基于在线实测数据的地铁列车空调回风温度预测
杨闯1杨宇2陈亮2陈焕新1程亨达1
Prediction of Metro Train Air-conditioning Return Air Temperature Based on Real-time Field-measured Data
YANG Chuang1YANG Yu2CHEN Liang2CHEN Huanxin1CHENG Hengda1
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作者信息:1.华中科技大学能源与动力工程学院，430074，武汉
2.广州鼎汉轨道交通车辆装备有限公司，510260，广州
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Affiliation:1.School of Energy and Power Engineering, Huazhong University of Science and Technology, 430074, Wuhan, China
2.Guangzhou Dinghan Rail Transit Vehicle Equipment Co., Ltd., 510260, Guangzhou, China
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关键词:
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Key words:
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DOI:10.16037/j.1007-869x.2024.12.018
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中图分类号/CLCN:U270.38+3
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栏目/Col:学术专论
摘要:
［目的］为了能够提前根据环境温度调控地铁列车空调系统的制冷能力，有必要对地铁列车空调回风温度预测进行研究。［方法］采用时序预测法预测地铁列车空调系统回风温度的变化趋势。利用空调系统传感器在线采集广州某地铁列车空调系统的实时运行数据，通过箱形图清洗数据异常值，采用滑动窗口处理输入及输出数据的时间跨度，构建LSTM(长短期记忆)神经网络模型对空调机组回风温度进行预测，并对比分析不同样本个数对模型预测精度的影响。［结果及结论］LSTM神经网络模型能够学习地铁列车空调系统的温度控制逻辑，预测温度曲线与真实温度曲线有相似的变化趋势，适用于预测地铁列车空调机组回风温度。通过参数优化将模型精度提高至0.84，实现了机组回风温度的精准预测。增大模型单次训练选取的样本数能够缩短模型的训练时间，但同时其模型最终预测精度也会有所降低。
Abstracts:
［Objective］ To enable proactive adjustment of the cooling capacity for metro train AC (air-conditioning) system based on ambient temperature, it is essential to study the prediction of return air temperature in metro train AC systems. ［Method］ A time series forecasting method is employed to predict the variation trend in return air temperature of metro train AC systems. Real-time operational data from the air-conditioning system of a Guangzhou Metro train are collected through AC sensors. Outliers are removed using a boxplot, and a sliding window approach is applied to handle the time span of input and output data. An LSTM (long short-term memory) neural network model is then constructed to predict the return air temperature of the AC units, and the impact of different sample sizes on the model prediction accuracy is comparatively analyzed. ［Result & Conclusion］ The LSTM neural network model can learn the temperature control logic of metro train AC system, with the predicted temperature curve closely matching the actual temperature curve, therefore is suitable for predicting the return air temperature of metro AC units. Through parameter optimization, the model accuracy is improved to 0.84, enabling precise prediction of unit return air temperature. Increasing the sample size for a single training session can shorten model training time but may reduce the model final prediction accuracy to some extent correspondently.
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