Modeling of Energy Efficiency for Residential Buildings Using Artificial Neuronal Networks
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...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2018-01-01
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| Series: | Advances in Civil Engineering |
| Online Access: | http://dx.doi.org/10.1155/2018/7612623 |
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| author | José Antonio Álvarez Juan Ramón Rabuñal Dolores García-Vidaurrázaga Alberto Alvarellos Alejandro Pazos |
| author_facet | José Antonio Álvarez Juan Ramón Rabuñal Dolores García-Vidaurrázaga Alberto Alvarellos Alejandro Pazos |
| author_sort | José Antonio Álvarez |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-b49798cf360448e89feb6fd9caca6aca |
| institution | Kabale University |
| issn | 1687-8086 1687-8094 |
| language | English |
| publishDate | 2018-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advances in Civil Engineering |
| spelling | doaj-art-b49798cf360448e89feb6fd9caca6aca2025-08-20T03:39:22ZengWileyAdvances in Civil Engineering1687-80861687-80942018-01-01201810.1155/2018/76126237612623Modeling of Energy Efficiency for Residential Buildings Using Artificial Neuronal NetworksJosé Antonio Álvarez0Juan Ramón Rabuñal1Dolores García-Vidaurrázaga2Alberto Alvarellos3Alejandro Pazos4University of A Coruña, School of Technical Architecture, Zapateira Campus 15071, A Coruña, SpainUniversity of A Coruña, Centre of Technological Innovation in Construction and Civil Engineering, Elviña Campus 15071, A Coruña, SpainUniversity of A Coruña, School of Technical Architecture, Zapateira Campus 15071, A Coruña, SpainUniversity of A Coruña, CITIC-Research Center on Information and Communication, Elviña Campus 15071, A Coruña, SpainUniversity of A Coruña, Computer Science Department, Elviña Campus 15071, A Coruña, SpainIncreasing 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.http://dx.doi.org/10.1155/2018/7612623 |
| spellingShingle | José Antonio Álvarez Juan Ramón Rabuñal Dolores García-Vidaurrázaga Alberto Alvarellos Alejandro Pazos Modeling of Energy Efficiency for Residential Buildings Using Artificial Neuronal Networks Advances in Civil Engineering |
| title | Modeling of Energy Efficiency for Residential Buildings Using Artificial Neuronal Networks |
| title_full | Modeling of Energy Efficiency for Residential Buildings Using Artificial Neuronal Networks |
| title_fullStr | Modeling of Energy Efficiency for Residential Buildings Using Artificial Neuronal Networks |
| title_full_unstemmed | Modeling of Energy Efficiency for Residential Buildings Using Artificial Neuronal Networks |
| title_short | Modeling of Energy Efficiency for Residential Buildings Using Artificial Neuronal Networks |
| title_sort | modeling of energy efficiency for residential buildings using artificial neuronal networks |
| url | http://dx.doi.org/10.1155/2018/7612623 |
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