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dc.contributor.authorKhosravani, Hamid R.
dc.contributor.authorCastilla Nieto, María del Mar
dc.contributor.authorBerenguel Soria, Manuel
dc.contributor.authorRuano, Antonio E.
dc.contributor.authorFerreira, Pedro M.
dc.date.accessioned2020-01-16T11:00:57Z
dc.date.available2020-01-16T11:00:57Z
dc.date.issued2016-01-20
dc.identifier.issn1996-1073
dc.identifier.urihttp://hdl.handle.net/10835/7369
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 naïve autoregressive baseline model. The models are intended to predict electric power demand at the Solar Energy Research Center (Centro de Investigación en Energía 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.es_ES
dc.language.isoenes_ES
dc.publisherMDPIes_ES
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internacional*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectpredictive modeles_ES
dc.subjectelectric power demandes_ES
dc.subjectneural networkses_ES
dc.subjectmulti objective genetic algorithm (MOGA)es_ES
dc.subjectdata selectiones_ES
dc.titleA Comparison of Energy Consumption Prediction Models Based on Neural Networks of a Bioclimatic Buildinges_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.relation.publisherversionhttps://www.mdpi.com/1996-1073/9/1/57es_ES
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses_ES


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Attribution-NonCommercial-NoDerivatives 4.0 Internacional
Except where otherwise noted, this item's license is described as Attribution-NonCommercial-NoDerivatives 4.0 Internacional