Demand prediction system of construction materials of power distribution network

Abstract

The invention discloses a demand prediction system of construction materials of a power distribution network. The system comprises a data acquisition module, a clustering module, a project type attribute determining module and a material demand prediction module; the data acquisition module obtains parameters of preset attributes of historical power grid projects as well as the material utilization amount of the different types of historical power grid projects; the clustering module carries out clustering analysis on the material utilization amount of the historical power grid projects in an improved k-means clustering method, and determines a clustering family; the project type attribute determining module uses a key word frequency analysis method in the clustering family to determine a project type attribute; and according to the material utilization amount of the historical power grid project, the parameter of the preset attribute of the historical project and the project type attribute, the material demand prediction module uses a neural network algorithm to construct a material demand prediction model, inputs the preset attribute parameter and project type attributes of a power-grid project to be predicted into the material demand prediction model, and outputs a predicted value of the material utilization amount of the corresponding power-grid project to be predicted. According to the invention, the cost of material prediction is reduced, and the prediction precision is higher.

Description

technical field [0001] The invention relates to a material forecasting system, in particular to a material demand forecasting system for electric power distribution network construction. Background technique [0002] The demand for production materials of power distribution network is large and there are many kinds. According to the material management requirements of the distribution network, the material demand forecasting work is carried out every year. The traditional method is a bottom-up working mode: the lower-level organization conducts research, statistics, estimates, and reports, and the upper-level organization approves, summarizes, and generates overall demand. From organization to collection, from review to summary, it takes a lot of manpower and material resources, reporting layer by layer, heavy review workload, long time period for generating demand, and low accuracy rate, material purchase, equipment storage, and material requisition for distribution network...

AI Technical Summary

Problems solved by technology

From organization to collection, from review to summary, it takes a lot of manpower and material resources, reporting layer by layer, heavy review workload, long time period for generating demand, and low accuracy rate, material purchase

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