Bayesian and network models with covariate effects for predicting heating energy demand
Ver/ Abrir
Impacto
Scholar |
Otros documentos de la autoría: Juan, Pablo; Braulio-Gonzalo, Marta; Díaz Ávalos, Carlos; Bovea, María D; Serra, Laura
Metadatos
Mostrar el registro completo del ítemcomunitat-uji-handle:10234/9
comunitat-uji-handle2:10234/7037
comunitat-uji-handle3:10234/8635
comunitat-uji-handle4:
INVESTIGACIONMetadatos
Título
Bayesian and network models with covariate effects for predicting heating energy demandFecha de publicación
2022Editor
ElsevierISSN
1877-5845; 1877-5853Cita bibliográfica
JUAN, Pablo, et al. Bayesian and network models with covariate effects for predicting heating energy demand. Spatial and Spatio-temporal Epidemiology, 2022, vol. 43, p. 100547Tipo de documento
info:eu-repo/semantics/articleVersión de la editorial
https://www.sciencedirect.com/science/article/pii/S1877584522000703Versión
info:eu-repo/semantics/publishedVersionPalabras clave / Materias
Resumen
The spatial effect is an element presented in many geostatistical works and it should be incorporated into studies regarding the heating energy demand of residential building stocks. The most common approaches have ... [+]
The spatial effect is an element presented in many geostatistical works and it should be incorporated into studies regarding the heating energy demand of residential building stocks. The most common approaches have been made by simple descriptive statistics or using analyses by Markov random fields. In this work, we propose two different methods. First, the Stochastic Partial Differential Equation with the Integrated Nested Laplace Approximation to model the variable heating energy demand in Castellón de la Plana, Spain also considering covariates and the spatial effect. Second, simulated street networks for analysing data. We describe and take advantage of the Bayesian methodology in the modelling process in all the scenarios, including covariates and the possibility of creating a simulated street network with the data for the modelling issue. Our results show that the spatial location of the building is a crucial element to study the heating energy demand using both methodologies. [-]
Publicado en
Spatial and Spatio-temporal Epidemiology, 2022, vol. 43, p. 100547Entidad financiadora
Ministerio de Ciencia y Educación de España
Código del proyecto o subvención
MTM2016–78917-R | PAPIIT-UNAM IG100221
Derechos de acceso
© 2023 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license
info:eu-repo/semantics/openAccess
info:eu-repo/semantics/openAccess
Aparece en las colecciones
- MAT_Articles [755]