Price Prediction of Power Material Procurement Based on Current Linear Regression Model

• Published in: Procedia computer science 2024, Vol.247, p.290-299
• Main Authors: Liu, Kangjun, Wei, Bo, Chen, Yiliang, Huang, Zhengqiu, Ma, Wanyi
• Format: Article
• Language: English
• Subjects: Linear regression; power equipment; Price forecasting; Prices of raw materials; Random forest
• ISSN: 1877-0509; 1877-0509
• DOI: 10.1016/j.procs.2024.10.034

Currently, the rapid development of China's social economy has put forward higher requirements for the power supply chain, and the research on the power material price prediction model helps to ensure the adaptability and flexibility of the supply chain and provides assistance to the management of power materials. However, the time discontinuity of power material procurement data, the existing research methods to meet the demand for forecasting data in discontinuous time, this paper starts from the raw materials of electric power equipment, using Granger causality test to screen the factors affecting the price of raw materials and the lead time, and predicts the price of raw materials through the two models of linear regression and Random Forest, and calculates the numerical relationship between the raw material price and the purchase price of electric power materials, so as to utilize the predicted price of raw materials to forecast the price of electric power materials and to predict the price of electric power materials. Finally, it is found that the linear regression model is more favorable for the prediction of power equipment procurement price, and it predicts the future procurement price of 110kV power cables, and provides procurement strategies based on the prediction results.