Anomaly Detection Mechanism In Smart Meters

single phase wifi energy meter Data analysis first involves standardizing the collected historical data, and then extracting data feature vectors through principal component decomposition or high-dimensional distribution modeling techniques. Mapping these vectors to a specific probability model space allows us to understand the distribution boundary of 3 phase smart energy meter under normal conditions. By comparing the deviation between the actual collected values and the model's inference results, we can determine the degree of anomaly of single phase smart meter and trigger further investigation procedures.

Deploying edge inference models within the power IoT architecture enables direct feature extraction and pattern evaluation at terminal nodes, making the data collection and analysis process wifi smart energy meter closer to the data source. This approach, by compressing the learning model size to adapt to terminal devices with limited hardware resources, supports real-time identification of dynamically sampled data, improving the system's response speed to sudden events.