Publication
Data-Mining-based filtering to support Solar Forecasting Methodologies
| dc.contributor.author | Pinto, Tiago | |
| dc.contributor.author | Marques, Luis | |
| dc.contributor.author | Sousa, Tiago M | |
| dc.contributor.author | Praça, Isabel | |
| dc.contributor.author | Vale, Zita | |
| dc.contributor.author | Abreu, Samuel L | |
| dc.date.accessioned | 2021-09-22T11:26:04Z | |
| dc.date.available | 2021-09-22T11:26:04Z | |
| dc.date.issued | 2017 | |
| dc.description.abstract | This paper proposes an hybrid approach for short term solar intensity forecasting, which combines different forecasting methodologies with a clustering algorithm, which plays the role of data filter, in order to support the selection of the best data for training. A set of methodologies based on Artificial Neural Networks (ANN) and Support Vector Machines (SVM), used for short term solar irradiance forecast, is implemented and compared in order to facilitate the selection of the most appropriate methods and respective parameters according to the available information and needs. Data from the Brazilian city of Florianópolis, in the state of Santa Catarina, has been used to illustrate the methods applicability and conclusions. The dataset comprises the years of 1990 to 1999 and includes four solar irradiance components as well as other meteorological variables, such as temperature, wind speed and humidity. Conclusions about the irradiance components, parameters and the proposed clustering mechanism are presented. The results are studied and analysed considering both efficiency and effectiveness of the results. The experimental findings show that the hybrid model, combining a SVM approach with a clustering mechanism, to filter the data used for training, achieved promising results, outperforming the approaches without clustering. | pt_PT |
| dc.description.sponsorship | This work has been developed under the European Union’s Horizon 2020 research and innovation programme, Marie Sklodowska-Curie grant agreement No 703689 (project ADAPT); EUREKA - ITEA2 Project FUSE-IT (ITEA-13023) and Project GREEDI (ANI|P2020 17822). | pt_PT |
| dc.description.version | info:eu-repo/semantics/publishedVersion | pt_PT |
| dc.identifier.doi | 10.14201/ADCAIJ20176385102 | pt_PT |
| dc.identifier.issn | 2255-2863 | |
| dc.identifier.uri | http://hdl.handle.net/10400.22/18474 | |
| dc.language.iso | eng | pt_PT |
| dc.publisher | University of Salamanca | pt_PT |
| dc.relation | ITEA-13023 | pt_PT |
| dc.relation | Adaptive Decision support for Agents negotiation in electricity market and smart grid Power Transactions | |
| dc.relation.publisherversion | https://revistas.usal.es/index.php/2255-2863/article/view/ADCAIJ20176385102 | pt_PT |
| dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | pt_PT |
| dc.subject | Artificial Neural Network | pt_PT |
| dc.subject | Clustering | pt_PT |
| dc.subject | Data Mining | pt_PT |
| dc.subject | Machine Learning | pt_PT |
| dc.subject | Solar Forecasting | pt_PT |
| dc.subject | Support Vector Machine | pt_PT |
| dc.title | Data-Mining-based filtering to support Solar Forecasting Methodologies | pt_PT |
| dc.type | journal article | |
| dspace.entity.type | Publication | |
| oaire.awardTitle | Adaptive Decision support for Agents negotiation in electricity market and smart grid Power Transactions | |
| oaire.awardURI | info:eu-repo/grantAgreement/EC/H2020/703689/EU | |
| oaire.citation.endPage | 102 | pt_PT |
| oaire.citation.issue | 3 | pt_PT |
| oaire.citation.startPage | 85 | pt_PT |
| oaire.citation.title | ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal | pt_PT |
| oaire.citation.volume | 6 | pt_PT |
| oaire.fundingStream | H2020 | |
| person.familyName | Pinto | |
| person.familyName | Praça | |
| person.familyName | Vale | |
| person.givenName | Tiago | |
| person.givenName | Isabel | |
| person.givenName | Zita | |
| person.identifier | R-000-T7J | |
| person.identifier | 299522 | |
| person.identifier | 632184 | |
| person.identifier.ciencia-id | 2414-9B03-C4BB | |
| person.identifier.ciencia-id | C710-4218-1BFF | |
| person.identifier.ciencia-id | 721B-B0EB-7141 | |
| person.identifier.orcid | 0000-0001-8248-080X | |
| person.identifier.orcid | 0000-0002-2519-9859 | |
| person.identifier.orcid | 0000-0002-4560-9544 | |
| person.identifier.rid | T-2245-2018 | |
| person.identifier.rid | K-8430-2014 | |
| person.identifier.rid | A-5824-2012 | |
| person.identifier.scopus-author-id | 35219107600 | |
| person.identifier.scopus-author-id | 22734900800 | |
| person.identifier.scopus-author-id | 7004115775 | |
| project.funder.identifier | http://doi.org/10.13039/501100008530 | |
| project.funder.name | European Commission | |
| rcaap.rights | openAccess | pt_PT |
| rcaap.type | article | pt_PT |
| relation.isAuthorOfPublication | 8d58ddc0-1023-47c0-a005-129d412ce98d | |
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