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A comparison of energy consumption prediction models based on neural networks of a bioclimatic building

2016-07Journal article Open access
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dc.contributor.authorKhosravani, Hamid Reza
dc.contributor.authorDel Mar Castilla, Maria
dc.contributor.authorBerenguel, Manuel
dc.contributor.authorRuano, Antonio
dc.contributor.authorFerreira, Pedro M.
dc.date.accessioned2017-04-07T15:57:38Z
dc.date.available2017-04-07T15:57:38Z
dc.date.issued2016-07
dc.description.abstractEnergy consumption has been increasing steadily due to globalization and industrialization. Studies have shown that buildings are responsible for the biggest proportion of energy consumption; for example in European Union countries, energy consumption in buildings represents around 40% of the total energy consumption. In order to control energy consumption in buildings, different policies have been proposed, from utilizing bioclimatic architectures to the use of predictive models within control approaches. There are mainly three groups of predictive models including engineering, statistical and artificial intelligence models. Nowadays, artificial intelligence models such as neural networks and support vector machines have also been proposed because of their high potential capabilities of performing accurate nonlinear mappings between inputs and outputs in real environments which are not free of noise. The main objective of this paper is to compare a neural network model which was designed utilizing statistical and analytical methods, with a group of neural network models designed benefiting from a multi objective genetic algorithm. Moreover, the neural network models were compared to a naive autoregressive baseline model. The models are intended to predict electric power demand at the Solar Energy Research Center (Centro de Investigacion en Energia SOLar or CIESOL in Spanish) bioclimatic building located at the University of Almeria, Spain. Experimental results show that the models obtained from the multi objective genetic algorithm (MOGA) perform comparably to the model obtained through a statistical and analytical approach, but they use only 0.8% of data samples and have lower model complexity.
dc.identifier.doi10.3390/en9010057
dc.identifier.issn1996-1073
dc.identifier.otherAUT: ARU00698;
dc.identifier.urihttp://hdl.handle.net/10400.1/9774
dc.language.isoeng
dc.peerreviewedyes
dc.relationSolar Facilities for the European Research Area-Second Phase
dc.relation.isbasedonWOS:000369501100025
dc.titleA comparison of energy consumption prediction models based on neural networks of a bioclimatic building
dc.typejournal article
dspace.entity.typePublication
oaire.awardTitleSolar Facilities for the European Research Area-Second Phase
oaire.awardURIinfo:eu-repo/grantAgreement/EC/FP7/312643/EU
oaire.citation.issue1
oaire.citation.titleEnergies
oaire.citation.volume9
oaire.fundingStreamFP7
person.familyNameKhosravani
person.familyNameRuano
person.givenNameHamid Reza
person.givenNameAntonio
person.identifier.orcid0000-0001-7273-5979
person.identifier.orcid0000-0002-6308-8666
person.identifier.ridB-4135-2008
person.identifier.scopus-author-id7004284159
project.funder.identifierhttp://doi.org/10.13039/501100008530
project.funder.nameEuropean Commission
rcaap.rightsopenAccess
rcaap.typearticle
relation.isAuthorOfPublicationdd2ad4e5-427f-468c-a272-688fae19ce52
relation.isAuthorOfPublication13813664-b68b-40aa-97a9-91481a31ebf2
relation.isAuthorOfPublication.latestForDiscovery13813664-b68b-40aa-97a9-91481a31ebf2
relation.isProjectOfPublication89181726-4b84-41fe-9c2b-3d5f4b0592a7
relation.isProjectOfPublication.latestForDiscovery89181726-4b84-41fe-9c2b-3d5f4b0592a7

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