Demand Forecasting Method For Rush Repair Spare Parts Of Power Equipment Based On Scenario Analysis

In recent years,the large-scale interruption of power grid caused by emergencies has caused a series of social problems.The safety and stability of power supply is related to the safety of people’s lives and property.If the accident of power equipment occurs,it will have a serious impact on production and life,and even bring huge economic losses.Rush repair spare parts of power equipment are special reserve materials for enterprises to repair power failures and ensure power supply safety.Once out of stock,it may aggravate the severity of accidents.It has become a hot and difficult issue in the electric power industry and academia to explore the scientific forecasting method of rush repair spare parts demand for electric power equipment,improve the response ability of electric power enterprises to emergencies and save social costs.The application scenario of rush repair spare parts of electric power equipment is complicated,and its demand is highly uncertain and the interval is long,which makes the prediction of rush repair spare parts demand of electric power equipment very difficult.Aiming at the problem of demand prediction for rush repair spare parts of electric power equipment,this paper constructs a forecasting method for rush repair spare parts of electric power equipment based on scenario analysis.The research works are as follows:(1)Consult the literature of spare parts demand prediction and collect the relevant information of spare parts management of electric power enterprises,analyze the research status of the problem of demand prediction of rush repair spare parts of electric power equipment,summarize the characteristics of the current demand of rush repair spare parts of electric power equipment,and provide theoretical support for the research of this paper;(2)In order to collect effective spatiotemporal data that can be used for scenario analysis of rush repair spare parts of power equipment,the data crawler method is used to obtain relevant data,and the data fusion method and data processing method are used to fuse and process the relevant data of demand prediction of rush repair spare parts of power equipment,and the random forest model is used to reduce the data dimension.(3)Starting from the situational factors that affect the demand for rush repair spare parts of power equipment,the Bayesian network is adopted to carry out situational evolution analysis of decision-making scenarios,and identify the core scenarios that trigger rush repair spare parts of power equipment.Secondly,combined with expert experience,the weight of scenario elements was set,and the Adaptive Neural-Network-Based Fuzzy Interference System was used to dig the fuzzy correlation between the core scenario and the demand for spare parts from the data,so as to predict the demand for rush repair spare parts of power equipment.(4)In order to verify the feasibility of this model in practical application,the above scenario analysis-based prediction model of rush repair spare parts demand of power equipment is used to forecast the demand of rush repair spare parts of a power enterprise,and the results are compared and analyzed with the common prediction methods in the electric power field,proving the superiority of the prediction model built in this paper.From the perspective of power equipment rush repair spare parts demand generation,this paper constructs rush repair spare parts demand decision scenario according to scenario elements,innovatively combines scenario analysis method with the adaptive neural-network-based fuzzy interference method,and combines data sample learning with expert experience to build a power equipment rush repair spare parts demand prediction model based on scenario analysis.This study provides a new method and a new idea for solving the problem of rush repair spare parts demand prediction of electric power equipment,improves the scientific and accurate prediction of rush repair spare parts demand from the perspective of scenario,reduces the blindness of rush repair spare parts inventory of electric power equipment,and helps realize the transformation of rush repair spare parts reserve strategy from "emergency" to "preventive".