Geometric classification method of rural residences at regional scale

Abstract

With the rapid growth of construction area, residential energy consumption has sharply increased in China. Under this background, energy consumption estimation and energy performance assessment for rural residences at regional scale has become a significant issue that needs to be solved. Bottom-up approach is widely used to establish the representative residence models for estimating building energy consumption. However, there are some difficulties when applying bottom-up method to estimate energy consumption for residential building stock in rural region of China. For instance, the rural residences classification, which is a fundamental step of bottom-up approach cannot be finished by the typology and classification approaches that have been proposed. In order to solve this problem, a new classification method for rural residences of China is presented in this article. This method includes four steps. Firstly, the remote sensing image of target region is checked whether the application conditions are met. Secondly, the target region can be divided into several sub-regions and the villages are randomly selected by stratified sampling from each sub-region. Thirdly, the 3D data of residences in each selected village is obtained with Google Earth. Lastly, based on the obtained 3D data, the residential buildings are classified and the representative geometric models for each group can be established. In order to further illustrate this method with more details, the municipal region of Hangzhou in Zhejiang province of China is taken as an example. The new method is significant to establish representative model for rural residences at municipal scale, which supports not only energy consumption estimation and energy performance assessment of residential building stock, but also the evaluation of energy saving potential and the impact of retrofit measures from the perspective of regional scale.