Publication
Industrial Facility Electricity Consumption Forecast Using Artificial Neural Networks and Incremental Learning
| dc.contributor.author | Ramos, Daniel | |
| dc.contributor.author | Faria, Pedro | |
| dc.contributor.author | Vale, Zita | |
| dc.contributor.author | Mourinho, João | |
| dc.contributor.author | Correia, Regina | |
| dc.date.accessioned | 2021-03-04T18:08:19Z | |
| dc.date.available | 2021-03-04T18:08:19Z | |
| dc.date.issued | 2020 | |
| dc.description | This article belongs to the Special Issue Time Series Forecasting for Energy Consumption | pt_PT |
| dc.description.abstract | Society’s concerns with electricity consumption have motivated researchers to improve on the way that energy consumption management is done. The reduction of energy consumption and the optimization of energy management are, therefore, two major aspects to be considered. Additionally, load forecast provides relevant information with the support of historical data allowing an enhanced energy management, allowing energy costs reduction. In this paper, the proposed consumption forecast methodology uses an Artificial Neural Network (ANN) and incremental learning to increase the forecast accuracy. The ANN is retrained daily, providing an updated forecasting model. The case study uses 16 months of data, split in 5-min periods, from a real industrial facility. The advantages of using the proposed method are illustrated with the numerical results | pt_PT |
| dc.description.sponsorship | This work has received funding from Portugal 2020 under the SPEAR project (NORTE-01-0247-FEDER-040224), in the scope of the ITEA 3 SPEAR Project 16001, from FEDER Funds through the COMPETE program and from National Funds through FCT under the project UIDB/00760/2020 and CEECIND/02887/2017. | pt_PT |
| dc.description.version | info:eu-repo/semantics/publishedVersion | pt_PT |
| dc.identifier.doi | 10.3390/en13184774 | pt_PT |
| dc.identifier.uri | http://hdl.handle.net/10400.22/17285 | |
| dc.language.iso | eng | pt_PT |
| dc.publisher | MDPI | pt_PT |
| dc.relation.publisherversion | https://www.mdpi.com/1996-1073/13/18/4774 | pt_PT |
| dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | pt_PT |
| dc.subject | Artificial neural networks | pt_PT |
| dc.subject | Electricity consumption | pt_PT |
| dc.subject | Machine learning | pt_PT |
| dc.subject | Load forecast | pt_PT |
| dc.subject | Industrial facility | pt_PT |
| dc.title | Industrial Facility Electricity Consumption Forecast Using Artificial Neural Networks and Incremental Learning | pt_PT |
| dc.type | journal article | |
| dspace.entity.type | Publication | |
| oaire.citation.issue | 18 | pt_PT |
| oaire.citation.startPage | 4774 | pt_PT |
| oaire.citation.title | Energies | pt_PT |
| oaire.citation.volume | 13 | pt_PT |
| person.familyName | Faria | |
| person.familyName | Vale | |
| person.givenName | Pedro | |
| person.givenName | Zita | |
| person.identifier | 632184 | |
| person.identifier.ciencia-id | B212-2309-F9C3 | |
| person.identifier.ciencia-id | 721B-B0EB-7141 | |
| person.identifier.orcid | 0000-0002-5982-8342 | |
| 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 | article | pt_PT |
| relation.isAuthorOfPublication | 35e6a4ab-f644-4bc5-b6fc-9fd89c23d6c6 | |
| relation.isAuthorOfPublication | ff1df02d-0c0f-4db1-bf7d-78863a99420b | |
| relation.isAuthorOfPublication.latestForDiscovery | 35e6a4ab-f644-4bc5-b6fc-9fd89c23d6c6 |
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