| dc.contributor.author | Silva, José | |
| dc.contributor.author | Praça, Isabel | |
| dc.contributor.author | Pinto, Tiago | |
| dc.contributor.author | Vale, Zita | |
| dc.date.accessioned | 2021-01-29T15:05:09Z | |
| dc.date.available | 2021-01-29T15:05:09Z | |
| dc.date.issued | 2020 | |
| dc.description | DCAI 2019: Distributed Computing and Artificial Intelligence, 16th International Conference, Special Sessions | |
| dc.description.abstract | The increase of renewable energy sources of intermittent nature has brought several new challenges for power and energy systems. In order to deal with the variability from the generation side, there is the need to balance it by managing consumption appropriately. Forecasting energy consumption becomes, therefore, more relevant than ever. This paper presents and compares three different ensemble learning methods, namely random forests, gradient boosted regression trees and Adaboost. Hour-ahead electricity load forecasts are presented for the building N of GECAD at ISEP campus. The performance of the forecasting models is assessed, and results show that the Adaboost model is superior to the other considered models for the one-hour ahead forecasts. The results of this study compared to previous works indicates that ensemble learning methods are a viable choice for short-term load forecast. | 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 | 10.1007/978-3-030-23946-6_1 | pt_PT |
| dc.identifier.isbn | 978-3-030-23946-6 | |
| dc.identifier.uri | http://hdl.handle.net/10400.22/16797 | |
| dc.language.iso | eng | pt_PT |
| dc.publisher | Springer | pt_PT |
| dc.relation.publisherversion | https://link.springer.com/chapter/10.1007%2F978-3-030-23946-6_1 | pt_PT |
| dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | pt_PT |
| dc.subject | Electricity consumption | pt_PT |
| dc.subject | Short-term load forecast | pt_PT |
| dc.subject | Ensemble learning methods | pt_PT |
| dc.subject | Forecasting | pt_PT |
| dc.title | Energy Consumption Forecasting Using Ensemble Learning Algorithms | pt_PT |
| dc.type | book part | |
| dspace.entity.type | Publication | |
| oaire.citation.endPage | 13 | pt_PT |
| oaire.citation.startPage | 5 | pt_PT |
| oaire.citation.title | Advances in Intelligent Systems and Computing | pt_PT |
| oaire.citation.volume | 1004 | pt_PT |
| person.familyName | Praça | |
| person.familyName | Pinto | |
| person.familyName | Vale | |
| person.givenName | Isabel | |
| person.givenName | Tiago | |
| person.givenName | Zita | |
| person.identifier | 299522 | |
| person.identifier | R-000-T7J | |
| person.identifier | 632184 | |
| person.identifier.ciencia-id | C710-4218-1BFF | |
| person.identifier.ciencia-id | 2414-9B03-C4BB | |
| person.identifier.ciencia-id | 721B-B0EB-7141 | |
| person.identifier.orcid | 0000-0002-2519-9859 | |
| person.identifier.orcid | 0000-0001-8248-080X | |
| person.identifier.orcid | 0000-0002-4560-9544 | |
| person.identifier.rid | K-8430-2014 | |
| person.identifier.rid | T-2245-2018 | |
| person.identifier.rid | A-5824-2012 | |
| person.identifier.scopus-author-id | 22734900800 | |
| person.identifier.scopus-author-id | 35219107600 | |
| person.identifier.scopus-author-id | 7004115775 | |
| rcaap.rights | openAccess | pt_PT |
| rcaap.type | bookPart | pt_PT |
| relation.isAuthorOfPublication | ee4ecacd-c6c6-41e8-bca1-21a60ff05f50 | |
| relation.isAuthorOfPublication | 8d58ddc0-1023-47c0-a005-129d412ce98d | |
| relation.isAuthorOfPublication | ff1df02d-0c0f-4db1-bf7d-78863a99420b | |
| relation.isAuthorOfPublication.latestForDiscovery | ee4ecacd-c6c6-41e8-bca1-21a60ff05f50 |
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