Journal of Civil Engineering and Architecture 13 (2019) 686-693 doi: 10.17265/1934-7359/2019.11.003 TransDATA2: A Data Visualization Toolkit to Assist the High-Performance Building Design Hui Ren 1, 2 , Wang Shou Long 3 and Gloria Pignatta 4 1. School of Architecture, Harbin Institute of Technology, Harbin 150001, China 2. Hei Long Jiang Cold Region Architectural Science Key Laboratory, Harbin 150001, China 3. Department of Chemical Engineering and Safety, Binzhou University, Binzhou256600, China 4. Faculty of Built Environment, University of New South Wales, 2052 Sydney, Australia Abstract: With the development of the economic and low-carbon society, high-performance building (EPB) design plays a more and more important role in the architectural area. The performance of buildings usually includes the building energy consumption, building interior natural daylighting, building surface solar radiation and so on. To obtain a high-performance building in the design process, building performance simulation (BPS) and multiple objective optimizations (MOO) are becoming the main methods. Correspondingly, the BPS and MOO are based on the parametric tools like Grasshopper and Dynamo. However, these tools are lacking the data analysis module for designers to select the EPB more conveniently. This paper proposes a toolkit “transDATA2” developed based on the Grasshopper platform and Python language. At the end of this paper, four experiments were operated to verify the function of transDATA2 which showed that it could aid architects to design the high-performance buildings more efficiently and conveniently. Key words: TransDATA2, BPS, MOO, EPB, Python language. 1. Introduction With the development of the computational architectural design tools, more and more building designers could utilize the building performance simulation (BPS) and building performance multiple objective optimization (MOO) methods to design the high-performance building (EPB) like low-energy consumption, ideal building interior natural daylighting and suitable building surface solar radiation. At the present stage, Grasshopper has been deeply favored by many designers, which plays an important role in parametric building design phase. It allows designers to operate the BPS and MOO using the Corresponding author: Ren Hui, Master, research fields: building performance simulation, multiple objective optimization; Wang Shou Long, Ph.D., professor, research fields: oil and gas flow theory and application, building performance simulation. plugin in Grasshopper-like Geco, Ladybug & Honeybee. However, the simulated data analysis tools in Grasshopper to aid BPS or MOO are rare and the existing tools like Parrot, Mr. Comfy and so on are not specially designed for BPS or MOO data visualization. As a result, this paper proposes a data analysis & visualization toolkit—transDATA2 based on Python language to assist the BPS and MOO analysis. It includes a set of Python language battery blocks to provide data analysis and decision support for BPS and MOO results to select the suitable design results. Different Python language battery blocks could assist a different kind of BPS or MOO simulated results which would be more convenient and accurate for architects to select the high-performance buildings design scheme (Fig. 1). In term of the BPS, MOO, and data analysis & visualization tools in previous studies, they contributed a lot to the sustainable building design process. BPS is an important method to predict building performance D DAVID PUBLISHING