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[1]肖荣鸽,刘博,王勤学,等.基于GRA-ABC-BPNN模型的城市燃气日负荷预测[J].油气储运,2022,41(08):987-994.[doi:10.6047/j.issn.1000-8241.2022.08.015]
　XIAO Rongge,LIU Bo,WANG Qinxue,et al.Daily load forecasting of urban gas based on GRA-ABC-BPNN model[J].Oil & Gas Storage and Transportation,2022,41(08):987-994.[doi:10.6047/j.issn.1000-8241.2022.08.015]
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基于GRA-ABC-BPNN模型的城市燃气日负荷预测

《油气储运》[ISSN:1000-8241/CN:13-1093/TE] 卷: 41 期数: 2022年08期 页码: 987-994 栏目: 出版日期: 2022-08-25
Title:
Daily load forecasting of urban gas based on GRA-ABC-BPNN model
文章编号:
1000-8241（2022）08-0987-08
作者:
肖荣鸽1; 刘博1; 王勤学1; 林海威2

1.西安石油大学石油工程学院·陕西省油气田特种增产技术重点实验室;2.西安秦华天然气有限公司

Author(s):
XIAO Rongge1; 3; 2; LIU Bo1; 3; 2; WANG Qinxue1; 3; 2; LIN Haiwei3; 4

1.College of Petroleum Engineering, Xi'an Shiyou University//Key Laboratory of Special Production Increasing Technology for Oil and Gas Fields of Shaanxi Province;2.Xi'an Qinhua Natural Gas Co. Ltd.

关键词:
燃气日负荷预测; BP神经网络; 人工蜂群算法; 灰色关联分析; 遗传算法
Keywords:
daily gas load forecasting;  Back Propagation Neural Network (BPNN);  artificial bee colony;  grey relation analysis;  genetic algorithm
分类号:
TE832
DOI:
10.6047/j.issn.1000-8241.2022.08.015
文献标志码:
A
摘要:

城市燃气负荷预测对于合理高效地调配燃气资源、解决城市燃气用户用气问题具有重要意义。通过灰色关联分析法（Grey Relation Analysis，GRA）对所确定的11个影响燃气日负荷的因素进行分析，依据关联度大小进行筛选，逐个剔除关联度较低的影响因素，将剩余关联度较高的影响因素作为BP神经网络（Back Propagation Neural Network，BPNN）的输入；采用人工蜂群（Artificial BeeColony，ABC）算法优化BPNN权值和阈值；搭建GRA-ABC-BPNN预测模型预测城市燃气日负荷，并对其准确性和有效性进行验证。结果显示：GRA-ABC-BPNN模型预测的城市燃气日负荷的平均绝对百分比误差（Mean Absolute Percentage Error，MAPE）值为0.5528％，而遗传算法优化BPNN（Genetic Algorithm-BPNN，GA-BPNN）模型及ABC-BPNN模型的MAPE值分别为1.4913％、0.6369％，证明了GRA-ABC-BPNN预测模型是一种有效且精度可观的城市燃气日负荷预测方法，为城市燃气日负荷预测提供了新的途径。（图5，表7，参32）

Abstract:

Urban gas load forecasting is of great significance for rationally and efficiently deploying gas resources and solving the problem of gas consumption by urban gas users. Herein, the 11 identified influencing factors of the daily gas load were analyzed through the Gray Relation Analysis (GRA) method and screened according to the correlation degree, having the influencing factors with little correlation eliminated one by one, and using the remained influencing factors with high correlation degree as the input of the Back Propagation Neural Network (BPNN). Meanwhile, the BPNN weights and thresholds were optimized with the Artificial Bee Colony (ABC) algorithm. Besides, a GRA-ABC-BPNN forecasting model was established to predict the daily load of urban gas, and the accuracy and effectiveness of the established forecasting model was verified. As shown by the results, the Mean Absolute Percentage Error (MAPE) of the daily load of urban gas forecast by GRA-ABC-BPNN model is 0.552 8%, while the MAPEs of Genetic Algorithm-BPNN (GA-BPNN) model and ABC-BPNN model are 1.491 3% and 0.636 9%, respectively. This indicates that the GRA-ABC-BPNN forecasting model is an effective and accurate method to forecast the daily load of urban gas, and it could provide a new way for daily load forecasting of urban gas. (5 Figures, 7 Tables, 32 References)

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备注/Memo

肖荣鸽，女，1978年生，教授，2014年博士毕业于西安理工大学水力学及河流动力学专业，现主要从事天然气处理与加工、城市燃气负荷预测研究及油气储运工程相关学科的教学工作。地址：陕西省西安市雁塔区电子二路东段18号，710065。电话：13572960817。Email：xiaorongge@163.com
基金项目：陕西省科技厅重点研发计划项目“城镇复杂用户燃气负荷预测与调峰方案优化研究”， 2021GY-139。
（收稿日期：2021-10-28；修回日期：2022-07-03；编辑：刘博）

更新日期/Last Update: 2022-08-25