Abstract
To establish and consummate the electric power network, the construction and investment scale of power substation projects is expanding every year. As a capital-technology-intensive project, it has high requirements for power substation project management. Accurate cost forecasting can help to reduce the project cost, improve the investment efficiency, and optimize project management. However, affected by many factors, the construction cost of a power substation project usually presents strong nonlinearity and uncertainty, which make it difficult to accurately forecast the cost. This paper presents a new hybrid substation project cost forecasting method called PCA-PSO-SVM model, which is a support vector machine (SVM) model optimized by a particle swarm optimization (PSO) algorithm with principal component analysis (PCA). In this intelligent prediction model, the PCA method is introduced to reduce the data dimension. Furthermore, the PSO algorithm is used to optimize the model parameters. In the example, 65 sets of substation construction data are input into PCA-PSO-SVM model for construction cost prediction, and the prediction results are compared with other prediction methods to verify the forecasting accuracy. The results show that the MAPE and RMSE of the PCA-PSO-SVM model is 6.21% and 3.62, respectively. And, the prediction accuracy of this model is better than that of other models, which can provide a reliable basis for investment decision-making of substation projects.
Highlights
To establish and consummate the electric power network, the construction and investment scale of power substation projects is expanding every year
The neural network model and the regression prediction model cannot adapt to the characteristics of fewer samples of substation project, and the prediction model based on gray theory cannot adapt to the characteristics of many influencing factors of substation project, so the error generated is rarely less than 10%. is study represents one of the first attempts to fill this important void by applying support vector machine optimized by principal component analysis and particle swarm algorithm to forecast the construction cost of substation projects
Cost control of substation projects directly affects the economic benefits of power grid projects, so it is an urgent problem to predict the cost with high accuracy in the early stage of the project
Summary
En, the principal component analysis is used to reduce the dimension of the original data, i.e., to recombine a series construction cost that affects the factor data into a new set of mutually irrelevant comprehensive indicators F1, F2, . Update the particle’s velocity and position, and repeat the steps of calculating the current particle fitness according to the particle’s velocity and position until the global optimal position is output when the iteration number reaches the maximum iteration number or the iterative process has traversed all coordinates, that is, the parameters optimized for support vector machine. Step 3: in this step, the original data of substation engineering after PCA dimension reduction in step 1 and the optimal parameters obtained by PSO iteration in step 2 are input into the model to establish the PCAPSO-SVM prediction model. Compared new fitness values with the current global optimal fitness and update the optimal fitness value
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