Achieving energy efficient urban planning requires a multidisciplinary 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.
CITATION STYLE
Liu, Y., & Stouffs, R. (2017). Familiar and unfamiliar data sets in sustainable urban planning. In CAADRIA 2017 - 22nd International Conference on Computer-Aided Architectural Design Research in Asia: Protocols, Flows and Glitches (pp. 705–714). The Association for Computer-Aided Architectural Design Research in Asia (CAADRIA). https://doi.org/10.52842/conf.caadria.2017.705
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