Intelligent prediction of the construction cost of substation projects using support vector machine optimized by particle swarm optimization

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.