Method and system for predicting power grid defect materials through multi-model fusion

Abstract

The invention discloses a method and system for predicting power grid defect materials through multi-model fusion. The method comprises the steps: sequentially building a regression model, a negativefeedback neural network model, a gradient boosting tree GBDT model and an XgBoost model based on the fault probability strategies of different regions, time and equipment; uniformly inputting the acquired defective material data and the corresponding meteorological data into the regression model, the negative feedback neural network model, the gradient boosting tree GBDT model and the XgBoost model for training; respectively outputting prediction results corresponding to the models; and performing fusion processing on the trained multiple models by using a multi-model fusion strategy to form aprediction model, and solving an average value of the prediction result to obtain a final fusion prediction result. According to the invention, early storage of emergency repair material types, scales and places is guided, the prospective planning capability of material management of a power grid company is improved, and the cost of operation materials of a power grid enterprise is reduced whilethe operation reliability of a power grid is enhanced.

Description

technical field [0001] The invention relates to the technical field of information processing of electric power materials in a power grid, in particular to a method and system for predicting defective materials in a power grid through multi-model fusion. Background technique [0002] During the operation of the power grid, many equipment will fail due to various reasons, such as load during operation, thunderstorm weather, heavy snow weather and landslides, etc. Some failures cannot be solved through maintenance, so the materials need to be repaired Reserves, traditional material storage is relatively inefficient in terms of efficiency, that is, through some simple analysis and strategies to ensure the supply of materials. [0003] With the development and transformation of power grid enterprises, the efficiency of warehousing and scheduling has received attention. With the development of technologies such as big data, Internet of Things, and artificial intelligence, data ac...

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Problems solved by technology

[0002] During the operation of the power grid, many devices will fail due to various reasons, such as load during operation, thunderstorm weather, heavy snow weather and landslides, etc. Some failures cannot be solved through maintenance, so the materials need to be repaired Reserves, the efficiency of traditional material storage is relatively low, that is, through some simple analysis and strategies to ensure the supply of materials

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