Non-Intrusive Load Monitoring (NILM) has become a research boom due to its energy-saving benefits as the smart grid develops and the greenhouse effect intensifies. However, existing NILM models may perform poorly in the target domains collected from different sources, or those containing unseen appliance types, blocking the rollout of NILM. In contrast to the methods that update the trained model on the unseen data to handle domain shift or heterogeneous label space, this paper proposes a novel generalizable NILM method based on metric-based meta-learning and supervised pre-training. It beforehand learns the cross-domain meta-knowledge through meta-training to eliminate the need for parameter updates in the target domain. Moreover, this method is specifically designed for practical deployments at the edge where both labeled instances and computation resources are limited. The proposed method transforms the appliance current into a metric vector via lightweight 1D convolution and then uses the relation module to propagate the labels via similarity. The experimental results on three public datasets and the proposed dataset demonstrate that the proposed method can achieve high generalization performance in different datasets, user’s houses, appliance brands, and appliance types.

Non-intrusive load monitoring (NILM) provides a cost-effective solution for smart services across numerous appliances by inferring appliance-level information from mains electrical measurements. With the rapid growth in appliance diversity, continual learning that adapts to new appliances while retaining knowledge of previously learned appliances is of great interest. However, existing methods can handle only a few new appliance types and suffer from high computational complexity and data leakage risks. Therefore, an analytic continual learning-based (ACL) NILM method is proposed. The method employs a lightweight model that is constructed with dual output branches using depthwise separable convolution for load identification and novelty detection. Meanwhile, a supervised contrastive learning strategy is applied to enhance the distinctiveness among appliance types in the feature extraction module. When the novelty detection branch determines that new data need to be learned, the parameters of the dual branches are updated by recursively calculating the analytical solution using only the current data. Experiments on four public datasets demonstrate superior performance on pre-collected appliances with lower computational effort. It also significantly outperforms existing methods during the continual learning process, as the number of appliance types increases to 56. The practicality of the proposed method is validated through a real-world application on an STM32F407-based smart socket.

Non-Intrusive Load Monitoring (NILM), a technique that extracts appliance-level energy consumption information through analysis of aggregated electrical measurements, has become essential for smart grids and energy management applications. Given the increasing diversification of electrical appliances, real-time NILM systems require continuous integration of knowledge from new client-side appliance data to maintain monitoring effectiveness. However, current methods face challenges with inter-client knowledge conflicts and catastrophic forgetting in distributed multi-client continual learning scenarios. This study addresses these challenges by proposing a confidence-based collaborative distributed continual learning framework for NILM. A lightweight layer-wise dual-supervised autoencoder (LWDSAE) model is initially designed for smart meter deployment, supporting both load identification and confidence-based collaboration tasks. Clients with learning capabilities generate new models through one-time fine-tuning, facilitating collaboration among client models and enhancing individual client load identification performance via a confidence judgment method based on signal reconstruction deviations. Furthermore, an anomaly sample detection-driven model portfolios update method is developed to assist each client in maintaining optimal local performance under model quantity constraints. Comprehensive evaluations on two public datasets and real-world applications demonstrate that the framework achieves sustained performance improvements in distributed continual learning scenarios, consistently outperforming state-of-the-art methods.

Faulty electrical wiring, overloading, and especially hazardous appliances are now threatening more and more peoples lives. Consumer electronics, particularly smart home devices such as smart sockets with load monitoring capabilities are gaining popularity, since they are expected to control the risks of electricity consumption based on appliance-level information. However, their timeliness and practicality remain a challenging issue. As a result, this paper presents an edge-device coordination (EDC) approach for real-time load monitoring that fully shares and exploits idle computing resources in edge-device systems. The complicated electrical safety protection tasks of smart home devices are first modeled in detail. Next, a novel edge-device coordination framework is proposed, building on a refined resource manager and a task scheduler that handles uncertainty with lightweight deep reinforcement learning. Furthermore, an electrical safety protection system that includes smart socket with communication, sensing, and control capabilities, as well as a Qt-based edge server are designed for experiments. In the simulation of a house, the task completion rate increases by 17.1%, while the efficiency improves by 3.28% with the proposed EDC approach. We further validate the scalability and robustness of EDC approach under varying device numbers and network conditions, and demonstrate its superiority over state-of-the-art methods.

Under the evolving modern power system, user-side load structures and consumption behaviors are becoming increasingly diversified, while grid–load interactions are growing more frequent. These changes pose new challenges for load characteristic analysis amid the energy transition. The advancement of metering automation has generated massive power consumption data, offering rich insights for load modeling and applications. This paper presents a comprehensive review of intelligent analysis and applications of load characteristics in new power systems. It begins by outlining a systematic framework for load analysis and summarizing relevant public datasets. Then, it synthesizes existing research from three perspectives: load feature analysis and user profiling, interactive aggregation behaviors under market mechanisms, and practical applications. Finally, this paper discusses platform development practices for big data and highlights future research challenges and directions.