Publicação
Dynamic artificial neural network for electricity market prices forecast
| Resumo: | This paper presents an artificial neural network applied to the forecasting of electricity market prices, with the special feature of being dynamic. The dynamism is verified at two different levels. The first level is characterized as a re-training of the network in every iteration, so that the artificial neural network can able to consider the most recent data at all times, and constantly adapt itself to the most recent happenings. The second level considers the adaptation of the neural network’s execution time depending on the circumstances of its use. The execution time adaptation is performed through the automatic adjustment of the amount of data considered for training the network. This is an advantageous and indispensable feature for this neural network’s integration in ALBidS (Adaptive Learning strategic Bidding System), a multi-agent system that has the purpose of providing decision support to the market negotiating players of MASCEM (Multi-Agent Simulator of Competitive Electricity Markets). |
|---|---|
| Autores principais: | Pinto, Tiago |
| Outros Autores: | Sousa, Tiago; Vale, Zita |
| Assunto: | Dynamic artificial neural network Electricity market prices forecast Artificial neural network Forecasting of electricity market prices |
| Ano: | 2012 |
| País: | Portugal |
| Tipo de documento: | documento de conferência |
| Tipo de acesso: | acesso restrito |
| Instituição associada: | Instituto Politécnico do Porto |
| Idioma: | inglês |
| Origem: | Repositório Científico do Instituto Politécnico do Porto |
| _version_ | 1868786083867656192 |
|---|---|
| author | Pinto, Tiago |
| author2 | Sousa, Tiago Vale, Zita |
| author2_role | author author |
| author_facet | Pinto, Tiago Sousa, Tiago Vale, Zita |
| author_role | author |
| contributor_name_str_mv | REPOSITÓRIO P.PORTO |
| country_str | PT |
| creators_json_txt | [{\"Person.name\":\"Pinto, Tiago\",\"Person.identifier.orcid\":\"0000-0001-8248-080X\"},{\"Person.name\":\"Sousa, Tiago\"},{\"Person.name\":\"Vale, Zita\",\"Person.identifier.orcid\":\"0000-0002-4560-9544\"}] |
| datacite.contributors.contributor.contributorName.fl_str_mv | REPOSITÓRIO P.PORTO |
| datacite.creators.creator.creatorName.fl_str_mv | Pinto, Tiago Sousa, Tiago Vale, Zita |
| datacite.date.Accepted.fl_str_mv | 2012-01-01T00:00:00Z |
| datacite.date.available.fl_str_mv | 2013-04-15T15:14:58Z |
| datacite.date.embargoed.fl_str_mv | 2013-04-15T15:14:58Z |
| datacite.rights.fl_str_mv | http://purl.org/coar/access_right/c_16ec |
| datacite.subjects.subject.fl_str_mv | Dynamic artificial neural network Electricity market prices forecast Artificial neural network Forecasting of electricity market prices |
| datacite.titles.title.fl_str_mv | Dynamic artificial neural network for electricity market prices forecast |
| dc.contributor.none.fl_str_mv | REPOSITÓRIO P.PORTO |
| dc.creator.none.fl_str_mv | Pinto, Tiago Sousa, Tiago Vale, Zita |
| dc.date.Accepted.fl_str_mv | 2012-01-01T00:00:00Z |
| dc.date.available.fl_str_mv | 2013-04-15T15:14:58Z |
| dc.date.embargoed.fl_str_mv | 2013-04-15T15:14:58Z |
| dc.format.none.fl_str_mv | application/pdf |
| dc.identifier.none.fl_str_mv | http://hdl.handle.net/10400.22/1318 |
| dc.language.none.fl_str_mv | eng |
| dc.publisher.none.fl_str_mv | IEEE |
| dc.rights.none.fl_str_mv | http://purl.org/coar/access_right/c_16ec |
| dc.subject.none.fl_str_mv | Dynamic artificial neural network Electricity market prices forecast Artificial neural network Forecasting of electricity market prices |
| dc.title.fl_str_mv | Dynamic artificial neural network for electricity market prices forecast |
| dc.type.none.fl_str_mv | http://purl.org/coar/resource_type/c_c94f |
| description | This paper presents an artificial neural network applied to the forecasting of electricity market prices, with the special feature of being dynamic. The dynamism is verified at two different levels. The first level is characterized as a re-training of the network in every iteration, so that the artificial neural network can able to consider the most recent data at all times, and constantly adapt itself to the most recent happenings. The second level considers the adaptation of the neural network’s execution time depending on the circumstances of its use. The execution time adaptation is performed through the automatic adjustment of the amount of data considered for training the network. This is an advantageous and indispensable feature for this neural network’s integration in ALBidS (Adaptive Learning strategic Bidding System), a multi-agent system that has the purpose of providing decision support to the market negotiating players of MASCEM (Multi-Agent Simulator of Competitive Electricity Markets). |
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| identifier.url.fl_str_mv | http://hdl.handle.net/10400.22/1318 |
