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Context classification in energy resource management of residential buildings using Artificial Neural Network

2017Conference object Open access
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dc.contributor.authorMadureira, Bruno
dc.contributor.authorPinto, Tiago
dc.contributor.authorFernandes, Filipe
dc.contributor.authorVale, Zita
dc.contributor.authorRamos, Carlos
dc.date.accessioned2021-09-23T14:18:32Z
dc.date.available2021-09-23T14:18:32Z
dc.date.issued2017
dc.description.abstractThis paper proposes an Artificial Neural Network (ANN) based approach to classify different contexts, with the goal of enhancing the management of residential energy resources. The increasing penetration of renewable based generation has completely changed the paradigm of the power and energy sector. The intermittent nature of these resources requires the system to incentivize the adaptability of consumers in order to guarantee the balance between generation and consumption. This leads to the emergence of several incentives with the objective of increasing the flexibility from the consumer's side. This, allied to the increasing price of electricity, leads to an increasing need for consumers to adapt their consumption in order to improve energy efficiency, decrease energy bills, and achieve a better use of their own generation resources. With this, several House Management Systems (HMS), and Building Energy Management Systems (BEMS) have emerged. These systems allow adapting the consumption (or suggesting changes in consumers' habits) according to several factors. However, in order to make this management truly smart, there is a need for adaptation to different contexts, so that changes can be done accordingly to the different situations that are faced at each time. This paper addresses this problem by proposing a novel methodology that enables classifying different situations in different contexts, according to different contextual variables.pt_PT
dc.description.sponsorshipThe present work has been developed under the EUREKA - ITEA2 Project M2MGrids (ITEA-13011), Project SIMOCE (ANI|P2020 17690), and has received funding from FEDER Funds through COMPETE program and from National Funds through FCT under the project UID/EEA/00760/2013.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.doi10.1109/IntelliSys.2017.8324297pt_PT
dc.identifier.isbn978-1-5090-6435-9
dc.identifier.urihttp://hdl.handle.net/10400.22/18516
dc.language.isoengpt_PT
dc.publisherIEEEpt_PT
dc.relationITEA-13011pt_PT
dc.relationANI|P2020 17690pt_PT
dc.relation.publisherversionhttps://ieeexplore.ieee.org/document/8324297pt_PT
dc.rights.urihttp://creativecommons.org/licenses/by-sa/4.0/pt_PT
dc.subjectArtificial intelligencept_PT
dc.subjectArtificial Neural Networkspt_PT
dc.subjectContext awarenesspt_PT
dc.subjectHouse Management Systemspt_PT
dc.titleContext classification in energy resource management of residential buildings using Artificial Neural Networkpt_PT
dc.typeconference object
dspace.entity.typePublication
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/5876/UID%2FEEA%2F00760%2F2013/PT
oaire.citation.conferencePlaceLondon, UKpt_PT
oaire.citation.title2017 Intelligent Systems Conference (IntelliSys)pt_PT
oaire.fundingStream5876
person.familyNamePinto
person.familyNameFernandes
person.familyNameVale
person.familyNameRamos
person.givenNameTiago
person.givenNameFilipe
person.givenNameZita
person.givenNameCarlos
person.identifierR-000-T7J
person.identifier632184
person.identifier.ciencia-id2414-9B03-C4BB
person.identifier.ciencia-id721B-B0EB-7141
person.identifier.ciencia-id1011-FAFC-AEBA
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person.identifier.orcid0000-0002-4560-9544
person.identifier.orcid0000-0002-5143-1711
person.identifier.ridT-2245-2018
person.identifier.ridA-5824-2012
person.identifier.ridK-7403-2014
person.identifier.scopus-author-id35219107600
person.identifier.scopus-author-id7004115775
person.identifier.scopus-author-id7201559105
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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