Cost Forecasting Model of Transformer Substation Projects Based on Data Inconsistency Rate and Modified Deep Convolutional Neural Network

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...

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Bibliographic Details
Main Authors: Hongwei Wang, Yuansheng Huang, Chong Gao, Yuqing Jiang
Format: Article
Language:English
Published: MDPI AG 2019-08-01
Series:Energies
Subjects:
substation project cost forecasting model
feature selection
data inconsistency rate
modified fruit fly optimization algorithm
deep convolutional neural network
Online Access:https://www.mdpi.com/1996-1073/12/16/3043

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