A Novel Method of BP Neural Network Based Green Building Design—The Case of Hotel Buildings in Hot Summer and Cold Winter Region of China

With the advent of the big data era, architectural design gradually tends to become more quantified and intelligent. This study proposes a novel green design method for energy-saving buildings based on a BP neural network. This study changed the traditional trial–error mode by evaluating energy consumption based on design performance parameters such as building shape, space, and interface. Instead, energy consumption quota values obtained from statistical data, as well as thermal parameters and energy system parameters in energy-saving standards, were taken as input parameters, and then the design scheme of building shape can be obtained through BP neural network technology. Based on data of 61 hotel buildings in a representative city among a hot summer and cold winter climate zone, the BP neural network model is established to control the building design variables, with 41 kgce/m2·a as its energy-saving design target. Through the energy consumption quota, the trained BP network is applied to predict the optimal architectural design parameters, including the building orientation angle, shape coefficient, window–wall ratio, etc., for twelve building typologies in an area range of 5000~60,000 m2. With recommended control thresholds of quantifiable architectural design elements obtained, this research can provide effective design decision-making suggestions for architects