Simultaneous disaggregation of multiple appliances based on non-intrusive load monitoring

Dong, Hua; Huang, Fanqi; Chen, Wutao

doi:10.1016/j.epsr.2020.106887

Cited by 23 publications

(10 citation statements)

References 21 publications

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“…Note, though, that the method does not intend to detect malfunctions as internal short circuits. In the future, we will improve the methodology by incorporating a good performance disaggregation algorithm so that the system status set can be obtained from only the smart meter information, which has received great research attention in the last few years-e.g., [27,[29][30][31]. Further, we will improve the classifier formulation, by giving each neuron a label and using the classifier as a real-time monitor.…”

Section: Discussionmentioning

confidence: 99%

“…If plugs are directly used for obtaining this data, then the method is clearly intrusive and it is unfeasible to become a scalable solution. However, we anticipate that in more realistic scenarios, an additional block will be included for data disaggregation, which accurately maps the utilization of the appliances (e.g., [27]). It is also worth mentioning that the proposed methodology does work with different time granularities, considering that it allows for detecting the activity of the different appliances.…”

Section: Methodsmentioning

confidence: 99%

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Noninvasive Detection of Appliance Utilization Patterns in Residential Electricity Demand

et al. 2021

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Smart meters with automatic meter reading functionalities are becoming popular across the world. As a result, load measurements at various sampling frequencies are now available. Several methods have been proposed to infer device usage characteristics from household load measurements. However, many techniques are based on highly intensive computations that incur heavy computational costs; moreover, they often rely on private household information. In this paper, we propose a technique for the detection of appliance utilization patterns using low-computational-cost algorithms that do not require any information about households. Appliance utilization patterns are identified only from the system status behavior, represented by large system status datasets, by using dimensionality reduction and clustering algorithms. Principal component analysis, k-means, and the elbow method are used to define the clusters, and the minimum spanning tree is used to visualize the results that show the appearance of utilization patterns. Self organizing maps are used to create a system status classifier. We applied our techniques to two public datasets from two different countries, the United Kingdom (UK-DALE) and the US (REDD), with different usage patterns. The proposed clustering techniques enable effective demand-side management, while the system status classifier can detect appliance malfunctions only through system status analyses.

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Noninvasive Detection of Appliance Utilization Patterns in Residential Electricity Demand

et al. 2021

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“…Recently, numbers of researchers proposed IoT-based household electricity energy monitoring and control system (23,(25)(26)(27). This system consists of a microcontroller, Wi-Fi module and sensors.…”

Section: Energy Monitoring and Control In Smart Gridmentioning

confidence: 99%

Simulation and Modelling of Electricity Usage Control and Monitoring System using ThingSpeak

2021

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Renewable energy technology is growing fast especially photovoltaic (PV) system to move the conventional electricity generation and distribution towards smart grid. However, similar to monthly electricity bill, the PV energy producers can only monitor their energy PV generation once a month. Any malfuntion in PV system components may reduce the performance of the system without notice. Thus, developing a real-time monitoring system of PV production is very crucial for early detection. In addition, electricity consumption is also important to be monitored more frequently to increase energy savings awareness among consumers. Hardware based Internet-of-Thing (IoT) monitoring and control system is widely used. However, the implementation of the actual smart grid system is high in cost. Thus, simulation and modelling of the system is important to see the capability of the actual system before being employed. Since the smart grid and its components are usually modeled using MATLAB/Simulink, the communication between MATLAB/Simulink, IoT platform such as ThingSpeak and mobile application is crucial to be explored to gain a better understanding of the features of the smart grid. To achieve the objectives, there are five main steps which are simulation of grid-connected photovoltaic (PV) system to generate data to be monitored and controlled using HOMER software, then, development of monitoring on ThingSpeak and mobile application using MIT App Inventor 2. Next, the control system is developed on mobile application and the communication on how data are transferred between all the softwares are set up. The results show that all the seletected parameters can be monitored in real-time successfully. The developed mobile application can be used to control the MATLAB/Simulink in two modes. During automatic mode, ThingSpeak controls the MATLAB/Simulink by giving a zero signal (OFF) if load demand is less than the power generated by PV and a one signal (ON) if the load demand is greater than PV power. During manual mode, consumer can send ON or OFF signal to MATLAB/Simulink via the mobile application unconditionally. It is hoped that the proposed system will bring many benefits in modeling a complete smart grid system in MATLAB/Simulink.

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“…Recently, the attention to load identification is also drawn to simultaneous identification problems for multiple electrical loads exist. Hua et al [31] proposed an event-based NILM method, in which the mixed linear integer programming model was developed to achieve the simultaneous disaggregation of multiple appliances. Liu et al [32] proposed a deep dictionary learning model and an adaptivewindow-based detection approach to conducting the NILM with simultaneous switching operations.…”

Section: Introductionmentioning

confidence: 99%

Simultaneous Load Identification Method Based on Hybrid Features and Genetic Algorithm for Nonintrusive Load Monitoring

Yi¹,

Wang²,

Liu³

2022

Mathematical Problems in Engineering

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Nonintrusive load monitoring (NILM) is a widely accepted technology to conduct load monitoring. Many effective methods have been established to make NILM more practical. However, the focus of current methods is mainly on the identification accuracy and efficiency of single load under the individual appliance operated independently, which have limited support for the identification problem under multiple appliances operated simultaneously. Therefore, a simultaneous identification method is proposed to efficiently identify the total load under multiple appliances operated simultaneously in this paper. The proposed identification method mainly consists of three parts: hybrid features extraction, simultaneous identification optimization model construction, and frequency-weighting-factor-based genetic algorithm (FWF-GA). Firstly, the hybrid feature model, which integrates the features of active power, reactive power, and harmonic magnitude, is constructed by hybrid features extraction. Secondly, the simultaneous identification optimization model is constructed by employing the features of active and reactive power. Thirdly, the developed FWF-GA is used to solve the simultaneous identification optimization problem. In FWF-GA, the relative errors of active power, reactive power, and the frequency-weighting factor of harmonic magnitude are used to evaluate the fitness of an individual. Finally, a NILM practice to identify household appliances is used to demonstrate the validity of the proposed method.

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Simultaneous disaggregation of multiple appliances based on non-intrusive load monitoring

Cited by 23 publications

References 21 publications

Noninvasive Detection of Appliance Utilization Patterns in Residential Electricity Demand

Noninvasive Detection of Appliance Utilization Patterns in Residential Electricity Demand

Simulation and Modelling of Electricity Usage Control and Monitoring System using ThingSpeak

Simultaneous Load Identification Method Based on Hybrid Features and Genetic Algorithm for Nonintrusive Load Monitoring

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