Analysis and Research on Electric Heating Risk Early Warning Based on Embedded Feature Selection and DBSCAN Adaptive Clustering

Abstract

With the gradual expansion of the scale of”coal to electricity” users, the number of complaints about the heating effect, electricity safety and heating equipment safety guarantee in the clean heating operation process continues increasing. It is not possible to warn of possible emergencies in advance, and can be only remedied afterwards, completely in a passive response state. Therefore, rapid and accurate positioning of the key link is an urgent problem to be solved, but also the key to improve user satisfaction. Aimed at above problems, this paper established an automatic, information and intelligent electric heating risk warning mechanism. Based on the embedded feature selection algorithm and the DBSCAN adaptive clustering algorithm, a standardized vocabulary of customer appeals and complaint topics were constructed, combined with user historical electricity consumption data, and through the monitoring and matching of risk topics, an early warning model of customer electric heating abnormal risks was established. The model proposed in the article has strong practicability and provides strong support for lean management on the grid side, precise positioning of problems on the operation and maintenance side, government-side management decisionmaking and user satisfaction, and can promote safe, reliable and economical operation of the grid.