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.
Highlights
The inadequate management and supervision of substation projects tend to bring about high cost, which has critical effects on the economy and sustainability of power engineering
Based on the aforementioned studies, this paper develops a novel hybrid approach for cost forecasting based on the data inconsistency rate (DIR), the deep convolutional neural network (DCNN) and the modified fruit fly optimization algorithm (MFOA)
43.0126, σ kernel functions, was applied, and the parameters optimized by MFOA equaled: 43.0126, For the purpose of verifying the performance of the established approach, four other methods incorporating the MFOA–DCNN, the DCNN, an support vector machine (SVM) and the back propagation neural network (BPNN) were used for comparison
Summary
The inadequate management and supervision of substation projects tend to bring about high cost, which has critical effects on the economy and sustainability of power engineering. Along with the less sample data, the difficulty of cost forecasting for substation projects has been increased It is of great significance for the sustainability of electric power engineering investment to study and construct the substation cost forecasting model and accurately forecast the substation cost. Traditional forecasting techniques primarily consist of time series [3], grey prediction [4], regression analysis [5] and so on. Reference [3] designed a time series prediction model for engineering cost based on bills of quantities and evaluation. Reference [4] put forward an improved grey forecasting method optimized by a time response function to predict main construction cost indicators in power projects, where the constant C was determined through the minimum Euclidean distance of an original series and constraints of simulation values. In reference [6], a forecasting technique grounded on multiple structure integral linear regression was established in line with the characteristics
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