基于数据挖掘算法的地铁站能耗时序预测方法

建立了误差反向传播神经网络(BPNN)、决策树分类与回归树(CART)、支持向量回归机(SVR)三种普通的输入-输出预测模型,对地铁站能耗进行预测。基于数据挖掘算法对三个模型进行改进,得到了三种模型基于时间延迟的预测结果,对比了改进前后的预测结果,并确定了最佳的时间延迟。结果表明:普通的输入-输出模型中,SVR对能耗的预测更加精确;基于时间序列的能耗预测模型对BPNN预测模型的提升最大;滞后时长为5 min时,三种模型的预测精度最高;基于决策树CART算法的时序能耗预测模型对时间延迟的敏感度最高。

Time Series Prediction of Subway Station Energy Consumption Based on Data Mining Algorithm

Three general input-output prediction models:back propagation neural network(BPNN),classification and regression tree(CART)and support vector regression(SVR)are established to predict the energy consumption of subway station.The data mining algorithm is used to improve the three models and the prediction results of them based on time delay are obtained.Through comparing the results before and after the improvement,the optimal time delay is determined.Results show that among the general input-output models,the prediction of SVR model is the most accurate in terms of the energy consumption.The energy consumption prediction model based on time series contributes to the maximum improvement of BPNN prediction model.When the time delay is 5 min,the three models could achieve the best prediction accuracy,but the time series prediction model based on CART is the most sensitive one to time delay.