A power consumption data anomaly detection model based on isolated forest algorithm

Abstract

The invention discloses a power consumption data anomaly detection model based on an isolated forest algorithm. The model comprises a feature extraction module, a feature dimension reduction module, an isolated forest calculation module, an expert sample module and a secondary training module, wherein the feature extraction module extracts the time series of the user's power consumption data fromthe original data set as an initial feature set, and then carries out dimensionless and feature selection processing on the initial feature set; the feature dimension reduction module adopts principalcomponent analysis and self-coding network method to reduce the dimension of the initial feature set to get the effective feature set; the isolated forest computing module uses isolated forest algorithm to calculate the outlier score of each user to determine whether the user data is abnormal or not. The electric power data anomaly detection model based on the isolated forest algorithm of the invention is an unsupervised electric power data anomaly detection model, which not only can quickly process a large amount of data, but also can adapt to the situation of lack of training samples, and can better meet the practical requirements of the electric power department.

Description

technical field [0001] The invention relates to an abnormal detection model of power consumption data based on an isolated forest algorithm, and relates to the fields of power data analysis, power data abnormal prediction, power data mining and data technology, and smart grid technology. Background technique [0002] In recent years, around the problem of abnormal pattern detection on the electricity side, three types of technical methods based on statistics, distance and learning have been developed. From a data point of view, and borrowing common concepts from the field of machine learning, these methods can be divided into two broad categories: supervised and unsupervised. Supervised methods usually require enough labeled training samples, which means that the electricity usage data needs to contain user type information, that is, whether the user is an abnormal user. Such data requires human expert identification and cannot be scaled to a large scale. Therefore, althou...

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Problems solved by technology

The traditional electricity anomaly detection mode has been difficult to meet the existing requirements, and the neural network and machine learning methods that have been widely used in anomaly detection in recent years, most of them have high requirements for training samples and cannot be well applied On the electricity consumption dataset with missing sample labels

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