基于ARMA模型的地铁站环控系统能耗预测
Energy Prediction Method for Metro HVAC Systems based on the ARMA Model

本文通过对时间序列的研究分析，提出一种基于自回归移动平均（ARMA）模型来预测地铁站环控系统能耗的方法。首先，对采集的地铁站环控系统能耗数据进行平稳性检验和白噪声检验；然后依据数据样本的自相关系数、偏自相关系数以及AIC准则确定模型最优参数，建立能够有效预测地铁站环控系统能耗的ARMA模型；采用了4种方法对拟合模型的有效性进行检验；同时，利用平均绝对误差（MAE）和均方根误差（RMSE）对模型拟合效果进行分析。结果表明，该方法能够有效提取出能耗数据中有用的信息，对于地铁站环控系统能耗预测具有较高的拟合精度。
This paper proposes an energy consumption-prediction method for metro heating, ventilation and air-conditioning (HVAC) systems based on an auto-regressive moving average (ARMA) model using a time-series data analysis. Firstly, stationarity analysis and white-noise analysis (also known as pure stochastic analysis) were carried out on the collected energy-consumption data from actual metro HVAC systems. Secondly, optimal model parameters were determined using the autocorrelation function (ACF), and partial autocorrelation function (PACF) and Akaike information criterion (AIC). Finally, an effective energy consumption-prediction model was established. Four different methods were employed to test the effectiveness of the established ARMA model. Meanwhile, two performance indexes, namely, mean absolute error and root mean square error, were adopted to evaluate its performance in terms of fitting the observed energy consumption data. The results demonstrate that the proposed method based on the ARMA model could extract useful information from the energy data and is thus effective for energy consumption prediction of metro HVAC systems