Simulation of Annual Investment Forecast of Power Grid Infrastructure Construction Based on Data Mining Algorithm
Shangxing Ye, Hongjun Wen, Yi Qiu, Pan Xia, Caifei Wang · 2024
Driven by the steady and good development trend of economic operation, the social power consumption is also growing, and a stable grid structure is an important guarantee to ensure power supply. In order to meet the demand, the power grid construction task increases year by year, and the investment scale also increases year by year. However, under the influence of the national macroeconomic situation, large-scale investment will make power grid companies face huge operating pressure, especially in terms of economic benefits. How to formulate scientific and reasonable investment scale prediction and distribution mode to avoid inefficient and ineffective investment is of great significance for power supply companies to make investment planning, and also provides theoretical and methodological support for optimizing the distribution of power grid construction funds. This paper takes data mining as a means and takes the power grid investment demand forecasting model as the research object, and proposes a new power grid investment demand forecasting model based on data mining. By means of data mining, this paper puts forward a new power grid investment demand forecasting model, and the simulation results show that the average relative error compared with other forecasting models is increased by 1.88%, which proves the effectiveness, applicability and superiority of the proposed model for power grid investment demand forecasting, and provides a new method for power grid investment forecasting.