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

Abstract

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.