Mathematical methods for maintenance and operation cost prediction based on transfer learning in State Grid

The electric power enterprise is an important basic energy industry for national development, and it is also the first basic industry of the national economy. With the continuous expansion of State Grid, the progressively complex operating conditions, and the increasing scope and frequency of data collection, how to make reasonable use of electrical big data, improve utilization, and provide a theoretical basis for the reliability of State Grid operation, has become a new research hot spot. Since electrical data has the characteristics of large volume, multiple types, low-value density, and fast processing speed, it is a challenge to mine and analyze it deeply, extract valuable information efficiently, and serve for the actual problem. According to the features of these data, this paper uses artificial intelligence methods such as time series and support vector regression to establish a data mining network model for standard cost prediction through transfer learning. The experimental results show that the model in this paper obtains better prediction results on a small sample data set, which verifies the feasibility of the deep transfer model. Compared with activity-based costing and the traditional prediction method, the average absolute error of the proposed method is reduced by 10%, which is effective and superior.