This tutorial describes how to use DataWorks together with Machine Learning Platform for AI (PAI) to automatically identify users who steal electricity. This ensures that users use electricity in a safe manner.
The traditional methods of identifying electricity theft and metering device failures include regular inspection, regular check of electricity meters, and reporting of electricity theft from users. These methods require manual operations. In addition, these methods are inefficient if you want to identify users who steal electricity or are involved in electricity leakage.
The staff of power supply bureaus, such as those who inspect electricity marketing and who check and meter electricity usage, use the existing automated system for monitor electricity usage online. The system triggers alerts for abnormal electricity usage and provides electricity usage data. The system collects data about abnormal electricity usage, abnormal load, abnormal line loss, and alerts that are reported by terminals and primary sites, and builds models for analyzing the data. This way, relevant staff can identify electricity theft, electricity leakage, and metering device failures in real time. After alerts are triggered, the system builds models for analyzing abnormal electricity usage based on the current, voltage, and load before and after the alert time. This helps identify electricity theft, electricity leakage, and metering device failures.
Information about abnormal electricity usage can be collected by using the traditional methods of identifying electricity theft and electricity leakage. However, due to frequent false positives and false negatives, these methods cannot precisely identify users who steal electricity or are involved in electricity leakage. In addition, experts need to determine the weight of each metric for the model to be built based on their knowledge and experience. This process is subjective.
The existing automated system for metering electricity usage can collect electricity load data, such as the current, voltage, and power data, and alert data that terminals report. Electrical inspection staff can also collect electricity theft and leakage data from the online inspection system or by conducting on-site inspection and enter the data into the system.
Based on the preceding data, DataWorks together with PAI can abstract key features of users who steal electricity or are involved in electricity leakage. In addition, DataWorks together with PAI can also build a model for identifying such users. This way, electricity theft or leakage can be automatically detected. This reduces the inspection workload of electrical inspection staff and ensures normal and secure electricity usage.