The Annual Radiation Intensity Neural Network (ARINet) demonstrates the feasibility of using a 3D convolutional neural network to predict the surface radiation received by building façades. The structure of ARINet is composed of 3D convolution, fully connected, and 3D deconvolution layers. In this research, it was trained on 1,692 datasets and validated by 424 datasets generated by a physical simulator. ARINet showed errors in 0.2% of the validation sets.
ARINet: A ANN model based Plugin for Radiation Analysis on Buildings' facades developed by Ellie Jungmin Han
Copyright (c) 2020, Ellie Jungmin Han (jhan2harvard)
Original ARINet paper Other contributors for the original project are Chih-Kang Chang, Tim Lee, Dabin Choi from Harvard SEAS