Towards a calibration of building energy models: A case study from the Spanish housing stock in the Mediterranean climate
Current energy policies focus on retrofitting to achieve Horizon 2020 aims, especially concerning the residential stock constructed before the first thermal regulations. According to this, improving energy efficiency and interior comfort conditions in buildings must be supported by the knowledge of...
| Autores: | , , |
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| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2015 |
| País: | España |
| Institución: | Universidad de Sevilla (US) |
| Repositorio: | idUS. Depósito de Investigación de la Universidad de Sevilla |
| OAI Identifier: | oai:idus.us.es:11441/52197 |
| Acceso en línea: | http://hdl.handle.net/11441/52197 https://doi.org/10.3989/ic.15.081. |
| Access Level: | acceso abierto |
| Palabra clave: | Calibration Energy models Monitoring Residential stock Retrofitting Simulation Calibración modelos energéticos monitorización parque residencial rehabilitación simulación |
| Sumario: | Current energy policies focus on retrofitting to achieve Horizon 2020 aims, especially concerning the residential stock constructed before the first thermal regulations. According to this, improving energy efficiency and interior comfort conditions in buildings must be supported by the knowledge of its real energy performance. Due to uncertainty and the lack of information on the current energy performance of housing and its real operational conditions, discrepancies between the results obtained and the measured data arise. Housing monitoring under real occupational conditions become essential for a better understanding of environmental behavior of residential building stock. Our aim is to show the calibration process, based on monitoring data obtained from a group of dwellings of national heritage interest built in the 1950s in Seville (a Mediterranean climate city). Calibration allows simulation results to approximate to current environmental conditions, aiming to predict and optimize the potential for subsequent environmental and energy implementation. |
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