In this paper, the real-time non-intrusive load monitoring (NILM) problem with limited measurements, i.e., low sampling rate data, is investigated. NILM is a technique to identify the various types of appliances by analyzing the voltage and current features collected by sensors such as smart outlets. It is one of the most important topics in smart grid management and optimization. Although recent NILM methods developed several general models that can be used for various scenarios, these algorithms either require high-frequency measurement devices or have been limited to use for some specific appliances. A real-time intelligent NILM algorithm that is able to be transferred among different appliances with low-frequency data is still lacking and desperately needed. In this paper, a novel online learning-based intelligent NILM algorithm has been developed that can online infer a variety of appliances using a transferred model with limited measurements. Specifically, the developed algorithm integrates the emerging transfer learning technique along with deep neural networks. Two neural networks are used in two different stages, i.e., 1) The long short-term memory (LSTM) neural network to extract the lower level spatial and temporal features from the grayscale image generated by the measurements, 2) The probabilistic neural network (PNN) to classify the appliance type as well as transfer between appliances. Finally, the algorithm is implemented into practical smart outlet hardware to demonstrate its effectiveness.

In this article, a decentralized optimal tracking control problem has been studied for a large-scale autonomous vehicle system with heterogeneous system dynamics. Due to the ultralarge number of agents, the notorious “curse of dimension” problem as well as the unrealistic assumption of the existence of reliable very large-scale communication links in uncertain environments have challenged the traditional multiagent system (MAS) algorithms for decades. The emerging mean-field game (MFG) theory has recently been widely adopted to generate a decentralized control method that deals with those challenges by encoding the large scale MASs’ information into a novel time-varying probability density functions (PDF) which can be obtained locally. However, the traditional MFG methods assume all agents are homogeneous, which is unrealistic in practical industrial applications, e.g., Internet of Things (IoTs), and so on. Therefore, a novel mean-field Stackelberg game (MFSG) is formulated based on the Stackelberg game, where all the agents have been classified as two different categories where one major leader’s decision dominates the other minor agents. Moreover, a hierarchical structure that treats all minor agents as a mean-field group is developed to tackle the assumption of homogeneous agents. Then, the actor-actor–critic–critic-mass ( $A^{2}C^{2}M$ ) algorithm with five neural networks is designed to learn the optimal policies by solving the MFSG. The Lyapunov theory is utilized to prove the convergence of $A^{2}C^{2}M$ neural networks and the closed-loop system’s stability. Finally, a series of numerical simulations are conducted to demonstrate the effectiveness of the developed method.

In this article, a decentralized adaptive optimal controller based on the emerging mean-field game (MFG) and self-organizing neural networks (NNs) has been developed for multiagent systems (MASs) with a large population and uncertain dynamics. This design can effectively break the ``curse of dimensionality'' and reduce the computational complexity by appropriately integrating emerging MFG theory with self-organizing NNs-based reinforcement learning techniques. First, the decentralized optimal control for massive MASs has been formulated into an MFG. To unfold the MFG, the coupled Hamilton-Jacobian-Bellman (HJB) equation and Fokker-Planck-Kolmogorov (FPK) equation needed to be solved simultaneously, which is challenging in real time. Therefore, a novel actor-critic-mass (ACM) structure has been developed along with self-organizing NNs subsequently. In the developed ACM structure, each agent has three NNs, including: 1) mass NN learning the mass MAS's overall behavior via online estimating the solution of the FPK equation; 2) critic NN obtaining the optimal cost function through learning the HJB equation solution along with time; and 3) actor NN estimating the decentralized optimal control by using the critic and mass NNs along with the optimal control theory. To reduce the NNs' computational complexity, a self-organizing NN has been adopted and integrated into a developed ACM structure that can adjust the NNs' architecture based on the NNs' learning performance and the computation cost. Finally, numerical simulation has been provided to demonstrate the effectiveness of the developed schemes.

This article studies the tracking control problem with a large-scale group of agents. Unlike traditional control techniques used in multiagent systems (MASs), a new type of intelligent design is needed to handle the intractable “Curse of Dimensionality” caused by the extremely large number of agents. To address this challenge, the mean field game (MFG) theory has been embedded into reinforcement learning to advance intelligent tracking control with large-scale MAS. Specifically, MFG-based control can calculate the optimal strategy based on one unified fix-dimension probability density function (pdf) instead of high-dimensional large-scale MAS information collected from individual agents. Moreover, the approximate dynamic programming technique is adopted to generate a new type of MFG-based algorithm. Each agent has three neural networks (NNs) to approximate the solution of the mean field type control. In addition to the algorithm development, the performance of the NNs is also analyzed using the Lyapunov method. Finally, the linear and nonlinear tracking control simulations are given to evaluate the algorithm’s performance.

Non-intrusive load monitoring (NILM) is a critical technique for advanced smart grid management due to the convenience of monitoring and analysing individual appliances’ power consumption in a non-intrusive fashion. Inspired by emerging machine learning technologies, many recent non-intrusive load monitoring studies have adopted artificial neural networks (ANN) to disaggregate appliances’ power from the non-intrusive sensors’ measurements. However, back-propagation ANNs have a very limit ability to disaggregate appliances caused by the great training time and uncertainty of convergence, which are critical flaws for low-cost devices. In this paper, a novel self-organizing probabilistic neural network (SPNN)-based non-intrusive load monitoring algorithm has been developed specifically for low-cost residential measuring devices. The proposed SPNN has been designed to estimate the probability density function classifying the different types of appliances. Compared to back-propagation ANNs, the SPNN requires less iterative synaptic weights update and provides guaranteed convergence. Meanwhile, the novel SPNN has less space complexity when compared with conventional PNNs by the self-organizing mechanism which automatically edits the neuron numbers. These advantages make the algorithm especially favourable to low-cost residential NILM devices. The effectiveness of the proposed algorithm is demonstrated through numerical simulation by using the public REDD dataset. Performance comparisons with well-known benchmark algorithms have also been provided in the experiment section.

As the evolution of smart grids accelerates, distributed energy resources (DERs) emerge as key elements in the transformation of global energy systems. However, the integration of these technologies introduces significant cybersecurity vulnerabilities, notably false data injection (FDI) and a direct load-altering attack (DLAA). Traditional load-altering attacks require a huge attack load and, thus, are not practical to implement. In contrast, in modern DER environments where households become “prosumers” with high-power energy generation, the implications of such attacks are substantially amplified. This paper considers a hybrid cyberattack that includes both FDI and a DLAA, and presents a hierarchical, optimal load adjustment framework that addresses these security concerns. A centralized optimizer first calculates the ideal load-shedding strategies for each substation, which are then securely broadcast to households. To address the complexities at the individual household level, we introduce a novel reinforcement learning algorithm termed Mean Field Deep Deterministic Policy Gradients (MF-DDPG). This algorithm employs mean-field game theory to enable decentrally coordinated decision-making among each household, making it particularly effective in zero-trust scenarios. Through this multifaceted approach, we offer a robust countermeasure against load-altering attacks, thereby enhancing the resilience and stability of advanced smart grids.