Open AccessTransformer Order Demand Forecasting Based on Grey Forecasting Model
Author(s) -
Chuncai Xiao,
Yu-Hsien Liao
Publication year - 2021
Publication title -
iop conference series. earth and environmental science
Language(s) - English
Resource type - Journals
eISSN - 1755-1307
pISSN - 1755-1315
DOI - 10.1088/1755-1315/831/1/012004
Subject(s) - transformer , computer science , electric power system , nonlinear system , time series , development plan , econometrics , engineering , economics , machine learning , power (physics) , voltage , electrical engineering , physics , civil engineering , quantum mechanics
For a grey system, the known information is not enough to create an accurate physical model. But there are some rules or characteristics about time series in it. According to the grey system theory, the internal law can be found to a certain extent. According to the grey system theory, the order demand of transformer can be predicted. This prediction model overcomes the influence of the nonlinear relationship and random fluctuation of the system on the prediction accuracy. The experimental data also show that the prediction accuracy of the model is good, and the result is ideal. According to the prediction model, the transformer enterprises can adjust the production plan in time to adapt to the market changes and improve the economy of the transformer. At the same time, for the whole transformer market, timely prediction is also conducive to the state’s macro-control of the power industry and promote the development of power construction.