This paper studies and analyzes the energy consumption of hotel buildings by establishing a support vector machine energy consumption prediction model. The support vector machine model takes the weather parameters and operating parameters of the hotel air-conditioning system as input variables, and determines the critical value of the input parameters by determining the normal-distribution interval, so as to avoid the influence of the outliers on the model prediction stability. The RBF kernel function is selected as the kernel function of the support vector machine, and the accuracy of the model prediction is improved by optimizing the kernel parameters. The MSE value of the final model prediction was 2.22 % and R² was 0.94. By predicting the results, you can visually assess the actual energy usage of the hotel and suggest timely improvements to the hotel's operations to reduce the hotel's energy consumption.

通过建立支持向量机能耗预测模型，对酒店建筑能耗进行研究和分析。支持向量机模型以酒店空调系统的天气参数和运行参数为输入变量，通过确定正态分布区间确定输入参数的临界值，从而避免了离群值对模型预测的稳定性。选择RBF核函数作为支持向量机的核函数，并通过优化核参数来提高模型预测的准确性。最终模型预测的MSE值为2.22％，R ²为0.94。通过预测结果，您可以直观地评估酒店的实际能耗，并建议及时改善酒店的运营以减少酒店的能耗。