Publication
A Review of the Main Machine Learning Methods for Predicting Residential Energy Consumption
| dc.contributor.author | Gonzalez-Briones, Alfonso | |
| dc.contributor.author | Hernandez, Guillermo | |
| dc.contributor.author | Pinto, Tiago | |
| dc.contributor.author | Vale, Zita | |
| dc.contributor.author | Corchado, Juan M. | |
| dc.date.accessioned | 2022-01-11T14:40:19Z | |
| dc.date.available | 2022-01-11T14:40:19Z | |
| dc.date.issued | 2019-11 | |
| dc.description.abstract | The ability to predict future energy consumption is very important for energy distribution companies because it allows them to estimate energy needs and supply them accordingly. Consumption prediction makes it possible for those companies to optimize their processes by, for example, providing them with knowledge about future periods of high energy demand or by enabling them to adapt their tariffs to customer consumption. Machine Learning techniques allow to predict future energy consumption on the basis of the customers' historical consumption and several other parameters. This article reviews some of the main machine learning models capable of predicting energy consumption, in our case study we use a specific set of data extracted from a two-year-period of a shoe store. Among the evaluated methods, Gradient Boosting has obtained an 86.3% success rate in predicting consumption. | pt_PT |
| dc.description.sponsorship | This work was carried out under the frame of the "Towards Sustainable Intelligent Mobility: Blockchain-based framework for IoT Security" Ref. RTI2018-095390-B-C32" project. The project was supported and funded by the Spanish Ministerio de Economıa, Industria y Competitividad. Retos de investigacion, ´Proyectos I+D+i. | pt_PT |
| dc.description.version | info:eu-repo/semantics/publishedVersion | pt_PT |
| dc.identifier.doi | 10.1109/EEM.2019.8916406 | pt_PT |
| dc.identifier.isbn | 978-1-72811-257-2 | |
| dc.identifier.uri | http://hdl.handle.net/10400.22/19386 | |
| dc.language.iso | eng | pt_PT |
| dc.peerreviewed | yes | pt_PT |
| dc.publisher | IEEE | pt_PT |
| dc.relation | RTI2018-095390-B-C32 | pt_PT |
| dc.rights.uri | http://creativecommons.org/licenses/by-nc-sa/4.0/ | pt_PT |
| dc.subject | Energy Forecasting | pt_PT |
| dc.subject | Machine Learning | pt_PT |
| dc.subject | Gradient Boosting | pt_PT |
| dc.subject | XGBoost | pt_PT |
| dc.subject | Lasso | pt_PT |
| dc.subject | SGDRegressor | pt_PT |
| dc.subject | MLP | pt_PT |
| dc.subject | Ridge regression | pt_PT |
| dc.title | A Review of the Main Machine Learning Methods for Predicting Residential Energy Consumption | pt_PT |
| dc.type | conference object | |
| dspace.entity.type | Publication | |
| oaire.citation.title | 20th International Conference on Intelligent System Application to Power Systems (ISAP) | pt_PT |
| person.familyName | Vale | |
| person.givenName | Zita | |
| person.identifier | 632184 | |
| person.identifier.ciencia-id | 721B-B0EB-7141 | |
| person.identifier.orcid | 0000-0002-4560-9544 | |
| person.identifier.rid | A-5824-2012 | |
| person.identifier.scopus-author-id | 7004115775 | |
| rcaap.rights | openAccess | pt_PT |
| rcaap.type | conferenceObject | pt_PT |
| relation.isAuthorOfPublication | ff1df02d-0c0f-4db1-bf7d-78863a99420b | |
| relation.isAuthorOfPublication.latestForDiscovery | ff1df02d-0c0f-4db1-bf7d-78863a99420b |
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