Short-term load forecasting for non-residential buildings contrasting artificial occupancy attributes
dc.contributor.author
dc.date.accessioned
2015-09-17T10:51:26Z
dc.date.available
2021-04-07T08:03:00Z
dc.date.issued
2015-10-15
dc.identifier.issn
0378-7788
dc.identifier.uri
dc.description.abstract
An accurate short-term load forecasting system allows an optimum daily operation of the power system and a suitable process of decision-making, such as with regard to control measures, resource planning or initial investment, to be achieved. In a previous work, the authors demonstrated that an SVR model to forecast the electric load in a non-residential building using only the temperature and occupancy of the building as attributes is the one that gives the best balance of accuracy and computational cost for the cases under study. Starting from this conclusion, a simple, low-computational requirements and economical hourly consumption prediction method, based on SVR model and only the calculated occupancy indicator as attribute, is proposed. The method, unlike the others, is able to perform hourly predictions months in advance using only the occupancy indicator. Due to the relevance of the occupancy indicator in the model, this paper provides a complete study of the methods and data sources employed in the creation of the artificial occupancy attributes. Several occupancy indicators are defined, from the simplest one, using general information, to the most complex one, based on very detailed information. Then, a load forecasting performance discrimination between the artificial occupancy attributes is realized demonstrating that using the most complex indicator increases the workload and complexity while not improving the load prediction significantly. A real case study, applying the forecasting method to seve
dc.description.sponsorship
This research project has been partially funded through BR-UdG Scholarship ofthe University of Girona granted to Joaquim Massana Raurich. Work developed with the support of the research group SITES awarded with distinction by the Generalitat de Catalunya (SGR 2014-2016) and the MESC project funded by the Spanish MINECO (Ref. DPI2013-47450-C2-1-R)
dc.format.mimetype
application/pdf
dc.language.iso
eng
dc.publisher
Elsevier
dc.relation
info:eu-repo/grantAgreement/MINECO//DPI2013-47450-C2-1-R/ES/PLATAFORMA PARA LA MONITORIZACION Y EVALUACION DE LA EFICIENCIA DE LOS SISTEMAS DE DISTRIBUCION EN SMART CITIES/
AGAUR/2014-2016/2014 SGR-1052
dc.relation.isformatof
Versió postprint del document publicat a: http://dx.doi.org/10.1016/j.enbuild.2016.08.081
dc.relation.ispartof
© Energy and Buildings, 2015, vol. 130, p. 519-531
dc.relation.ispartofseries
Articles publicats (D-EEEiA)
dc.rights
Reconeixement-NoComercial-SenseObraDerivada 4.0 Internacional
dc.rights.uri
dc.subject
dc.title
Short-term load forecasting for non-residential buildings contrasting artificial occupancy attributes
dc.type
info:eu-repo/semantics/article
dc.rights.accessRights
info:eu-repo/semantics/openAccess
dc.embargo.terms
2018-10-15
dc.date.embargoEndDate
info:eu-repo/date/embargoEnd/2018-10-15
dc.relation.projectID
info:eu-repo/grantAgreement/EC/H2020/680708/EU/Highly Innovative building control Tools Tackling the energy performance GAP/HIT2GAP
dc.type.version
info:eu-repo/semantics/acceptedVersion
dc.identifier.doi
dc.identifier.idgrec
025832
dc.contributor.funder
dc.type.peerreviewed
peer-reviewed
dc.relation.dataset
dc.relation.FundingProgramme
dc.relation.ProjectAcronym