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| institution | Instituto Politécnico do Porto |
| instname_str | Instituto Politécnico do Porto |
| language | eng |
| network_acronym_str | recipp |
| network_name_str | Repositório Científico do Instituto Politécnico do Porto |
| oai_identifier_str | oai:recipp.ipp.pt:10400.22/1318 |
| organization_str_mv | urn:organizationAcronym:recipp |
| person_str_mv | Pinto, Tiago Pinto, Tiago https://www.ciencia-id.pt/2414-9B03-C4BB 2414-9B03-C4BB http://orcid.org/0000-0001-8248-080X 0000-0001-8248-080X Sousa, Tiago Vale, Zita Vale, Zita https://www.ciencia-id.pt/721B-B0EB-7141 721B-B0EB-7141 http://orcid.org/0000-0002-4560-9544 0000-0002-4560-9544 |
| publishDate | 2012 |
| publisher.none.fl_str_mv | IEEE |
| reponame_str | Repositório Científico do Instituto Politécnico do Porto |
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| spelling | engIEEEporThis paper presents an artificial neural network applied to the forecasting of electricity market prices, with the special feature of being dynamic. The dynamism is verified at two different levels. The first level is characterized as a re-training of the network in every iteration, so that the artificial neural network can able to consider the most recent data at all times, and constantly adapt itself to the most recent happenings. The second level considers the adaptation of the neural network’s execution time depending on the circumstances of its use. The execution time adaptation is performed through the automatic adjustment of the amount of data considered for training the network. This is an advantageous and indispensable feature for this neural network’s integration in ALBidS (Adaptive Learning strategic Bidding System), a multi-agent system that has the purpose of providing decision support to the market negotiating players of MASCEM (Multi-Agent Simulator of Competitive Electricity Markets).application/pdfporDynamic artificial neural network for electricity market prices forecastPersonalPinto, TiagoDSpacehttp://dspace.org/items/8d58ddc0-1023-47c0-a005-129d412ce98dDSpacehttp://dspace.org/items/8d58ddc0-1023-47c0-a005-129d412ce98dPintoTiagoCiência IDhttps://www.ciencia-id.pt2414-9B03-C4BBORCIDhttp://orcid.org0000-0001-8248-080XResearcher IDhttps://www.researcherid.comT-2245-2018Scopus Author IDhttps://www.scopus.com35219107600Sousa, TiagoPersonalVale, ZitaDSpacehttp://dspace.org/items/ff1df02d-0c0f-4db1-bf7d-78863a99420bDSpacehttp://dspace.org/items/ff1df02d-0c0f-4db1-bf7d-78863a99420bValeZitaCiência IDhttps://www.ciencia-id.pt721B-B0EB-7141ORCIDhttp://orcid.org0000-0002-4560-9544Researcher IDhttps://www.researcherid.comA-5824-2012Researcher IDhttps://www.researcherid.comN-1643-2019Scopus Author IDhttps://www.scopus.com7004115775Scopus Author IDhttps://www.scopus.com57198257978Scopus Author IDhttps://www.scopus.com57203219788HostingInstitutionOrganizationalREPOSITÓRIO P.PORTOe-mailmailto:recipp@sc.ipp.ptrecipp@sc.ipp.ptISBNIsPartOf978-1-4673-2693-3ISBNIsPartOf978-1-4673-2694-0DOIIsPartOf10.1109/INES.2012.62498502013-04-15T15:14:58Z20122013-04-11T14:45:25Z2012-01-01T00:00:00ZHandlehttp://hdl.handle.net/10400.22/1318http://purl.org/coar/access_right/c_16ecrestricted accessDynamic artificial neural networkElectricity market prices forecastArtificial neural networkForecasting of electricity market prices665561 bytesother research producthttp://purl.org/coar/resource_type/c_c94fconference objecthttp://purl.org/coar/access_right/c_16ecapplication/pdffulltexthttps://recipp.ipp.pt/bitstreams/95d97f66-b5d9-4294-9623-c031ac74976f/download16th International Conference on Intelligent Engineering Systems 2012 (INES 2012311316Lisboa |
| spellingShingle | Dynamic artificial neural network for electricity market prices forecast Pinto, Tiago Dynamic artificial neural network Electricity market prices forecast Artificial neural network Forecasting of electricity market prices |
| status | SINGLETON |
| subject.fl_str_mv | Dynamic artificial neural network Electricity market prices forecast Artificial neural network Forecasting of electricity market prices |
| title | Dynamic artificial neural network for electricity market prices forecast |
| title_full | Dynamic artificial neural network for electricity market prices forecast |
| title_fullStr | Dynamic artificial neural network for electricity market prices forecast |
| title_full_unstemmed | Dynamic artificial neural network for electricity market prices forecast |
| title_short | Dynamic artificial neural network for electricity market prices forecast |
| title_sort | Dynamic artificial neural network for electricity market prices forecast |
| topic | Dynamic artificial neural network Electricity market prices forecast Artificial neural network Forecasting of electricity market prices |
| topic_facet | Dynamic artificial neural network Electricity market prices forecast Artificial neural network Forecasting of electricity market prices |
| url | http://hdl.handle.net/10400.22/1318 |
| visible | 1 |