Power Line Engineering Computer Investment Prediction Model Based on SVR-PCA

With the rapid development of China's national economy, the demand for electricity in the whole society is increasing day by day, which leads to the increasing demand for investment in China's power grid. Accurate and effective power grid investment demand forecast can not only reasonably arrange the investment of power grid construction funds, reduce the cost of funds, but also effectively improve the operating conditions of power grid enterprises and ensure the stable development of power grid enterprises. Power grid line project occupies a large part of the power grid investment, so accurate prediction of power grid line project investment is particularly important. This paper first reviews the research status of domestic and foreign scholars on power grid line engineering investment demand forecasting, data mining technology and prediction technology, and then on the basis of literature review, from the three aspects of infrastructure standards, material prices and plant construction costs, this paper preliminarily selects the influencing factors of power grid overhead lines and cable lines investment, and uses principal component analysis to identify the key factors of power grid line engineering investment. The input vector of the model is obtained, and then the support vector machine regression is used to predict the investment of power grid line engineering. In this paper, data mining is used as a mean to propose the SVR-PCA prediction method to predict the investment in power grid line engineering. Through empirical research, it is proved that the model is effective, applicable, and superior in predicting the investment in power grid line engineering, which provides a new method for predicting the investment in power grid line engineering.