method, the cooling and heat demand model of regional buildings and the energy consumption prediction model of regional buildings are built. Finally, the energy consumption simulation and energy consumption prediction of the regional building complex after energy-saving renovation are carried out. The experiment shows that the building energy consumption in July and August was relatively high, reaching 2.36E+14 and 2.4E+14, respectively. The energy consumption in April and November was relatively low, reaching 1.2E+14 and 1.4E+14, respectively. The highest prediction error was in November, reaching 12%. The lowest prediction error was in January and February, only about 2%. The error of monthly energy consumption predicted by Monte Carlo method is less than 12%, the Root-mean-square deviation is 5%, and the error between predicted and actual annual total energy consumption is only about 2%. By comparing the predicted energy consumption after energy-saving renovation with before, the energy-saving rate reached about 20%. The research results indicate that the proposed Monte Carlo based predictive stochastic model exhibits good predictive performance in building energy-saving renovation, providing theoretical guidance and reference for feasibility studies, planning, prediction, decision-making, and optimization of building energy-saving renovation.