How can the smart meter management system achieve remote meter reading?

The smart meter management system integrates IoT, communication technology, and cloud computing to achieve the core process of remote meter reading, which can be summarized into four links: data collection, transmission, processing, and application. The specific implementation method is as follows:

1、 Data collection: hardware support for smart meters

High precision measurement module

Smart meters are equipped with dedicated metering chips (such as ADI's ADE7880) that can collect real-time parameters such as voltage, current, and power factor, and calculate electricity consumption (active/reactive) through algorithms with an accuracy of 0.5S level (in compliance with IEC 62053 standard).

Communication interface integration

The electricity meter needs to be equipped with at least one communication module, and common types include:

Narrowband Internet of Things (NB IoT): Wide coverage, low power consumption, suitable for remote areas (such as rural power grids);

4G/5G: High speed, supporting real-time data transmission (such as peak electricity consumption monitoring in industrial parks);

LoRa: Low power consumption, long-distance, suitable for distributed photovoltaic power plants;

RS485/M-Bus: wired transmission, high stability, commonly used in building distribution boxes.

2、 Data Transmission: Multi Protocol Adaptation and Secure Encryption

Standardization of communication protocols

The system needs to support DL/T 645-2007 (China Electric Power Standard), IEC 62056 (International Standard) or Modbus protocol to ensure compatibility of meter data from different manufacturers. For example, State Grid concentrators typically require support for both DL/T 645 and MQTT protocols.

Encryption transmission mechanism

The data is encrypted using AES-128 or SM4 algorithms, combined with dynamic key management to prevent man in the middle attacks. For example, Southern Power Grid adopts the "one meter, one secret" strategy, where each meter independently generates an encryption key that rotates every 24 hours.

Network topology optimization

Star structure: The electricity meter is directly connected to the main station (suitable for small-scale scenarios, such as community meter reading);

Cascade structure: Summarize data from multiple electricity meters through a concentrator (for example, 500 electricity meters in a substation are uploaded through one concentrator);

Mesh network: self-organizing network between electricity meters, expanding coverage through relay nodes (suitable for mountainous areas or underground utility tunnels).

3、 Data Processing: Cloud Analysis and Storage

Edge computing preprocessing

Concentrators or gateways perform preliminary cleaning (such as removing outliers), compression (reducing transmission volume by 30% -50%), and protocol conversion on raw data to alleviate the pressure on the main station. For example, Huawei's eLTE IoT solution can complete data aggregation on the gateway side and only upload key indicators.

Cloud platform architecture

Data layer: using time-series databases (such as InfluxDB) to store historical data, supporting writing of millions of data points per second;

Analysis layer: Predicting electricity load through machine learning models (such as LSTM neural network predicting future 24-hour electricity consumption);

Application layer: Provides API interfaces to interface with marketing systems, energy efficiency management platforms, etc.

4、 Application scenarios and functional extensions

Automatic meter reading and bill generation

The system automatically collects data according to a set cycle (such as midnight every day) and generates electricity bills based on electricity pricing policies. For example, Shanghai Electric Power implements "daily settlement" through smart meters, and users can view electricity consumption details in real time.

Abnormal electricity consumption monitoring

By setting a threshold (such as a sudden increase of 50% in single-phase current) or pattern recognition (such as continuous low-power operation at night, which may be considered electricity theft), an alarm is triggered and pushed to the operation and maintenance personnel. The pilot project of a certain province's power grid shows that this function has increased the efficiency of electricity theft investigation by 70%.

Demand response and energy efficiency management

During peak electricity usage, the system can remotely issue instructions to adjust meter parameters (such as limiting air conditioning load) or push energy-saving suggestions to users. For example, German company E.ON guides users to participate in virtual power plants through smart meters, reducing peak load on the power grid by 15%.

5、 Typical case: State Grid's "New Generation Electricity Information Collection System"

Scale: Covering 470 million electricity meters, with an average daily data collection of over 100TB;

Technology: Adopting the "cloud management edge end" architecture, 5G+NB IoT hybrid networking, data transmission latency<1 second;

Effect: The success rate of meter reading has increased from 98.5% to 99.99%, and the cost of manual inspection has been reduced by 60%.

Development trend: With the popularization of 5G Advanced and AIoT technologies, smart meters will integrate more sensors (such as temperature and humidity) in the future, evolving from "measuring tools" to "energy routers" and supporting distributed energy access and carbon trading markets.