Grid investment capability prediction based on path analysis and BP neural network
With the more complex investment environment of China’s power grid, the accurate prediction of the investment ability of power grid enterprises has become an important prerequisite for managers to make precise investment decisions. This paper first selects the factors affecting the investment capaci...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
EDP Sciences
2022-01-01
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| Series: | SHS Web of Conferences |
| Subjects: | |
| Online Access: | https://www.shs-conferences.org/articles/shsconf/pdf/2022/21/shsconf_emsd2022_01043.pdf |
| _version_ | 1797951061847179264 |
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| author | Zhang Chengke Liu Huideng Zhu Yuning Wang Yuzhu Hu Zeping |
| author_facet | Zhang Chengke Liu Huideng Zhu Yuning Wang Yuzhu Hu Zeping |
| author_sort | Zhang Chengke |
| collection | DOAJ |
| description | With the more complex investment environment of China’s power grid, the accurate prediction of the investment ability of power grid enterprises has become an important prerequisite for managers to make precise investment decisions. This paper first selects the factors affecting the investment capacity of the power grid from the internal and external environment, and establishes the index system of the factors affecting the investment capacity. Secondly, the path analysis is used to deeply explore the interaction relationship and influence degree of each index and investment capacity. Finally, the maximum investment capacity of the power network can be predicted based on the BP neural network prediction model. The results show that the BP neural network prediction model can achieve higher prediction accuracy when predicting the power grid investment capability. |
| first_indexed | 2024-04-10T22:24:48Z |
| format | Article |
| id | doaj.art-044057fac4f740859216ac17b5d641fb |
| institution | Directory Open Access Journal |
| issn | 2261-2424 |
| language | English |
| last_indexed | 2024-04-10T22:24:48Z |
| publishDate | 2022-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | SHS Web of Conferences |
| spelling | doaj.art-044057fac4f740859216ac17b5d641fb2023-01-17T09:16:14ZengEDP SciencesSHS Web of Conferences2261-24242022-01-011510104310.1051/shsconf/202215101043shsconf_emsd2022_01043Grid investment capability prediction based on path analysis and BP neural networkZhang Chengke0Liu Huideng1Zhu Yuning2Wang Yuzhu3Hu Zeping4Chongqing Urban Power Supply Branch, State Grid Chongqing Electric Power CompanyChongqing Urban Power Supply Branch, State Grid Chongqing Electric Power CompanyChongqing Urban Power Supply Branch, State Grid Chongqing Electric Power CompanyNorth China Electric Power UniversityNorth China Electric Power UniversityWith the more complex investment environment of China’s power grid, the accurate prediction of the investment ability of power grid enterprises has become an important prerequisite for managers to make precise investment decisions. This paper first selects the factors affecting the investment capacity of the power grid from the internal and external environment, and establishes the index system of the factors affecting the investment capacity. Secondly, the path analysis is used to deeply explore the interaction relationship and influence degree of each index and investment capacity. Finally, the maximum investment capacity of the power network can be predicted based on the BP neural network prediction model. The results show that the BP neural network prediction model can achieve higher prediction accuracy when predicting the power grid investment capability.https://www.shs-conferences.org/articles/shsconf/pdf/2022/21/shsconf_emsd2022_01043.pdfindex systempath analysisbp neural networkpower grid investment capability |
| spellingShingle | Zhang Chengke Liu Huideng Zhu Yuning Wang Yuzhu Hu Zeping Grid investment capability prediction based on path analysis and BP neural network SHS Web of Conferences index system path analysis bp neural network power grid investment capability |
| title | Grid investment capability prediction based on path analysis and BP neural network |
| title_full | Grid investment capability prediction based on path analysis and BP neural network |
| title_fullStr | Grid investment capability prediction based on path analysis and BP neural network |
| title_full_unstemmed | Grid investment capability prediction based on path analysis and BP neural network |
| title_short | Grid investment capability prediction based on path analysis and BP neural network |
| title_sort | grid investment capability prediction based on path analysis and bp neural network |
| topic | index system path analysis bp neural network power grid investment capability |
| url | https://www.shs-conferences.org/articles/shsconf/pdf/2022/21/shsconf_emsd2022_01043.pdf |
| work_keys_str_mv | AT zhangchengke gridinvestmentcapabilitypredictionbasedonpathanalysisandbpneuralnetwork AT liuhuideng gridinvestmentcapabilitypredictionbasedonpathanalysisandbpneuralnetwork AT zhuyuning gridinvestmentcapabilitypredictionbasedonpathanalysisandbpneuralnetwork AT wangyuzhu gridinvestmentcapabilitypredictionbasedonpathanalysisandbpneuralnetwork AT huzeping gridinvestmentcapabilitypredictionbasedonpathanalysisandbpneuralnetwork |