Effects of Window-to-Wall Ratio on Energy Consumption: Application of Numerical and ANN Approaches
Buildings account for a major part of Total Energy Consumption (TEC) in comparison to that of industry and other sections. The opening and envelope material can affect their TEC. Accordingly, this paper aims to study the effects of the window to external wall ratio (WWR) and the application of recyc...
Guardado en:
| Autor principal: | |
|---|---|
| Formato: | article |
| Lenguaje: | EN |
| Publicado: |
Pouyan Press
2021
|
| Materias: | |
| Acceso en línea: | https://doaj.org/article/dd0f068e5cb749aaba39cdf5de14b6c5 |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| Sumario: | Buildings account for a major part of Total Energy Consumption (TEC) in comparison to that of industry and other sections. The opening and envelope material can affect their TEC. Accordingly, this paper aims to study the effects of the window to external wall ratio (WWR) and the application of recycled panels as the building envelope on the total energy consumption in a one-floor residential building located in Iran and characterized by a semi-arid climate. To follow the sustainability criterion, we designed two concrete panels for the external walls’ envelope including a porous concrete panel and recycled ash concrete panel. The WWR varies between 5% to 95% and the optimal WWRs are separately presented for all the months. To develop the models, we used Design Builder software which its simulations are validated via field observations. For all the panels, the least energy consumption is obtained when the WWR is 5%. However, due to lighting issues, the most optimal WWR is calculated as 45-55% based on the results of the numerical simulations. Further, it is proved that the recycled ash concrete panel outperforms the porous concrete panel in terms of minimum energy consumption. Hence, it is recommended to use eco-friendly material as the external walls envelop with the WWR below 50%. The numerical simulations provided 240 data points for each panel which is exploited to develop an ANN model. The results suggested that the ANN models predict the TEC based on the month and WWR with high accuracy. |
|---|