Recent years have witnessed the widespread popularity of Internet of things (IoT). By providing sufficient data for model training and inference, IoT has promoted the development of artificial intelligence (AI) to a great extent. Under this background and trend, the traditional cloud computing model may nevertheless encounter many problems in independently tackling the massive data generated by IoT and meeting corresponding practical needs. In response, a new computing model called edge computing (EC) has drawn extensive attention from both industry and academia. With the continuous deepening of the research on EC, however, scholars have found that traditional (non-AI) methods have their limitations in enhancing the performance of EC. Seeing the successful application of AI in various fields, EC researchers start to set their sights on AI, especially from a perspective of machine learning, a branch of AI that has gained increased popularity in the past decades. In this article, we first explain the formal definition of EC and the reasons why EC has become a favorable computing model. Then, we discuss the problems of interest in EC. We summarize the traditional solutions and hightlight their limitations. By explaining the research results of using AI to optimize EC and applying AI to other fields under the EC architecture, this article can serve as a guide to explore new research ideas in these two aspects while enjoying the mutually beneficial relationship between AI and EC.

Condition monitoring and fault diagnosis of photovoltaic modules are essential to ensure the efficient and reliable operation of large-scale photovoltaic plants. This article presents an algorithmic solution for the rapid and sensitive detection of photovoltaic modules with multiple visible defects by an image analyzing apparatus mounted onto an unmanned aerial vehicle. The proposed solution is composed of three stages to efficiently and accurately analyze various forms of module defects. First, the Kirsch operator is employed to identify the anomalous regions, which can significantly reduce the computational complexity, and error rate. Afterward, a deep convolutional neural network is adopted to extract defect features. Finally, a multiple classification support vector machine is developed to facilitate the defects detection decision-making. The proposed solution is extensively evaluated by the comprehensive dataset collected from real-world solar photovoltaic plants. The experimental results clearly demonstrate the effectiveness of our solution for photovoltaic modules diagnosis with multiple visible defects.

From the perspective of energy providers, accurate short-term load forecasting plays a significant role in the energy generation plan, efficient energy distribution process and electricity price strategy optimisation. However, it is hard to achieve a satisfactory result because the historical data is irregular, non-smooth, non-linear and noisy. To handle these challenges, in this work, we introduce a novel model based on the Transformer network to provide an accurate day-ahead load forecasting service. Our model contains a similar day selection approach involving the LightGBM and k-means algorithms. Compared to the traditional RNN-based model, our proposed model can avoid falling into the local minimum and outperforming the global search. To evaluate the performance of our proposed model, we set up a series of simulation experiments based on the energy consumption data in Australia. The performance of our model has an average MAPE (mean absolute percentage error) of 1.13, where RNN is 4.18, and LSTM is 1.93.

The paradigm of the Internet of Things (IoT) and cloud-edge computing plays a significant role in future smart grids. The data-driven solution integrating the artificial intelligence functionalities brings novel methods to address the nontrivial task of economic dispatch in microgrids in the presence of uncertainties of renewable generations and loads. This article proposes a learning-based decision-making framework for the economic energy dispatch of an islanding microgrid based on the cloud-edge computing architecture. Cloud resources are utilized to solve the optimal dispatch decision sequences over historical operating patterns. It can be considered as a sample labeling process for the supervised training that can implement the complex mapping of input–output space through an advanced machine learning model. Then, the well-trained model can be adopted locally at edge computing devices keeping the long-term parameters unchanged for implement the real-time microgrid energy dispatch. The key benefit of the proposed solution is that it effectively avoids the prediction of multiple stochastic variables and the design of sophisticated regulation strategies or reward policy functions for real-time dispatch. The solution is extensively assessed through simulation experiments by the use of real data measurements for a set of operational scenarios and the numerical results validate the effectiveness and benefit of the proposed algorithmic solution.

Random transmission delay and packet loss may cause load frequency control (LFC) performance degradation or even instability in interconnected power systems. Traditional robust methods mainly focus on guaranteeing the asymptotic stability with pre-estimate a maximum delay case. As a result, the designed controller cannot satisfy the actual operational requirements completely since the improper network performance estimation. In this paper, based on jump system theory, the state-space model for LFC system with the different transmission delay is established as the jumping decision variable, which describes the possible dynamics of the networked LFC system with the bounded time delay. Secondly, in order to avoid the defect of conservative or optimistic design caused by imprecise pre-set delay, the upper boundary of the transmission delay in the power communication system is calculated by deterministic network theory. Furthermore, the asymptotic stability constraints in bilinear matrix inequality forms are deduced via construction of a Lyapunov function. Moreover, to reduce the peak value, peak time and setting time of frequency deviations caused by power mismatches, an iterative optimization algorithm is proposed to obtain the optimal feedback matrix. Simulation studies with two-area and five-area interconnected power systems show that the proposed method can avoid optimistic or conservative design effectively while the stability and dynamic performance can be ensured simultaneously.