Construction of power network operation and maintenance cost prediction model based on data information mining

Abstract

It is very important to scientifically plan the asset operation and maintenance investment scale and formulate scientific and reasonable operation and maintenance capital investment strategies to meet the requirements of fine management development and deal with the complex and severe internal and external situation. At present, the reasonable scale of power grid asset operation and maintenance investment lacks certain, monitoring and evaluation means, and the traditional extensive asset operation and maintenance investment management mode has certain deficiencies. This paper is based on data information mining, combined with the current status of power grid operation and maintenance cost investment management and management characteristics, the system identifies total fixed assets, average operating life of equipment, equipment defect rate, equipment failure rate, power supply reliability rate, and electricity sales as key influences. Factors; then combined with the analysis of influencing factors, an intelligent prediction model based on neural network theory is constructed; finally, through empirical analysis, it is verified that the prediction error of the model is 10.35%.