State of art overview of Non-Intrusive Load Monitoring applications in smart grids

Bucci, G.; Ciancetta, Fabrizio; Fiorucci, Edoardo; Mari, Simone; Fioravanti, Andrea

doi:10.1016/j.measen.2021.100145

Cited by 18 publications

(12 citation statements)

References 16 publications

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“…2) Condition-Based Maintenance Unlike traditional time-based maintenance approaches, condition-based maintenance with NILM focuses on realtime monitoring and analysis of electrical signatures and load patterns associated with machinery and appliances [301]. NILM allows for the detection of anomalies and deviations from normal operating conditions, enabling the early identification of potential issues or deterioration in equipment performance.…”

Section: ) Anomaly Detectionmentioning

confidence: 99%

Non-Intrusive Load Monitoring by Voltage–Current Trajectory Enabled Transfer Learning

Liu

Wang

You

2019

IEEE Trans. Smart Grid

163

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Non-Intrusive Load Monitoring (NILM) is pivotal in today's energy landscape, offering vital solutions for energy conservation and efficient management. Its growing importance in enhancing energy savings and understanding consumer behavior makes it a pivotal technology for addressing global energy challenges. This paper delivers an in-depth review of NILM, highlighting its critical role in smart homes and smart grids. The significant contributions of this study are threefold: Firstly, it compiles a comprehensive global dataset table, providing a valuable tool for researchers and engineers to select appropriate datasets for their NILM studies. Secondly, it categorizes NILM approaches, simplifying the understanding of various algorithms by focusing on technologies, label data requirements, feature usage, and monitoring states. Lastly, by identifying gaps in current NILM research, this work sets a clear direction for future studies, discussing potential areas of innovation.

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Section: ) Anomaly Detectionmentioning

confidence: 99%

Non-Intrusive Load Monitoring by Voltage–Current Trajectory Enabled Transfer Learning

Liu

Wang

You

2019

IEEE Trans. Smart Grid

163

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“…Even the establishment of power sources is not possible practically for some armed posts where they will consume only power (i.e., only load buses). Under this situation a locally established smart grid by interconnecting possible isolated microgrids can solve many problems including power quality, reliability, security, smart operation and uninterrupted supply [3][4][5][6][7][8][9][10][11][12][13][14][15][16]. The location of a smart grid can be selected based on the location where maximum Microgrids are possible for connection through a smart grid.…”

Section: Related Workmentioning

confidence: 99%

Design and control of LSTM-ANN controllers for an efficient energy management system in a smart grid based on hybrid renewable energy sources

Wesley,

Babu,

Kumar

2024

Eng. Res. Express

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Many isolated locations, including hilly areas, remote sites, and military camps, lack feasible access to the main power grid. In these conditions, locally established Microgrids can provide the necessary power supply. However, to meet the demands of these isolated areas, numerous individual Microgrids are required. Although some of these Microgrids might be integratable, geographical constraints may preclude full integration. Under these circumstances, the establishment of a smart grid – integrating multiple Microgrids with a battery management system – can potentially solve many power supply issues. A smart grid control system paired with a centralized battery management system is proposed in this paper. The system considers the use of multiple renewable energy sources at various locations for stable and reliable power generation. The proposed method incorporates a long short-term memory (LSTM)-based artificial neural network (ANN) to ensure a stable, high-quality power supply at different load buses. Additionally, this work introduces an artificial intelligence-based operating system designed to maintain energy management under various conditions. To enhance voltage quality, a 7-level aligned multilevel inverter is incorporated into the system. As compared with PI and Fuzzy controllers, the proposed method with LSTM-ANN controller is improved the power quality under sudden changes in the system which are also presented in results. The effectiveness of the proposed energy management system (EMS) is verified by using a hardware-in the-loop approach with OPAL-RT modules, yielding realistic results.

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“…Table 2 provides a summary of ADLs monitoring techniques based on NILM. An even more detailed review of AAL approaches based on NILM can be found in [5,38].…”

Section: Learning the Usage Patterns Of Appliancesmentioning

confidence: 99%

Ageing Safely in the Digital Era: A New Unobtrusive Activity Monitoring Framework Leveraging on Daily Interactions with Hand-Operated Appliances

Bousbiat

Leitner

Elmenreich

2022

Sensors

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Supporting the elderly to maintain their independence, safety, and well-being through Active Assisted Living (AAL) technologies, is gaining increasing momentum. Recently, Non-intrusive Load Monitoring (NILM) approaches have become the focus of these technologies due to their non-intrusiveness and reduced price. Whilst some research has been carried out in this respect; it still is challenging to design systems considering the heterogeneity and complexity of daily routines. Furthermore, scholars gave little attention to evaluating recent deep NILM models in AAL applications. We suggest a new interactive framework for activity monitoring based on custom user-profiles and deep NILM models to address these gaps. During evaluation, we consider four different deep NILM models. The proposed contribution is further assessed on two households from the REFIT dataset for a period of one year, including the influence of NILM on activity monitoring. To the best of our knowledge, the current study is the first to quantify the error propagated by a NILM model on the performance of an AAL solution. The results achieved are promising, particularly when considering the UNET-NILM model, a multi-task convolutional neural network for load disaggregation, that revealed a deterioration of only 10% in the f1-measure of the framework’s overall performance.

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State of art overview of Non-Intrusive Load Monitoring applications in smart grids

Cited by 18 publications

References 16 publications

Non-Intrusive Load Monitoring by Voltage–Current Trajectory Enabled Transfer Learning

Non-Intrusive Load Monitoring by Voltage–Current Trajectory Enabled Transfer Learning

Design and control of LSTM-ANN controllers for an efficient energy management system in a smart grid based on hybrid renewable energy sources

Ageing Safely in the Digital Era: A New Unobtrusive Activity Monitoring Framework Leveraging on Daily Interactions with Hand-Operated Appliances

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