CumInCAD is a Cumulative Index about publications in Computer Aided Architectural Design
supported by the sibling associations ACADIA, CAADRIA, eCAADe, SIGraDi, ASCAAD and CAAD futures

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
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 liuyzwillow@gmail.com
full text file.pdf (4,336,628 bytes)
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