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dc.contributor.authorMartínez Comesaña, Miguel 
dc.contributor.authorFebrero Garrido, Lara 
dc.contributor.authorTroncoso Pastoriza, Francisco
dc.contributor.authorMartínez Torres, Javier
dc.date.accessioned2021-03-01T11:31:32Z
dc.date.available2021-03-01T11:31:32Z
dc.date.issued2020-10-23
dc.identifier.citationApplied Sciences, 10(21): 7439 (2020)spa
dc.identifier.issn20763417
dc.identifier.urihttp://hdl.handle.net/11093/1808
dc.description.abstractAccurate prediction of building indoor temperatures and thermal demand is of great help to control and optimize the energy performance of a building. However, building thermal inertia and lag lead to complex nonlinear systems is difficult to model. In this context, the application of artificial neural networks (ANNs) in buildings has grown considerably in recent years. The aim of this work is to study the thermal inertia of a building by developing an innovative methodology using multi-layered perceptron (MLP) and long short-term memory (LSTM) neural networks. This approach was applied to a public library building located in the north of Spain. A comparison between the prediction errors according to the number of time lags introduced in the models has been carried out. Moreover, the accuracy of the models was measured using the CV(RMSE) as advised by AHSRAE. The main novelty of this work lies in the analysis of the building inertia, through machine learning algorithms, observing the information provided by the input of time lags in the models. The results of the study prove that the best models are those that consider the thermal lag. Errors below 15% for thermal demand and below 2% for indoor temperatures were achieved with the proposed methodology.spa
dc.description.sponsorshipMinisterio de Ciencia, Innovación y Universidades | Ref. RTI2018-096296-B-C21spa
dc.language.isoengspa
dc.publisherApplied Sciencesspa
dc.rightsCreative Commons Attribution (CC BY) license
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.titlePrediction of building’s thermal performance using LSTM and MLP neural networksspa
dc.typearticlespa
dc.rights.accessRightsopenAccessspa
dc.identifier.doi10.3390/app10217439
dc.identifier.editorhttps://www.mdpi.com/2076-3417/10/21/7439spa
dc.publisher.departamentoEnxeñaría mecánica, máquinas e motores térmicos e fluídosspa
dc.publisher.departamentoMatemática aplicada Ispa
dc.publisher.grupoinvestigacionGTE (Grupo de Tecnoloxía Enerxética)spa
dc.publisher.grupoinvestigacionExplotación de Minasspa
dc.subject.unesco3305.90 Transmisión de Calor en la Edificaciónspa
dc.subject.unesco3305.26 Edificios Públicosspa
dc.subject.unesco2203 Electrónicaspa
dc.date.updated2021-03-01T09:50:27Z


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