| id |
caadria2021_161 |
| authors |
Zhao, Xin, Han, Yunsong and Shen, Linhai |
| year |
2021 |
| title |
Multi-objective Optimisation of a Free-form Building Shape to improve the Solar Energy Utilisation Potential using Artificial Neural Networks |
| source |
A. Globa, J. van Ameijde, A. Fingrut, N. Kim, T.T.S. Lo (eds.), PROJECTIONS - Proceedings of the 26th CAADRIA Conference - Volume 1, The Chinese University of Hong Kong and Online, Hong Kong, 29 March - 1 April 2021, pp. 221-230 |
| doi |
https://doi.org/10.52842/conf.caadria.2021.1.221
|
| summary |
Optimisation of free-form building design is more challenging in terms of building information modelling and performance evaluation compared to conventional buildings. The paper provides a Photogrammetry-based BIM Modelling - Machine Learning Modelling - Multi-objective Optimisation framework to improve the solar energy utilisation potential of free-form buildings. Low altitude photogrammetry is used to collect the building and site environmental information. An ANN prediction model is developed using the control point coordinates and simulation data. Through parametric programming, the multi-objective algorithm is coupled with the ANN model to obtain the trade-off optimal building form. The results show that the maximum solar radiation value in winter can increase by 30.60% and the minimum solar radiation in summer can decrease by 13.99%. It is also shown that the integration of ANN modelling and photogrammetry-based BIM modelling into the multi-objective optimisation method can accelerate the optimisation process. |
| keywords |
Multi-objective optimisation; Artificial neural network; Free-form shape building ; Solar energy utilisation |
| series |
CAADRIA |
| email |
|
| full text |
 file.pdf (10,328,925 bytes) |
| references |
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