基于灰色神经网络的地铁牵引用电预测模型
张军1王凯1刘佳喜1李根1赵岩1王鹏1耿伟1张浩2陈欢2
Prediction Model of Metro Traction Power Consumption Based on Grey Neural Network
ZHANG Jun1WANG Kai1LIU Jiaxi1 LI Gen1ZHAO Yan1WANG Peng1GENG Wei1ZHANG Hao2CHEN Huan2
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作者信息:1.天津市地下铁道集团有限公司， 300011， 天津
2.天津凯发电气股份有限公司， 300392， 天津
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Affiliation:1.Tianjin Rail Transit Co., Ltd., 300011, Tianjin, China
2.Tianjin Keyvia Electric Co., Ltd., 300392, Tianjin,China
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关键词:
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Key words:
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DOI:10.16037/j.1007-869x.2024.07.003
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中图分类号/CLCN:TM714；U239.5
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栏目/Col:学术专论
摘要:
［目的］为了提高列车运行效率，需对地铁牵引能耗进行监测，并建立相关能耗模型对地铁牵引能耗进行预测分析。［方法］介绍了灰色预测模型和BP(反向传播)神经网络的基本原理；以天津某典型地铁车站2021年6月的牵引日用电量数据为例，采用灰色关联分析法筛选出与地铁牵引日用电量关联度大的影响因素，基于GM(1,1)灰色预测模型预测出短期牵引日用电量；将所筛选出的关联度大的影响因素、GM(1,1)灰色预测模型预测的短期牵引日用电量及相邻历史牵引日用电量数据，作为BP神经网络模型中的输入量进行训练，建立GM-BP灰色神经网络模型，并生成所需短期地铁牵引日用电量预测数据。［结果及结论］与传统GM(1,1)灰色预测模型和BP神经网络模型相比，通过GM-BP灰色神经网络模型预测的短期牵引日用电量预测误差有明显的改善，能够作为有效的地铁牵引能耗数据进行短期预测数据分析。
Abstracts:
［Objective］ In order to improve train operational efficiency, it is necessary to monitor metro traction power consumption and establish a relevant energy consumption model for prediction analysis of metro traction power consumption. ［Method］ The basic principles of grey prediction model and BP (backpropagation) neural network are introduced. Taking the traction daily electricity consumption data for a typical metro station in Tianjin in June 2021 as example, the grey correlation analysis method is used to select the influencing factors with high correlation to the daily traction power consumption of metro. Based on the GM (1,1) grey prediction model, the short-term traction daily power consumption is predicted. The selected influencing factors with high correlation, the short-term traction daily power consumption predicted by GM (1,1) grey model, and the adjacent historical traction daily power consumption data are used as input for training in BP neural network model to establish the GM-BP grey neural network model. The required short-term metro traction daily power consumption prediction data is generated. ［Result & Conclusion］ Compared with conventional GM (1,1) grey prediction model and BP neural network model, the prediction error of short-term traction daily power consumption predicted by the GM-BP grey neural network model shows significant improvement, and can be used as effective metro traction power consumption data for short-term prediction data analysis.
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