Cost Forecasting Model of Transformer Substation Projects Based on Data Inconsistency Rate and Modified Deep Convolutional Neural Network
Precise and steady substation project cost forecasting is of great significance to guarantee the economic construction and valid administration of electric power engineering. This paper develops a novel hybrid approach for cost forecasting based on a data inconsistency rate (DIR), a modified fruit f...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2019-08-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/12/16/3043 |
| _version_ | 1798041021936828416 |
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| author | Hongwei Wang Yuansheng Huang Chong Gao Yuqing Jiang |
| author_facet | Hongwei Wang Yuansheng Huang Chong Gao Yuqing Jiang |
| author_sort | Hongwei Wang |
| collection | DOAJ |
| description | Precise and steady substation project cost forecasting is of great significance to guarantee the economic construction and valid administration of electric power engineering. This paper develops a novel hybrid approach for cost forecasting based on a data inconsistency rate (DIR), a modified fruit fly optimization algorithm (MFOA) and a deep convolutional neural network (DCNN). Firstly, the DIR integrated with the MFOA is adopted for input feature selection. Simultaneously, the MFOA is utilized to realize parameter optimization in the DCNN. The effectiveness of the MFOA−DIR−DCNN has been validated by a case study that selects 128 substation projects in different regions for training and testing. The modeling results demonstrate that this established approach is better than the contrast methods with regard to forecasting accuracy and robustness. Thus, the developed technique is feasible for the cost prediction of substation projects in various voltage levels. |
| first_indexed | 2024-04-11T22:15:40Z |
| format | Article |
| id | doaj.art-b9c669d112704c9398346c35098250b5 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-04-11T22:15:40Z |
| publishDate | 2019-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-b9c669d112704c9398346c35098250b52022-12-22T04:00:24ZengMDPI AGEnergies1996-10732019-08-011216304310.3390/en12163043en12163043Cost Forecasting Model of Transformer Substation Projects Based on Data Inconsistency Rate and Modified Deep Convolutional Neural NetworkHongwei Wang0Yuansheng Huang1Chong Gao2Yuqing Jiang3School of Economics and Management, North China Electric Power University, Beijing 102206, ChinaSchool of Economics and Management, North China Electric Power University, Beijing 102206, ChinaSchool of Economics and Management, North China Electric Power University, Beijing 102206, ChinaSchool of Economics and Management, North China Electric Power University, Beijing 102206, ChinaPrecise and steady substation project cost forecasting is of great significance to guarantee the economic construction and valid administration of electric power engineering. This paper develops a novel hybrid approach for cost forecasting based on a data inconsistency rate (DIR), a modified fruit fly optimization algorithm (MFOA) and a deep convolutional neural network (DCNN). Firstly, the DIR integrated with the MFOA is adopted for input feature selection. Simultaneously, the MFOA is utilized to realize parameter optimization in the DCNN. The effectiveness of the MFOA−DIR−DCNN has been validated by a case study that selects 128 substation projects in different regions for training and testing. The modeling results demonstrate that this established approach is better than the contrast methods with regard to forecasting accuracy and robustness. Thus, the developed technique is feasible for the cost prediction of substation projects in various voltage levels.https://www.mdpi.com/1996-1073/12/16/3043substation project cost forecasting modelfeature selectiondata inconsistency ratemodified fruit fly optimization algorithmdeep convolutional neural network |
| spellingShingle | Hongwei Wang Yuansheng Huang Chong Gao Yuqing Jiang Cost Forecasting Model of Transformer Substation Projects Based on Data Inconsistency Rate and Modified Deep Convolutional Neural Network Energies substation project cost forecasting model feature selection data inconsistency rate modified fruit fly optimization algorithm deep convolutional neural network |
| title | Cost Forecasting Model of Transformer Substation Projects Based on Data Inconsistency Rate and Modified Deep Convolutional Neural Network |
| title_full | Cost Forecasting Model of Transformer Substation Projects Based on Data Inconsistency Rate and Modified Deep Convolutional Neural Network |
| title_fullStr | Cost Forecasting Model of Transformer Substation Projects Based on Data Inconsistency Rate and Modified Deep Convolutional Neural Network |
| title_full_unstemmed | Cost Forecasting Model of Transformer Substation Projects Based on Data Inconsistency Rate and Modified Deep Convolutional Neural Network |
| title_short | Cost Forecasting Model of Transformer Substation Projects Based on Data Inconsistency Rate and Modified Deep Convolutional Neural Network |
| title_sort | cost forecasting model of transformer substation projects based on data inconsistency rate and modified deep convolutional neural network |
| topic | substation project cost forecasting model feature selection data inconsistency rate modified fruit fly optimization algorithm deep convolutional neural network |
| url | https://www.mdpi.com/1996-1073/12/16/3043 |
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