Adaptive comfort control implemented model (ACCIM) for energy consumption predictions in dwellings under current and future climate conditions: A case study located in Spain
Currently, the knowledge of energy consumption in buildings of new and existing dwellings is essential to control and propose energy conservation measures. Most of the predictions of energy consumption in buildings are based on fixed values related to the internal thermal ambient and pre-established...
| Autores: | , , , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2019 |
| País: | España |
| Institución: | Consejo General de la Arquitectura Técnica de España (CGATE) |
| Repositorio: | RIARTE |
| OAI Identifier: | oai:www.riarte.es:20.500.12251/1553 |
| Acceso en línea: | http://hdl.handle.net/20.500.12251/1553 https://doi.org/10.3390/en12081498 |
| Access Level: | acceso abierto |
| Palabra clave: | Confort térmico Cambio climático Edificación residencial Ahorro energético Consumo energético Adaptive Comfort Control Implemented Model (ACCIM) Análisis estadístico 3305.90 Transmisión de Calor en la Edificación 3305.14 Viviendas 3311.16 Instrumentos de Medida de la Temperatura 6310.09 Calidad de Vida |
| Sumario: | Currently, the knowledge of energy consumption in buildings of new and existing dwellings is essential to control and propose energy conservation measures. Most of the predictions of energy consumption in buildings are based on fixed values related to the internal thermal ambient and pre-established operation hypotheses, which do not reflect the dynamic use of buildings and users’ requirements. Spain is a clear example of such a situation. This study suggests the use of an adaptive thermal comfort model as a predictive method of energy consumption in the internal thermal ambient, as well as several operation hypotheses, and both conditions are combined in a simulation model: the Adaptive Comfort Control Implemented Model (ACCIM). The behavior of ACCIM is studied in a representative case of the residential building stock, which is located in three climate zones with different characteristics (warm, cold, and mild climates). The analyses were conducted both in current and future scenarios with the aim of knowing the advantages and limitations in each climate zone. The results show that the average consumption of the current, 2050, and 2080 scenarios decreased between 23% and 46% in warm climates, between 19% and 25% in mild climates, and between 10% and 29% in cold climates by using such a predictive method. It is also shown that this method is more resilient to climate change than the current standard. This research can be a starting point to understand users’ climate adaptation to predict energy consumption. © 2019 by the authors |
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