E3S Web Conf.
Volume 165, 20202020 2nd International Conference on Civil Architecture and Energy Science (CAES 2020)
|Number of page(s)||4|
|Section||Electrical and Power Engineering|
|Published online||01 May 2020|
Forecast of Power Grid Investment Scale Based on Support Vector Machine
1 Departments of Economics and Management, North China Electric Power University, Beijing 102200, China
2 State Grid Economic and Technological Research Institute Co., Ltd., Changping District, Beijing 102209, China
Economic transformation creates a new environment for grid investment. In the situation of high quality development, the traditional investment scale prediction model is no longer applicable. Aiming at the problems of single parameter of grid-driven investment scale prediction model and poor linear fitting accuracy, a provincial medium- and long-term investment scale prediction model based on support vector machine was proposed. Aiming at the single parameter and poor line fitting accuracy of the grid-driven investment scale prediction model under the new situation, the new environment, new directions and new requirements for grid investment are analyzed. Based on the support vector machine algorithm, a medium-and long-term investment scale prediction model for provincial grids based on support vector machines is proposed. The scale of provincial grid investment is expected from 2019 to 2022. The empirical results show that the prediction model constructed in this paper is effective and feasible. It is of great significance to explore the prediction model of medium and long-term investment scale of power grid enterprises in the new situation.
© The Authors, published by EDP Sciences, 2020
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