the existing state. Forecasting techniques are essential in the field of knowledge development to overcome the uncertainty to increase the efficiency of all systems. In this paper, artificial neural network algorithm is applied to forecast the CO2 concentration in an office building. The algorithm is implemented in Rstudio software using neural net package. The case study of the paper presents two scenarios with different input data to propose the impacts of train data on forecasting algorithms results. The used dataset in the case study is real data that have been monitored for 2 years. The obtained results of algorithms show the predicted values of CO2 concentration in one office for 600 minutes of a working day. The mean percentage error means absolute percentage error, and standard deviation of predicted data for both scenarios are presented in results section.pt_PT
dc.description.sponsorshipThe present work was done and funded in the scope of the following projects: COLORS Project, CEECIND/02887/2017, and UID/EEA/00760/2019 funded by FEDER Funds through COMPETE program and National Funds through FCT.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.doi10.1109/ISAP48318.2019.9065944pt_PT
dc.identifier.isbn978-1-7281-3192-4
dc.identifier.urihttp://hdl.handle.net/10400.22/18488
dc.language.isoengpt_PT
dc.publisherIEEEpt_PT
dc.relationCEECIND/02887/2017pt_PT
dc.relationResearch Group on Intelligent Engineering and Computing for Advanced Innovation and Development
dc.relation.publisherversionhttps://ieeexplore.ieee.org/document/9065944pt_PT
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/4.0/pt_PT
dc.subjectArtificial Neural Networkpt_PT
dc.subjectCO2pt_PT
dc.subjectForecastingpt_PT
dc.titleCO2 Concentration Forecasting in an Office Using Artificial Neural Networkpt_PT
dc.typeconference object
dspace.entity.typePublication
oaire.awardTitleResearch Group on Intelligent Engineering and Computing for Advanced Innovation and Development
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UID%2FEEA%2F00760%2F2019/PT
oaire.citation.conferencePlaceNew Delhi, Indiapt_PT
oaire.citation.endPage6pt_PT
oaire.citation.startPage1pt_PT
oaire.citation.title2019 20th International Conference on Intelligent System Application to Power Systems (ISAP)pt_PT
oaire.fundingStream6817 - DCRRNI ID
person.familyNameKhorram Ghahfarrokhi
person.familyNameFaria
person.familyNameAbrishambaf
person.familyNameVale
person.familyNameSoares
person.givenNameMahsa
person.givenNamePedro
person.givenNameOmid
person.givenNameZita
person.givenNameJoão
person.identifier632184
person.identifier1043580
person.identifier.ciencia-id9719-75D7-B785
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person.identifier.ciencia-id1612-8EA8-D0E8
person.identifier.orcid0000-0002-0581-2898
person.identifier.orcid0000-0002-5982-8342
person.identifier.orcid0000-0002-4249-8367
person.identifier.orcid0000-0002-4560-9544
person.identifier.orcid0000-0002-4172-4502
person.identifier.ridA-5824-2012
person.identifier.scopus-author-id57201796142
person.identifier.scopus-author-id57189232486
person.identifier.scopus-author-id7004115775
person.identifier.scopus-author-id35436109600
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
rcaap.rightsopenAccesspt_PT
rcaap.typeconferenceObjectpt_PT
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