基于动态客流量模型的地铁车站空调负荷预测

Cooling Load Prediction for Metro Station Based on Dynamic Passenger Flow Model

摘要:

为了准确预测地铁车站的空调负荷，首先通过地铁车站能耗监测平台的历史数据分析，识别得到客流量和室外气象参数是主要影响因素。其次利用车站CO₂体积浓度逐时监测数据建立客流量神经网络预测模型，并与闸机数据对比，预测模型的复相关系数R²可达0.87。以客流量预测为基础，建立了车站空调负荷预测模型，并比较了不同时间尺度训练数据下误差反向传播神经网络算法和支持向量机算法的预测效果，两种算法的R²达到了0.95以上，均方根误差在70~90 kW之间，预测精度较高，但支持向量机算法的运算时间是误差反向传播神经网络算法的3~4倍左右，推荐数据量较大时优先选择神经网络算法。

Abstract:

In order to accurately predict the cooling load of metro stations， firstly， by analyzing the historical data from the metro station monitoring platform， the passenger flow and meteorological parameters are identified as the main influential factors. Then， a dynamic passenger flow neural network prediction model is established， by utilizing the hourly monitored carbon dioxide volume concentration data in the metro station， A comparion of the carbon dioxide data with the automatic fare collection data indicates that the correlation coefficient R² of the prediction model can reach 0.87. After that， based on passenger flow prediction， the cooling load predicting model of the metro station is built. A comparsion of the predicting performance of back propagation neural network and support vector machine algorithm under different time scale training data suggests that the R² of both models can reached 0.95 or higher， and the root mean square error is between 70 kW and 90 kW， which is of a high prediction accuracy. However， the calculation time of the support vector machine algorithm is about 3 to 4 times that of the back propagation neural network algorithm. The neural network model is recommended when the amount of data is large.