Abstract
The project cost of power grid engineering technology transformation projects has shown importance in the development of China's power industry. By summarizing the models that can be used to predict the cost of technological transformation, the genetic algorithm based support vector machine (GA-SVM) and the cost estimation model based on extreme gradient boosting machine learning algorithm (XGBoosting) are studied. This paper combines the above theories and the Shapley value theory in game theory to construct a combined cost estimation model, which can realize high-precision estimation of power grid technological transformation projects. This paper takes the overhead line project as an example for simulation, which proves that the combined forecasting model can accurately reflect the actual project cost, and it has reference value for the cost forecast of the power grid engineering technology transformation project.
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