Load Forecasting of Large-space Stadium Based on Surrogate Modeling

A surrogate-based load forecasting model for a large-space stadium is proposed. The model is able to perform load forecasting with incomplete historical operation data of building energy systems or completely missing. In this paper, a 3D model of a large-space stadium is constructed based on existing engineering drawings and on-site research. Load calculations are carried out based on this model. In addition, a neural network model for load forecasting of the large-space stadium is constructed. In this neural network, weather data and equipment set points are used as model input features, and historical energy system data and mechanism modeling data are used as model output labels. The results of the case study show that the load calculation based on 3D modeling meets the expected objectives and the mean square error of the final cooling and heat loads of the neural network results in engineering errors. The surrogate model in this paper can provide an idea for the case where historical operational data of large-space stadiums are insufficient while low-carbon upgrades of energy systems are needed.