ENERGY DATA ANOMALY CAUSE ANALYSIS METHOD BASED ON RANDOM FOREST AND SUPPORT VECTOR MACHINE

An energy data anomaly cause analysis method based on a random forest and a support vector machine. The method comprises: step 1: performing data cleaning processing, data labeling and model parameter tuning by using a model training module; step 2: supporting, by using an abnormal-data receiving module, the reading of relevant abnormal power load control data from MySQL, Oracle and Postgre; step 3: data processing, which involves performing key information screening, redundant data deletion and time window calculation on abnormal data by using big data technology, wherein Python extracts the abnormal power load control data from Oracle and Postgre in a JDBC manner; and step 4: performing cause analysis on the abnormal data by using an anomaly cause analysis module, and feeding back an anomaly cause analysis result. In the method, on the basis of a random forest and a support vector machine model, and according to abnormal power load control data that is fed back by a power system, cause analysis is performed