# A Review of Edge Computing Technology and Its Applications in Power Systems
## 5 Discussion and Future Opportunities
### Grid Edge Intelligence  
At present, machine learning and AI are popular topics. As is well known, machine learning is a subset of AI and is a highly efficient approach for forecasting and decision-making. For power system management, it is important to keep the system operating stably and ensure grid resiliency. Machine learning, as a highly successful mathematical method, has great potential to satisfy these requirements well.  
However, to pursue higher accuracy with machine learning models, a large amount of training and verification is required. Three general methods to resolve this conflict are improving the hardware of edge devices, optimizing machine learning models, and partitioning the models deployed on the end devices at the grid edge. For hardware improvement, the basic method is to add chips that can accelerate the training speed into the edge devices. Multi-core processing systems, multiprocessors, or even graphics processing units (GPUs) can be used in edge devices to accelerate the training speed. For partitioning the models on the edge devices, the independent model can be deployed in all edge devices. Another architecture is where edge devices cooperate with local edge servers to handle data processing and model training. High-performance computing (HPC) can also be introduced into edge intelligence and offer opportunities [124]. HPC is an ideal tool that can support real-time processing of vast datasets. Furthermore, this computational strength enables edge devices and edge servers to run scalable and complex machine learning algorithms. Although most systems and compilers running on smart devices or edge servers can provide parallel processing, the machine learning algorithms that can be executed in parallel still need to be developed with HPC technologies. With the integration between HPC and machine learning, the devices at the grid edge can complete more complex tasks, such as data preprocessing and model training.  
With the development of modern power systems and energy resources, the power grid infrastructure encounters more and more uncertainties, such as erratic load [125]. It is important for algorithms used in power systems to deal with massive real-time uncertain variables. The power system is a huge and complex system with a large  
Figure 6: The number of reviewed papers over the years.  
number of nonlinear variables and uncertainties. Machine learning techniques can be used to propose models for uncertainty quantification. For example, machine learning regressions are one of the effective methods contributing to the development of models for uncertainty quantification.  
There are also many other opportunities for machine learning to integrate into the grid edge. Implementing machine learning or deep learning-based fault detection in embedded edge devices can significantly augment the reliability of power systems. Cybersecurity of the grid edge has arisen due to the sensitive data stored and transmitted at the edge. Machine learning and AI can benefit the identifying and responding to potential threats.