SVM (Support Vector Machine)-based power consumption abnormality detection method

The invention discloses an SVM (Support Vector Machine)-based power consumption abnormality detection method. An overall system comprises a metering database system (1-1), a preprocessing module (1-2), a One-class SVM sorting machine (1-3), a warning message filtering module (1-4) and a warning module (1-5), and the relation of all the modules is shown by using a data flowing direction (1-6); a system flowchart consists of thirteen modules: a data collection module (2-1), a data preprocessing module (2-2), a training sample collection module (2-3), a working day model module (2-4), a holiday model module (2-5), a weekend model module (2-6), a data preprocessing module (2-7), a KKT condition judger module (2-8), a One-class SVM classifier module (2-9), a system decision module (2-10), a warning module (2-11), a program execution direction module (2-12) meeting KKT conditions, and a program execution direction module (2-13) incapable of meeting KKT conditions. The SVM-based power consumption abnormality detection method has the remarkable advantages of being small in training samples, capable of setting detection accuracy, and the like.