Heating energy performance gap in vulnerable households: identification and impact of associated variables
Reducing energy consumption in the construction sector is urgently needed. In Chile, where income distribution is unequal and the cost of energy is high, energy demand is seriously affected, especially in vulnerable households. Hence, it is essential to establish public policies with more realistic...
| Autores: | , , , |
|---|---|
| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2024 |
| País: | España |
| Institución: | Universidad de Sevilla (US) |
| Repositorio: | idUS. Depósito de Investigación de la Universidad de Sevilla |
| OAI Identifier: | oai:idus.us.es:11441/164351 |
| Acceso en línea: | https://hdl.handle.net/11441/164351 https://doi.org/10.3390/en17194995 |
| Access Level: | acceso abierto |
| Palabra clave: | Energy performance gap Energy poverty Occupant behavior Social housing Energy modeling of buildings Energy policy of buildings |
| Sumario: | Reducing energy consumption in the construction sector is urgently needed. In Chile, where income distribution is unequal and the cost of energy is high, energy demand is seriously affected, especially in vulnerable households. Hence, it is essential to establish public policies with more realistic energy-saving goals to address this situation. However, reliably predicting the energy performance of buildings remains a challenge. For this reason, this study aims to identify and evaluate the impact of the variables associated with energy performance in vulnerable households in Central-Southern Chile and propose values that would reduce the gap. A sensitivity analysis was conducted to achieve this, adjusting the energy performance parameters in a base model with data analyzed using local standards. In addition, field information was collected in 93 households to obtain the actual energy consumption. The main results show that the variables that most impacted performance were infiltration, COP, heating setpoints, and schedules, which generated a 60% difference between the theoretical and actual consumption. |
|---|