Classification of building complex for the large-scale construction of distributed photovoltaics in urban buildings

Abstract

The large-scale development of urban building photovoltaics (PV) has become an important avenue for cities to achieve building energy conservation, emission reduction, and carbon neutrality. Assessing the solar energy potential of urban buildings plays a crucial role in the installation and overall planning of urban building PV systems. However, due to the vast number and diverse types of urban buildings, individual building calculations can be both labor-intensive and inefficient. Therefore, batch processing of buildings after classification is a feasible approach. Previous assessments of solar energy potential in building clusters mainly focused on typical clusters or standard geometric areas, but all urban building types were covered. Based on this, this paper proposes a method for defining Building Complexes with a Single Land Use Nature (BCSLUN) by combining geographical distribution and urban land use characteristics. Furthermore, population density, building density, compactness, and land use characteristics are selected as features, and a method that combines K-means clustering and random forest classification is proposed to complete the classification of building complexes. Taking the central urban area of Xi'an, China as an example, 5,804 building complexes are classified into four building types. The cross-validation classification accuracy is 0.99, and the Kappa coefficient is 0.94. Based on Rhino modeling, the global solar radiation on the surface of each building complex was calculated. The annual surface global radiation of the four building types is 11,442.73 MWh, 2,734.71 MWh, 13,923.81 MWh, and 67,215 MWh. The violin-box plot shows a significant classification effect, which proves the rationality of the proposed classification method. Finally, a potential map based on solar radiation from urban building surfaces is generated. Building complex classification can quickly and accurately estimate the solar energy potential of urban buildings, providing decision-making support for the large-scale construction of urban building PV. Moreover, this method can be easily applied to other cities.