Publication
Classification of local energy trading negotiation profiles using artificial neural networks
| dc.contributor.author | Pinto, Angelo | |
| dc.contributor.author | Pinto, Tiago | |
| dc.contributor.author | Praça, Isabel | |
| dc.contributor.author | Vale, Zita | |
| dc.date.accessioned | 2022-03-17T10:46:39Z | |
| dc.date.available | 2022-03-17T10:46:39Z | |
| dc.date.issued | 2019 | |
| dc.description.abstract | Electricity markets are evolving into a local trading setting, which makes it for unexperienced players to achieve good agreements and obtain profits. One of the solutions to deal with this issue is to provide players with decision support solutions capable of identifying opponents' negotiation profiles, so that negotiation strategies can be adapted to those profiles in order to reach the best possible results from negotiations. This paper presents an approach that classifies opponents' proposals during a negotiation, to determine which is the typical negotiation profile in which the opponent most relates. The classification process is performed using an artificial neural network approach, and it is able to adapt at each new proposal during the negotiation process, by re-classifying the opponents' negotiation profile according to the most recent actions. In this way, effective decision support is provided to market players, enabling them to adapt the negotiation strategy throughout the negotiations. | pt_PT |
| dc.description.sponsorship | This work has received funding from National Funds through FCT (Fundaçao da Ciencia e Tecnologia) under the project SPET – 29165, call SAICT 2017 | pt_PT |
| dc.description.version | info:eu-repo/semantics/publishedVersion | pt_PT |
| dc.identifier.doi | 10.1109/PESGM40551.2019.8973600 | pt_PT |
| dc.identifier.isbn | 978-1-7281-1981-6 | |
| dc.identifier.issn | 1944-9933 | |
| dc.identifier.uri | http://hdl.handle.net/10400.22/20274 | |
| dc.language.iso | eng | pt_PT |
| dc.peerreviewed | yes | pt_PT |
| dc.publisher | IEEE | pt_PT |
| dc.relation.publisherversion | https://ieeexplore.ieee.org/document/8973600 | pt_PT |
| dc.rights.uri | http://creativecommons.org/licenses/by-nc-sa/4.0/ | pt_PT |
| dc.subject | Artificial neural networks | pt_PT |
| dc.subject | Classification | pt_PT |
| dc.subject | Electricity markets | pt_PT |
| dc.subject | Profile modelling | pt_PT |
| dc.title | Classification of local energy trading negotiation profiles using artificial neural networks | pt_PT |
| dc.type | conference object | |
| dspace.entity.type | Publication | |
| oaire.citation.conferencePlace | Atalanta, USA | pt_PT |
| oaire.citation.endPage | 5 | pt_PT |
| oaire.citation.startPage | 1 | pt_PT |
| oaire.citation.title | 2019 IEEE Power & Energy Society General Meeting (PESGM) | pt_PT |
| person.familyName | Praça | |
| person.familyName | Vale | |
| person.givenName | Isabel | |
| person.givenName | Zita | |
| person.identifier | 299522 | |
| person.identifier | 632184 | |
| person.identifier.ciencia-id | C710-4218-1BFF | |
| person.identifier.ciencia-id | 721B-B0EB-7141 | |
| person.identifier.orcid | 0000-0002-2519-9859 | |
| person.identifier.orcid | 0000-0002-4560-9544 | |
| person.identifier.rid | K-8430-2014 | |
| person.identifier.rid | A-5824-2012 | |
| person.identifier.scopus-author-id | 22734900800 | |
| person.identifier.scopus-author-id | 7004115775 | |
| rcaap.rights | openAccess | pt_PT |
| rcaap.type | conferenceObject | pt_PT |
| relation.isAuthorOfPublication | ee4ecacd-c6c6-41e8-bca1-21a60ff05f50 | |
| relation.isAuthorOfPublication | ff1df02d-0c0f-4db1-bf7d-78863a99420b | |
| relation.isAuthorOfPublication.latestForDiscovery | ee4ecacd-c6c6-41e8-bca1-21a60ff05f50 |
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