Assessing building-integrated photovoltaic potential in dense urban areas using a multi-channel single-dimensional convolutional neural network model

Assessing BIPV (Building Integrated Photovoltaic) potential is of great significance for the comprehensive promotion and deployment of solar energy. Traditional models mostly rely on morphological parameters for PV potential assessment, presenting challenges such as subjective knowledge of urban forms and difficulty in generalization within dense urban areas. This study employs Convolutional Neural Network (CNN) for 3D modeling to evaluate BIPV potential at medium and large urban scales, introducing a framework for a multidimensional single-channel one-dimensional CNN model. The model utilizes the Gaussian Mixture Model combined with building point cloud data to extract the building window-to-wall ratio, thereby enhancing individual features in the building cluster point cloud. It also utilizes the 3D physical model to extract building geographic orientation information, integrating point cloud distribution through spatial connectivity to address the issue of missing geographic orientation due to rotational invariance of point cloud convolution. Additionally, it uses the surface area of the 3D model as the weight for surface point cloud sampling and combines it with normal estimation to retain building entity information, solving the disorder of point cloud convolution. This modeling framework enables accurate prediction of PV potentials in urban blocks by utilizing city point cloud data and predicting urban block boundaries. Using Melbourne City as a case study, the model demonstrates superior performance compared to traditional morphological parameter-based prediction models, with a root mean square error of 2415.548 kWh/year and an R2 SCORE of 0.937 in 75 training sets. The proposed modeling framework enables the prediction of multi-scale BIPV potential, which is beneficial for the staged promotion of BIPV and the development of effective energy deployment strategies. This study offers new insights for urban building energy modeling, deep learning, and energy prediction in complex scenarios at medium and large scales for sustainable urban development.