| id |
ecaaderis2023_11 |
| authors |
Sepúlveda, Abel, Eslamirad, Nasim, Seyed Salehi, Seyed Shahabaldin, Thalfeldt, Martin and De Luca, Francesco |
| year |
2023 |
| title |
Machine Learning-based Optimization Design Workflow based on Obstruction Angles for Building Facades |
| source |
De Luca, F, Lykouras, I and Wurzer, G (eds.), Proceedings of the 9th eCAADe Regional International Symposium, TalTech, 15 - 16 June 2023, pp. 15–24 |
| summary |
This paper proposes a ML-based optimization design workflow based on obstruction
angles for the optimization of building facades (i.e. g-value and window width). The
optimization output consists of the optimal clustering of windows in order to ensure a
desired level of daylight provision according to method 2 defined in the EN17307:2018
(i.e. based on Spatial Daylight Autonomy: sDA) and to not exceed a maximum level of
specific cooling capacity (SCC). The independent variables or design parameters of the
parametric model are: room orientation/dimensions, window dimensions, and obstruction
angle (??). The ML prediction models were trained and tested with reliable simulation
results using validate softwares. The total number of room combinations is 61440 for
sDA and SCC simulations. The development of reliable (90% of right predictions) ML
predictive models based on decision tree technique were calibrated. The optimal
clustering of windows was done first by floors and secondly by the designer’s need to
homogenize the external facade with similar glazing properties and window sizes, having
impact on the annual heating consumption. The proposed method help designers to make
accurate and faster design decisions during early design stages and renovation plans. |
| keywords |
optimization, daylight, thermal comfort, cooling capacity, machine-learning predictive model, office buildings, cold climates |
| series |
eCAADe |
| email |
|
| full text |
 file.pdf (1,108,219 bytes) |
| references |
Content-type: text/plain
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| last changed |
2024/02/05 14:28 |
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