Prediction of Power Grid Material demand based on Based on matrix decomposition

Abstract:

It is of great significance to accurately predict the material demand of substation and distribution network for saving project cost and improving capital utilization. Researchers have carried out a series of studies on power material demand forecasting, and proposed many prediction models and algorithms such as neural networkbased algorithms. However, these algorithms have several disadvantages. Specifically, these algorithms can only process simple and ideal input, predict the demand of limited materials, and suffer from the problem of low accuracy. As a result, in current production systems, the demand of electric power materials is predicted by experienced experts according to the preliminary design scheme of the project manually. In order to solve the existing shortcomings of the current demand forecasting methods, this paper proposes a forecasting method based on matrix decomposition. The method takes the historical data of the power grid construction project material requirements and part of the project plan as input, and use matrix decomposition algorithm to predict the demand for other materials in the project. The matrix decomposition algorithm can be implemented with the material data of some projects instead of a large amount of historical usage data. In addition, the developed model does not need to be trained in advance.