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首页> 外文期刊>Mathematical Problems in Engineering: Theory, Methods and Applications >Intelligent Prediction of the Construction Cost of Substation Projects Using Support Vector Machine Optimized by Particle Swarm Optimization
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Intelligent Prediction of the Construction Cost of Substation Projects Using Support Vector Machine Optimized by Particle Swarm Optimization

机译：智能预测粒子群优化优化的支持向量机的变电站项目的施工成本

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摘要

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.
机译：建立和完善电力网络，功率变电站项目的建设和投资规模每年都在扩大。作为资本技术密集型项目，它对电力变电站项目管理有很高的要求。准确的成本预测有助于降低项目成本，提高投资效率，优化项目管理。然而，受许多因素影响，功率变电站项目的施工成本通常具有强烈的非线性和不确定性，这使得难以准确预测成本。本文介绍了一种新的混合变电站项目成本预测方法，称为PCA-PSO-SVM模型，它是由具有主成分分析（PCA）的粒子群优化（PSO）算法优化的支持向量机（SVM）模型。在此智能预测模型中，引入PCA方法以减少数据尺寸。此外，PSO算法用于优化模型参数。在该示例中，将65组变电站构造数据输入到PCA-PSO-SVM模型中进行施工成本预测，并将预测结果与其他预测方法进行比较，以验证预测精度。结果表明，PCA-PSO-SVM模型的MAPE和RMSE分别为6.21％和3.62。并且，该模型的预测精度优于其他模型的预测精度，其可以为变电站项目的投资决策提供可靠的基础。

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