dc.creator | Ruiz Arahal, Manuel | es |
dc.creator | Ortega Linares, Manuel Gil | es |
dc.creator | Garrido Satué, Manuel | es |
dc.date.accessioned | 2022-09-28T16:40:25Z | |
dc.date.available | 2022-09-28T16:40:25Z | |
dc.date.issued | 2021-06 | |
dc.identifier.citation | Ruiz Arahal, M., Ortega Linares, M.G. y Garrido Satué, M. (2021). Chiller Load Forecasting Using Hyper-Gaussian Nets. Energies, 14 (12), 3479. | |
dc.identifier.issn | EISSN 1996-1073 | es |
dc.identifier.uri | https://hdl.handle.net/11441/137453 | |
dc.description.abstract | Energy load forecasting for optimization of chiller operation is a topic that has been
receiving increasing attention in recent years. From an engineering perspective, the methodology
for designing and deploying a forecasting system for chiller operation should take into account
several issues regarding prediction horizon, available data, selection of variables, model selection
and adaptation. In this paper these issues are parsed to develop a neural forecaster. The method
combines previous ideas such as basis expansions and local models. In particular, hyper-gaussians
are proposed to provide spatial support (in input space) to models that can use auto-regressive,
exogenous and past errors as variables, constituting thus a particular case of NARMAX modelling.
Tests using real data from different world locations are given showing the expected performance of
the proposal with respect to the objectives and allowing a comparison with other approaches. | es |
dc.description.sponsorship | Unión Europea RTI2018-101897-B-I00 | es |
dc.description.sponsorship | Ministerio de Ciencia e Innovación RTI2018-101897-B-I00 | es |
dc.format | application/pdf | es |
dc.format.extent | 15 p. | es |
dc.language.iso | eng | es |
dc.publisher | MDPI | es |
dc.relation.ispartof | Energies, 14 (12), 3479. | |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 Internacional | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
dc.subject | Energy consumption prediction | es |
dc.subject | Time-series forecasting | es |
dc.subject | Neural approximation | es |
dc.subject | Hyper-gaussian | es |
dc.title | Chiller Load Forecasting Using Hyper-Gaussian Nets | es |
dc.type | info:eu-repo/semantics/article | es |
dcterms.identifier | https://ror.org/03yxnpp24 | |
dc.type.version | info:eu-repo/semantics/publishedVersion | es |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | es |
dc.contributor.affiliation | Universidad de Sevilla. Departamento de Ingeniería de Sistemas y Automática | es |
dc.relation.projectID | RTI2018-101897-B-I00 | es |
dc.relation.publisherversion | https://doi.org/10.3390/en14123479 | es |
dc.identifier.doi | 10.3390/en14123479 | es |
dc.journaltitle | Energies | es |
dc.publication.volumen | 14 | es |
dc.publication.issue | 12 | es |
dc.publication.initialPage | 3479 | es |
dc.contributor.funder | European Commission (EC). Fondo Europeo de Desarrollo Regional (FEDER) | es |
dc.contributor.funder | Agencia Estatal de Investigación. España | es |