Integrated energy systems enable the complementary utilization of various forms of energy. With the rapid increase in installed capacity of distributed generations,their intermittency and randomness have posed significant challenges to the operational efficiency and safety of the system. To address the impact of uncertainties in both energy supply and demand on the economic scheduling of building integrated energy systems,this study first models the operational characteristics of each subsystem,including the power grid,natural gas network,and various coupled equipments. Next,a thermal cell model of the building users is constructed based on the quantitative relationship among temperature,thermal radiation,and thermal load. Then,a day-ahead optimization scheduling model for the building integrated energy systems is established with the objective of minimizing the operating cost. A chance-constrained programming approach is employed to convert the non-linear scheduling model into a mixed-integer second-order cone programming problem easy to solve. Finally,simulation analysis is conducted in the Python environment using the CPLEX solver. The results demonstrate that the proposed model and solution method are capable of effectively characterizing and addressing uncertainty risks in the system,facilitating the consumption of renewable energy,and improving the economic efficiency of system operation.