The ongoing deployment of smart meters and different commercial devices has made electricity disaggregation feasible in buildings and households, based on a single measure of the current and, sometimes, of the voltage. Energy disaggregation is intended to separate the total power consumption into specific appliance loads, which can be achieved by applying Non-Intrusive Load Monitoring (NILM) techniques with a minimum invasion of privacy. NILM techniques are becoming more and more widespread in recent years, as a consequence of the interest companies and consumers have in efficient energy consumption and management. This work presents a detailed review of NILM methods, focusing particularly on recent proposals and their applications, particularly in the areas of Home Energy Management Systems (HEMS) and Ambient Assisted Living (AAL), where the ability to determine the on/off status of certain devices can provide key information for making further decisions. As well as complementing previous reviews on the NILM field and providing a discussion of the applications of NILM in HEMS and AAL, this paper provides guidelines for future research in these topics.

NILM Techniques for Intelligent Home Energy Management and Ambient Assisted Living: A Review

Cyber Physical System (CPS) has provided an outstanding foundation to build advanced industrial systems and applications by integrating innovative functionalities through Internet of Things (IoT) and Web of Things (WoB) to enable connection of the operations of the physical reality with computing and communication infrastructures. A wide range of industrial CPS-based applications have been developed and deployed in Industry 4.0. In order to understand the development of CPS in Industry 4.0, this paper reviews the current research of CPS, key enabling technologies, major CPS applications in industries, and identifies research trends and challenges. A main contribution of this review paper is that it summarizes the current state-of-the-art CPS in Industry 4.0 from Web of Science (WoS) database (including 595 articles) and proposes a potential framework of CPS systematically.

Cyber Physical System (CPS)-Based Industry 4.0: A Survey

With the development and application of advanced technologies such as Cyber Physical System, Internet of Things, Industrial Internet of Things, Artificial Intelligence, Big Data, Cloud Computing, Blockchain, etc., more manufacturing enterprises are transforming to intelligent enterprises. Smart manufacturing systems (SMSs) have become the focus of attention of some countries and manufacturing enterprises. At present, there are some applications of SMSs in different industrial fields. However, there is still a lack of a unified definition of SMSs and a unified analysis of requirements. In order to have a comprehensive understanding of SMSs, this paper summarized the evolution, definition, objectives, functional requirements, business requirements, technical requirements, and components of SMSs. At the same time, it points out the current development status and level. Based on above, an autonomous SMSs model driven by dynamic demand and key performance indicators is proposed. Through the review of this paper, the reference can be provided for the transformation of more manufacturing enterprises from the traditional to the intellectualized ones.

Smart manufacturing systems: state of the art and future trends

Auction-based adaptive sensor activation algorithm for target tracking in wireless sensor networks

The application of EMD-based methods for diagnosis of winding faults in a transformer using transient and steady state currents

Pure harmonics extracting from time-varying power signal based on improved empirical mode decomposition

Wireless sensor networks involve a large number of sensor nodes with limited energy supply, which impacts the behavior of their application. In wireless multimedia sensor networks, sensor nodes are equipped with audio and visual information collection modules. Multimedia contents are ubiquitously retrieved in surveillance applications. To solve the energy problems during target surveillance with wireless multimedia sensor networks, an energy-aware sensor scheduling method is proposed in this paper. Sensor nodes which acquire acoustic signals are deployed randomly in the sensing fields. Target localization is based on the signal energy feature provided by multiple sensor nodes, employing particle swarm optimization (PSO). During the target surveillance procedure, sensor nodes are adaptively grouped in a totally distributed manner. Specially, the target motion information is extracted by a forecasting algorithm, which is based on the hidden Markov model (HMM). The forecasting results are utilized to awaken sensor node in the vicinity of future target position. According to the two properties, signal energy feature and residual energy, the sensor nodes decide whether to participate in target detection separately with a fuzzy control approach. Meanwhile, the local routing scheme of data transmission towards the observer is discussed. Experimental results demonstrate the efficiency of energy-aware scheduling of surveillance in wireless multimedia sensor network, where significant energy saving is achieved by the sensor awakening approach and data transmission paths are calculated with low computational complexity.

https://www.mdpi.com/1424-8220/10/4/3100/pdf

Energy-aware Scheduling of Surveillance in Wireless Multimedia Sensor Networks

Target tracking is an important task addressed in wireless sensor network (WSN), because the resources of WSN are limited. A distributed lightweight particle filter algorithm is proposed to achieve robust target tracking with low resource requirement in the WSN consisting of acoustic sensor nodes. In the proposed algorithm, each sensor node carries out partial particle filter with its local data and the neighbor nodes' data in distributed manner. Then local results of selected sensor nodes are fused to make final decision. To simplify the computation of particle filter, lightweight sampling and resampling schemes are introduced, where a simple range-free algorithm is adopted to restrict potential area of target's location. The experimental results verify that the proposed distributed lightweight particle filter algorithm can effectively achieve target tracking in WSN with low resource consumption. Compared to previous target localization algorithms, such as maximum likelihood estimation and centralized particle filter, the proposed algorithm has outstanding performance in accurate target tracking and low resources consumption.

Distributed lightweight target tracking for wireless sensor networks

Cyber physical systems(CPS) recently emerge as a new technology which can provide promising approaches to demand side management(DSM), an important capability in industrial power systems. Meanwhile, the manufacturing center is a typical industrial power subsystem with dozens of high energy consumption devices which have complex physical dynamics. DSM, integrated with CPS, is an effective methodology for solving energy optimization problems in manufacturing center. This paper presents a prediction-based manufacturing center self-adaptive energy optimization method for demand side management in cyber physical systems. To gain prior knowledge of DSM operating results, a sparse Bayesian learning based componential forecasting method is introduced to predict 24-hour electric load levels for specific industrial areas in China. From this data, a pricing strategy is designed based on short-term load forecasting results. To minimize total energy costs while guaranteeing manufacturing center service quality, an adaptive demand side energy optimization algorithm is presented. The proposed scheme is tested in a machining center energy optimization experiment. An AMI sensing system is then used to measure the demand side energy consumption of the manufacturing center. Based on the data collected from the sensing system, the load prediction-based energy optimization scheme is implemented. By employing both the PSO and the CPSO method, the problem of DSM in the manufacturing center is solved. The results of the experiment show the self-adaptive CPSO energy optimization method enhances optimization by 5% compared with the traditional PSO optimization method.

Prediction-based manufacturing center self-adaptive demand side energy optimization in cyber physical systems

Cyber-physical energy systems, which emerges as the approach for integrating physical layers and control networks, have drawn extensively attention in recent years. Electric load forecasting is believed to be an important issue in CPES for its applications in prices determination and automatic generation control. Conventional deterministic load forecast method have drawbacks to providing information about the probability distribution of the prediction results, which are important for stochastic decision in power systems. This paper explores a hierarchical probabilistic approach for short-term load forecast, which combines sparse Bayesian learning with empirical mode decomposition, in order to obtain a componential forecasting results, as well as the forecasting uncertainty. Mahalanobis distance based similar day weighting is introduced to prune the training data. The numerical testing results illustrate that the proposed approach exhibits better performance in comparison with original SBL model and weighted SBL without componential analysis.

Xinyao Sun

Papers

Pure harmonics extracting from time-varying power signal based on improved empirical mode decomposition

Energy-aware Scheduling of Surveillance in Wireless Multimedia Sensor Networks

Distributed lightweight target tracking for wireless sensor networks

Prediction-based manufacturing center self-adaptive demand side energy optimization in cyber physical systems

Hierarchical sparse learning for load forecasting in cyber-physical energy systems