# A Review of Edge Computing Technology and Its Applications in Power Systems
## 4 Review of Edge Computing in Power Systems
### Smart Meters Management  
Energy resources play a crucial role in the development of a country and its economy. A stable energy source can provide significant benefits. To achieve this goal, governments have been making efforts to build and deploy smart grid infrastructures in recent years. As an essential device to measure and record the energy consumption of the customer side, smart meters need to be optimized for more efficient and stable power systems. This subsection presents the state-of-the-art edge computing-based applications in smart grids. In smart grids, smart meters can help to detect anomalies in real-time quickly and effectively. However, sending data within a short time slice is not normal because of the bottleneck of the communication network and storage media. [PERSON] et al. [95] proposed prediction techniques by combining deep neural network (DNN), support vector regression (SVR), and k-nearest neighbors (KNN). To predict anomalies in the next few weeks, the timestep is set as one day. This can help users to check their usage and decide whether to prepare sufficient resources or not. The proposed techniques also involve Edge Meter Data Management System (MDMS) and Cloud-MDMS in model training. Smart meters are typical end devices that can collect and send energy data. The usage of smart meters can establish a close relationship between smart grids and cyber-physical systems. With the development of the smart grid, an increasing number of smart meters have been installed. The malfunction detection of large-scale meters has become an issue. [PERSON] et al. [96] proposed a meter error estimation method and studied a low-voltage energy system model. The proposed method includes a decision tree to sort data with different loss levels. It also generates data clusters with different energy usage behavior. Then, a data matrix can be established, and a recursive algorithm is used to calculate the equation and estimate the meter error. By comparing the regulation threshold and the meter error, the malfunctioning meter can be detected. Furthermore, this approach can be applied in an edge computing environment. [PERSON] et al. [97] focused on transferring smart meter data from consumers to the operator within the given time with minimum energy consumption. The authors proposed a smart metering system using edge computing in Long Range (LoRa). All devices in a single house are connected to a smart meter. The edge device and the LoRa node are responsible for processing and transferring the data collected by smart meters. The energy multivariate time series are considered as the energy consumption of various electric devices. At first, the system uses a deep learning-based compression model to reduce the size of the time series. Then, it finds the optimal energy time series that can decrease energy consumption. Finally, an algorithm is adopted to obtain the appropriate spreading factors to transfer the time series from consumers to the operator within the given time. The experiments show that this system can transfer the time series within the given time with higher energy efficiency. In recent years, the computing capabilities of IoT devices have significantly grown, and processing data in the end devices has become possible. The work in [98] presents a smart metering system that can process data analysis on the smart meter where it is produced. This system is based on a multi-core edge computing architecture. The experimental results show that this architecture reduces the processing time. As the penetration of smart meters and the diversity of participants increase, allowing users to obtain and use the information behind the meter will be highly beneficial. To achieve this goal, the concept of an unbundled smart meter (USM) was introduced in the European Nobel Grid project [99]. The USM has two components: a smart metrology meter and a smart meter extension. The smart metrology meter, a sensor module, includes interfaces that enable users to read real-time information.