| id |
caadria2017_051 |
| authors |
Liu, Yuezhong and Stouffs, Rudi |
| year |
2017 |
| title |
Familiar and Unfamiliar Data Sets in Sustainable Urban Planning |
| source |
P. Janssen, P. Loh, A. Raonic, M. A. Schnabel (eds.), Protocols, Flows, and Glitches - Proceedings of the 22nd CAADRIA Conference, Xi'an Jiaotong-Liverpool University, Suzhou, China, 5-8 April 2017, pp. 705-714 |
| doi |
https://doi.org/10.52842/conf.caadria.2017.705
|
| summary |
Achieving energy efficient urban planning requires a multi-disciplinary planning approach. The huge increase in data from sensors and simulations does not help to reduce the burden of planners. On the contrary, unfamiliar multi-disciplinary data sets can bring planners into a hopeless tangle. This paper applies semi-supervised learning methods to address such planning data issues. A case study is used to demonstrate the proposed method with respect to three performance issues: solar heat gains, natural ventilation and daylight. The result shows that the method addressing both familiar and unfamiliar data has the ability to guide the planner during the planning process. |
| keywords |
energy performance; S3VM; decision tree; familiar and unfamiliar. |
| series |
CAADRIA |
| email |
|
| full text |
 file.pdf (4,336,628 bytes) |
| references |
Content-type: text/plain
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| last changed |
2022/06/07 07:59 |
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