Nonintrusive load monitoring (NILM) is performed by monitoring the total electricity consumption data from the customer’s meter and decomposing it with a load decomposition algorithm to get real-time electricity consumption data for individual appliances. Nonintrusive load decomposition is a single-channel blind source separation problem, which is difficult to implement because of the inherently unidentifiable problem. Recent studies have shown that deep learning is gradually becoming the mainstream approach to solve the NILM problem owing to the availability of data, computing power, and deep network training algorithm models. Among them, the sequence-to-point (seq2point) (Zhang) load decomposition model achieves state-of-the-art prediction because it concentrates on the representation ability of the network at one point. In this article, we optimize the structure of the network model based on the seq2point model, use a temporal convolutional network (TCN) with a more flexible perceptual field of view to train the load decomposition model, and replace the traditional activation function Relu with the activation function GELU. Finally, we test the algorithm in the public dataset UK-DALE and select the mean absolute error and F-score as the main evaluation indexes of the algorithm performance. Comparing the decomposition results of the two algorithms for the same period dataset, the results show that the temporal convolutional network model has substantially improved the performance index of load decomposition while reducing the model parameters.

In the virtual power plant architecture based on edge computing, short-term photovoltaic power prediction is carried out by using multi-source data of photovoltaic power plants stored in photovoltaic intelligent edge terminal (PVIET), which can achieve efficient dispatching and management of renewable energy by the control and coordination center, thus ensuring the safe and stable operation of the power grid. Therefore, this paper proposes a short-term photovoltaic power prediction method that combines improved grey relation analysis (IGRA), efficient channel attention module (ECANet), and temporal convolutional network (TCN). The IGRA algorithm can fully explore the correlation between predicted days and historical days using meteorological environmental data from multiple sources, while the deep TCN model combined with ECANet can effectively avoid the drawbacks of traditional photovoltaic power prediction models. Finally, using the real case of Alice Spring in Australia, the proposed method (IGRA-ECA-TCN) is compared with other hybrid models in different seasons and weather conditions, and the mean absolute error (MAE), root mean square error (RMSE) and coefficient of determination (R2) are selected as the evaluation indicators of algorithm performance. The results indicate that IGRA-ECA-TCN has stronger adaptability and higher accuracy.

A lightweight rotational object detection algorithm, R-YOLOv5, is proposed to address the limitations of traditional object detection algorithms that do not consider the diversity of vehicle scales in drone images and fail to obtain information on rotation angles. The proposed algorithm incorporated an angle prediction branch and introduced a circular smooth label (CSL) angle classification method to make it suitable for detection scenarios based on rotational boxes. A cascaded Swin Transformer block (STrB) is used to reduce computational complexity during feature fusion in the backbone network, further enhancing semantic information and global perception capabilities for small objects. A feature enhancement attention module (FEAM) is proposed to improve the utilization of detailed information through local feature self-supervision. An adaptive spatial feature fusion structure (ASFF) is introduced, which employs features extracted from different levels of the backbone network to perform multi-scale feature fusion. The experimental results show that the detection accuracy reaches 84.91% on the Drone-Vehicle dataset and 90.23% on the UCAS-AOD remote sensing dataset. The lightweight model has a parameter count of only 2.02 million and can achieve 82.6 FPS for high-resolution images, which is significantly better than existing lightweight models and more suitable for real-time detection of rotating vehicles in dense scenes, making it suitable for deployment on a large majority of embedded platforms.

With the rapid development of Industrial Internet of Things (IIoT) technology, video surveillance devices and video data in IIoT environments are massively growing and increasingly important. Deploying rapidly evolving deep learning-based object detection algorithms in IIoT can improve the efficiency of video data utilization and increase the automation and intelligence of the IIoT. Facing the transmission latency problem of massive video data, the algorithms are better deployed in edge devices. However, due to the large size and high computing power requirements of existing object detection algorithms, it is difficult to deploy them on edge devices with limited performance. Therefore, to solve this problem, we propose a smaller and less computationally intensive object detection algorithm, PG-YOLO. We replaced all the convolutional modules of YOLOV5 with low computational ghost modules and streamlined the backbone network to improve the network performance. Then, we propose a better pruning algorithm to compress the network, and finally improve the accuracy by distillation to obtain PG-YOLO. PG-YOLO is smaller and faster and can be deployed in edge devices. In addition, we conducted experimental validation on the SHWD dataset. The experimental results show that PG-YOLO can compress the volume of the model by 9 times compared with YOLOv5s, and the compressed detection accuracy reaches 0.934, with only 0.1% loss of accuracy. Also, the time required for inference is reduced by 32.7%, and the inference speed is improved by 10 FPS. Compared with other object detection algorithms, PG-YOLO also has advantages.

Corona Virus Disease 2019 (COVID-19) poses a significant threat to human health and safety. As the core of the prevention and control of COVID-19, the health and safety of medical and nursing personnel are extremely important, and the standardized use of medical personal protective equipment can effectively prevent cross-infection. Due to the existence of severe occlusion and overlap, traditional image processing methods struggle to meet the demand for real-time detection. To address these problems, we propose the ME-YOLO model, which is an improved model based on the one-stage detector. To improve the feature extraction ability of the backbone network, we propose a feature fusion module (FFM) merged with the C3 module, named C3_FFM. To fully retain the semantic information and global features of the up-sampled feature map, we propose an up-sampling enhancement module (USEM). Furthermore, to achieve high-accuracy localization, we use EIoU as the loss function of the border regression. The experimental results demonstrate that ME-YOLO can better balance performance (97.2% mAP) and efficiency (53 FPS), meeting the requirements of real-time detection.