The parametric classroom unit model files is available at: https://pan.baidu.com/s/1TZlcepW9sVQDV8dT0rWkVg (Extraction code: bdi2).
The natural lighting prediction tool for primary and secondary school classrooms in the Lingnan region is a tool with the capability to rapidly and accurately predict four types of daylighting design patterns using five daylighting indicators. Users can freely adjust the input parameters by dragging sliders and clicking buttons on the interactive interface. Subsequently, the tool can quickly generate a three-dimensional visualization of the model, bar charts for five indicators, and the daylighting distribution. This provides users with a convenient tool to understand the daylighting effects of different lighting design patterns at the early stages of the design process.
Install Rhino 7.0. Log in to https://www.food4rhino.com/en/app/ladybug-tools, register an account, and download LadyBug 0.0.68 and Honeybee 0.0.65. Install the corresponding tool plugins Radiance_012CB7C8A554, Openstudio3.0.1, and THERM7_6_01 using the installation package (or visit the LadyBug official website). Download and install HumanUI 0.8.1.3 from https://www.food4rhino.com/en/app/ladybug-tools. Install python3.9.13 and torch==cuda+1.12.1 environment using the command: pip install -r requirements.txt.
Download the parametric classroom unit model files from the following address: https://pan.baidu.com/s/1TZlcepW9sVQDV8dT0rWkVg (Extraction code: bdi2).
The whole project is supervised by: Professor Yubo Liu ([email protected]) and Associate Professor Qiaoming Deng ([email protected]), Doctor Binyan Liu([email protected]) provided technical support for algorithms and data collection. For any questions about this project please contact the authors by sending email.
Note that the DATA and CODE are free for Research and Education Use ONLY. Please cite our project if you use any part of our ALGORITHM, CODE, DATA or RESULTS in any publication.