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Use of Sensors and Analyzers Data for Load Forecasting: A Two Stage Approach

2020Journal article Open access
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dc.contributor.authorRamos, Daniel
dc.contributor.authorTeixeira, Brigida
dc.contributor.authorFaria, Pedro
dc.contributor.authorGomes, Luis
dc.contributor.authorAbrishambaf, Omid
dc.contributor.authorVale, Zita
dc.date.accessioned2021-03-04T18:12:58Z
dc.date.available2021-03-04T18:12:58Z
dc.date.issued2020
dc.descriptionThis article belongs to the Special Issue Sensors for Smart Gridspt_PT
dc.description.abstractThe increase in sensors in buildings and home automation bring potential information to improve buildings' energy management. One promissory field is load forecasting, where the inclusion of other sensors' data in addition to load consumption may improve the forecasting results. However, an adequate selection of sensor parameters to use as input to the load forecasting should be done. In this paper, a methodology is proposed that includes a two-stage approach to improve the use of sensor data for a specific building. As an innovation, in the first stage, the relevant sensor data is selected for each specific building, while in the second stage, the load forecast is updated according to the actual forecast error. When a certain error is reached, the forecasting algorithm (Artificial Neural Network or K-Nearest Neighbors) is trained with the most recent data instead of training the algorithm every time. Data collection is provided by a prototype of agent-based sensors developed by the authors in order to support the proposed methodology. In this case study, data over a period of six months with five-minute time intervals regarding eight types of sensors are used. These data have been adapted from an office building to illustrate the advantages of the proposed methodology.pt_PT
dc.description.sponsorshipThis work has received funding from Portugal 2020 under SPEAR project (NORTE-01-0247-FEDER-040224), in the scope of ITEA 3 SPEAR Project 16001 and from FEDER Funds through COMPETE program and from National Funds through (FCT) under the project UIDB/00760/2020, and CEECIND/02887/2017pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.doi10.3390/s20123524pt_PT
dc.identifier.issn1424-8220
dc.identifier.urihttp://hdl.handle.net/10400.22/17286
dc.language.isoengpt_PT
dc.publisherMDPIpt_PT
dc.relation.publisherversionhttps://www.mdpi.com/1424-8220/20/12/3524pt_PT
dc.rights.urihttp://creativecommons.org/licenses/by-sa/4.0/pt_PT
dc.subjectDemand responsept_PT
dc.subjectBuilding energy managementpt_PT
dc.subjectSCADApt_PT
dc.subjectUser Comfortpt_PT
dc.subjectLoad shiftingpt_PT
dc.titleUse of Sensors and Analyzers Data for Load Forecasting: A Two Stage Approachpt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.citation.issue12pt_PT
oaire.citation.startPage3524pt_PT
oaire.citation.titleSensorspt_PT
oaire.citation.volume20pt_PT
person.familyNameFaria
person.familyNameVale
person.givenNamePedro
person.givenNameZita
person.identifier632184
person.identifier.ciencia-idB212-2309-F9C3
person.identifier.ciencia-id6F19-CB63-C8A8
person.identifier.ciencia-id721B-B0EB-7141
person.identifier.orcid0000-0002-5982-8342
person.identifier.orcid0000-0002-8597-3383
person.identifier.orcid0000-0002-4560-9544
person.identifier.ridA-5824-2012
person.identifier.scopus-author-id7004115775
rcaap.rightsopenAccesspt_PT
rcaap.typearticlept_PT
relation.isAuthorOfPublication35e6a4ab-f644-4bc5-b6fc-9fd89c23d6c6
relation.isAuthorOfPublicationeaac2304-a007-4531-8398-ee9f426c2f52
relation.isAuthorOfPublicationff1df02d-0c0f-4db1-bf7d-78863a99420b
relation.isAuthorOfPublication.latestForDiscovery35e6a4ab-f644-4bc5-b6fc-9fd89c23d6c6

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