Familiar and unfamiliar data sets in sustainable urban planning

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Abstract

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.

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APA

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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