Modeling of energy efficiency for residential buildings using artificial neuronal networks
Ver/Abrir
Use este enlace para citar
http://hdl.handle.net/2183/21593Colecciones
- GI-RNASA - Artigos [193]
Metadatos
Mostrar el registro completo del ítemTítulo
Modeling of energy efficiency for residential buildings using artificial neuronal networksAutor(es)
Fecha
2018-11-28Cita bibliográfica
Álvarez JA, Rabuñal JR, García-Vidaurrázaga, et al. Modeling of energy efficiency for residential buildings using artificial neuronal networks. Adv Civil Engineer. 2018; 2018: 7612623
Resumen
[Abstract] Increasing the energy efficiency of buildings is a strategic objective in the European Union, and it is the main reason why numerous studies have been carried out to evaluate and reduce energy consumption in the residential sector. The process of evaluation and qualification of the energy efficiency in existing buildings should contain an analysis of the thermal behavior of the building envelope. To determine this thermal behavior and its representative parameters, we usually have to use destructive auscultation techniques in order to determine the composition of the different layers of the envelope. In this work, we present a nondestructive, fast, and cheap technique based on artificial neural network (ANN) models that predict the energy performance of a house, given some of its characteristics. The models were created using a dataset of buildings of different typologies and uses, located in the northern area of Spain. In this dataset, the models are able to predict the U-opaque value of a building with a correlation coefficient of 0.967 with the real U-opaque measured value for the same building.
Descripción
The energy efficiency dataset used to support the findings of this study has been deposited in the GitHub repository https://github.com/mereshow/ann-energy-efficiency.git.
Versión del editor
Derechos
Atribución 3.0 España
ISSN
1687-8086