Daily load forecasting of urban gas based on GRA-ABC-BPNN model
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...
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| Format: | Article |
| Language: | zho |
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Editorial Office of Oil & Gas Storage and Transportation
2022-08-01
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| Series: | You-qi chuyun |
| Subjects: | |
| Online Access: | http://yqcy.xml-journal.net/cn/article/doi/10.6047/j.issn.1000-8241.2022.08.015 |
| _version_ | 1797210115891265536 |
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| author | Rongge XIAO Bo LIU Qinxue WANG Haiwei LIN |
| author_facet | Rongge XIAO Bo LIU Qinxue WANG Haiwei LIN |
| author_sort | Rongge XIAO |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-24T10:05:28Z |
| format | Article |
| id | doaj.art-a091982b5db74f47a3f09fca89d25801 |
| institution | Directory Open Access Journal |
| issn | 1000-8241 |
| language | zho |
| last_indexed | 2024-04-24T10:05:28Z |
| publishDate | 2022-08-01 |
| publisher | Editorial Office of Oil & Gas Storage and Transportation |
| record_format | Article |
| series | You-qi chuyun |
| spelling | doaj.art-a091982b5db74f47a3f09fca89d258012024-04-13T02:18:37ZzhoEditorial Office of Oil & Gas Storage and TransportationYou-qi chuyun1000-82412022-08-0141898799410.6047/j.issn.1000-8241.2022.08.015yqcy-41-8-987Daily load forecasting of urban gas based on GRA-ABC-BPNN modelRongge XIAO0Bo LIU1Qinxue WANG2Haiwei LIN3College of Petroleum Engineering, Xi'an Shiyou University//Key Laboratory of Special Production Increasing Technology for Oil and Gas Fields of Shaanxi ProvinceCollege of Petroleum Engineering, Xi'an Shiyou University//Key Laboratory of Special Production Increasing Technology for Oil and Gas Fields of Shaanxi ProvinceCollege of Petroleum Engineering, Xi'an Shiyou University//Key Laboratory of Special Production Increasing Technology for Oil and Gas Fields of Shaanxi ProvinceXi'an Qinhua Natural Gas Co. Ltd.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.http://yqcy.xml-journal.net/cn/article/doi/10.6047/j.issn.1000-8241.2022.08.015daily gas load forecastingback propagation neural network (bpnn)artificial bee colonygrey relation analysisgenetic algorithm |
| spellingShingle | Rongge XIAO Bo LIU Qinxue WANG Haiwei LIN Daily load forecasting of urban gas based on GRA-ABC-BPNN model You-qi chuyun daily gas load forecasting back propagation neural network (bpnn) artificial bee colony grey relation analysis genetic algorithm |
| title | Daily load forecasting of urban gas based on GRA-ABC-BPNN model |
| title_full | Daily load forecasting of urban gas based on GRA-ABC-BPNN model |
| title_fullStr | Daily load forecasting of urban gas based on GRA-ABC-BPNN model |
| title_full_unstemmed | Daily load forecasting of urban gas based on GRA-ABC-BPNN model |
| title_short | Daily load forecasting of urban gas based on GRA-ABC-BPNN model |
| title_sort | daily load forecasting of urban gas based on gra abc bpnn model |
| topic | daily gas load forecasting back propagation neural network (bpnn) artificial bee colony grey relation analysis genetic algorithm |
| url | http://yqcy.xml-journal.net/cn/article/doi/10.6047/j.issn.1000-8241.2022.08.015 |
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