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Classification Approaches to Foster the Use of Distributed Generation with Improved Remuneration

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dc.contributor.authorSilva, Cátia
dc.contributor.authorFaria, Pedro
dc.contributor.authorVale, Zita
dc.date.accessioned2021-02-19T16:41:53Z
dc.date.available2021-02-19T16:41:53Z
dc.date.issued2018
dc.description.abstractThere are currently efforts to implement the concept of smart grids throughout the electric sector. This will bring radical changes to the entire management of the sector. The energy market does not run away from the rule. In this way, virtual power players will be required to update their business models to introduce all the concepts that the context of smart grids imposes. Thus, in this article is proposed a method that aggregates distributed generation and consumers who belong to demand response programs. Optimized scheduling, resource aggregation and classification of possible new resources, rescheduling, and remuneration are the phases of the methodology proposed and presented in this article. The focus will be on classification phase and the main objective is to create rules, through a previously trained model, to be able to classify the new resources and help with the challenges that virtual power players may face. Thus, five classification methods were tested and compared: neural networks, Bayesian naïve classification, decision trees, k-nearest neighbor method, and lastly support vector machine method.pt_PT
dc.description.sponsorshipThe present work was done and funded in the scope of the following projects: CONTEST Project (P2020-23575), and UID/EEA/00760/2013 funded by FEDER Funds through COMPETE program and by National Funds through FCT.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.doi10.1109/SSCI.2018.8628764pt_PT
dc.identifier.isbn978-1-5386-9276-9
dc.identifier.urihttp://hdl.handle.net/10400.22/17055
dc.language.isoengpt_PT
dc.publisherIEEEpt_PT
dc.relation.publisherversionhttps://ieeexplore.ieee.org/document/8628764pt_PT
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/pt_PT
dc.subjectDistributed Generationpt_PT
dc.subjectNaïve Bayesianpt_PT
dc.subjectNeural Networkspt_PT
dc.subjectk-nearest neighborpt_PT
dc.subjectDecision Treespt_PT
dc.subjectSupport Vector Machinept_PT
dc.titleClassification Approaches to Foster the Use of Distributed Generation with Improved Remunerationpt_PT
dc.title.alternativeDistributed Generation with Improved Remunerationpt_PT
dc.typeconference object
dspace.entity.typePublication
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/5876/UID%2FEEA%2F00760%2F2013/PT
oaire.citation.conferencePlaceBangalore, Indiapt_PT
oaire.citation.endPage1644pt_PT
oaire.citation.startPage1639pt_PT
oaire.citation.title2018 IEEE Symposium Series on Computational Intelligence (SSCI)pt_PT
oaire.fundingStream5876
person.familyNameSilva
person.familyNameFaria
person.familyNameVale
person.givenNameCátia
person.givenNamePedro
person.givenNameZita
person.identifier632184
person.identifier.ciencia-id5318-DCFD-218D
person.identifier.ciencia-idB212-2309-F9C3
person.identifier.ciencia-id721B-B0EB-7141
person.identifier.orcid0000-0001-8306-4568
person.identifier.orcid0000-0002-5982-8342
person.identifier.orcid0000-0002-4560-9544
person.identifier.ridA-5824-2012
person.identifier.scopus-author-id7004115775
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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relation.isAuthorOfPublication35e6a4ab-f644-4bc5-b6fc-9fd89c23d6c6
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