Publication
Using diverse sensors in load forecasting in an office building to support energy management
| dc.contributor.author | Ramos, Daniel | |
| dc.contributor.author | Teixeira, Brígida | |
| dc.contributor.author | Faria, Pedro | |
| dc.contributor.author | Gomes, Luis | |
| dc.contributor.author | Abrishambaf, Omid | |
| dc.contributor.author | Vale, Zita | |
| dc.date.accessioned | 2021-09-17T14:21:05Z | |
| dc.date.available | 2021-09-17T14:21:05Z | |
| dc.date.issued | 2020 | |
| dc.description.abstract | The increasing penetration of renewable energy sources led to the development of several energy management approaches. One of the main topics in this field is related to the load forecast in buildings, which can contribute to more intelligent and sustainable energy consumption. However, it is necessary to build a proper forecast model, capable of detecting an accurate consumption profile. The minimum effort to achieve this is to extract a historic with energy consumptions to use as input. Additional information should be considered in order to achieve improvements in forecasting results. This way, information regarding the day of the week is discussed as a reliable source of information that may enhance the load forecast. In this paper, two forecasting techniques, namely neural networks and support vector machine, are used to predict the energy consumption of a building for all 5 min from a period. The proposed model finds the best forecasting technique and determines if the additional information regarding the day of the week enhances the load forecast. In this case study, a period of two years and a half data with a 5-minute time interval is used. Moreover, several tests are performed for varied inputs to understand if the insights are consistent for these tests. This data has been adapted from an office building to illustrate the advantages of the proposed methodology. | pt_PT |
| dc.description.sponsorship | This work has received funding from FEDER Funds through COMPETE program and from National Funds through (FCT) under the projects UIDB/00760/2020, MAS-Society (PTDC/EEI-EEE/28954/2017), CEECIND/02887/2017, and SFRH/BD/144200/2019, and from ANI (project GREEDi). | pt_PT |
| dc.description.version | info:eu-repo/semantics/publishedVersion | pt_PT |
| dc.identifier.doi | 10.1016/j.egyr.2020.11.100 | pt_PT |
| dc.identifier.issn | 2352-4847 | |
| dc.identifier.uri | http://hdl.handle.net/10400.22/18418 | |
| dc.language.iso | eng | pt_PT |
| dc.publisher | Elsevier | pt_PT |
| dc.relation | CEECIND/02887/2017 | pt_PT |
| dc.relation.publisherversion | https://www.sciencedirect.com/science/article/pii/S2352484720315250?via%3Dihub | pt_PT |
| dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | pt_PT |
| dc.subject | Clustering | pt_PT |
| dc.subject | Data mining | pt_PT |
| dc.subject | Fuzzy C-means | pt_PT |
| dc.subject | Typical load profile | pt_PT |
| dc.subject | Unsupervised learning | pt_PT |
| dc.title | Using diverse sensors in load forecasting in an office building to support energy management | pt_PT |
| dc.type | journal article | |
| dspace.entity.type | Publication | |
| oaire.awardURI | info:eu-repo/grantAgreement/FCT/9471 - RIDTI/150159/PT | |
| oaire.awardURI | info:eu-repo/grantAgreement/FCT/OE/74543/PT | |
| oaire.citation.endPage | 187 | pt_PT |
| oaire.citation.startPage | 182 | pt_PT |
| oaire.citation.title | Energy Reports | pt_PT |
| oaire.citation.volume | 6 | pt_PT |
| oaire.fundingStream | 9471 - RIDTI | |
| oaire.fundingStream | OE | |
| person.familyName | Teixeira | |
| person.familyName | Faria | |
| person.familyName | Abrishambaf | |
| person.familyName | Vale | |
| person.givenName | Brígida | |
| person.givenName | Pedro | |
| person.givenName | Omid | |
| person.givenName | Zita | |
| person.identifier | FWGHOEYAAAAJ | |
| person.identifier | 632184 | |
| person.identifier.ciencia-id | 1B19-6E7A-A964 | |
| person.identifier.ciencia-id | B212-2309-F9C3 | |
| person.identifier.ciencia-id | 6F19-CB63-C8A8 | |
| person.identifier.ciencia-id | 7F1A-B942-5BD2 | |
| person.identifier.ciencia-id | 721B-B0EB-7141 | |
| person.identifier.orcid | 0000-0002-0848-5319 | |
| person.identifier.orcid | 0000-0002-5982-8342 | |
| person.identifier.orcid | 0000-0002-8597-3383 | |
| person.identifier.orcid | 0000-0002-4249-8367 | |
| person.identifier.orcid | 0000-0002-4560-9544 | |
| person.identifier.rid | A-5824-2012 | |
| person.identifier.scopus-author-id | 57189232486 | |
| person.identifier.scopus-author-id | 7004115775 | |
| project.funder.identifier | http://doi.org/10.13039/501100001871 | |
| project.funder.identifier | http://doi.org/10.13039/501100001871 | |
| project.funder.name | Fundação para a Ciência e a Tecnologia | |
| project.funder.name | Fundação para a Ciência e a Tecnologia | |
| rcaap.rights | openAccess | pt_PT |
| rcaap.type | article | pt_PT |
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