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Gauges indicate whether the metrics pertaining to the selected load are within an acceptable range.

Gauges indicate whether the metrics pertaining to the selected load are within an acceptable range.

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Article
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Non-intrusive load monitoring (NILM) uses electrical measurements taken at a centralized point in a network to monitor many loads downstream. This paper introduces NILM Dashboard, a machine intelligence and graphical platform that uses NILM data for real-time electromechanical system diagnostics. The operation of individual loads is disaggregated u...

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... "Metric View" shown in Fig. 5 is a user's first stop when a fault is suspected. It provides the user with a set of diagnostic indicators for a selected piece of equipment. The metrics available are real power, apparent power, power factor, average run duration, total daily run time and daily number of actuations. Each metric is displayed as a gauge with green, ...

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... Another important aspect of the NILM models is the interpretation of their outcome, since it facilitates users to improve the overall efficiency thanks to the knowledge gained from NILM feedback [5,53]. In this sense, several solutions have emerged allowing the user to interact with the outcome of NILM models in interactive data visualisations [1,19,48]. Furthermore, improving the interpretability of NILM models would increase the level of confidence in their feedback [41], so any effort to improve transparency is valuable [6]. ...
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Energy management requires reliable tools to support decisions aimed at optimising consumption. Advances in data-driven models provide techniques like Non-Intrusive Load Monitoring (NILM), which estimates the energy demand of appliances from total consumption. Common single-target NILM approaches perform energy disaggregation by using separate learned models for each device. However, the use of single-target systems in real scenarios is computationally expensive and can obscure the interpretation of the resulting feedback. This study assesses a conditioned deep neural network built upon a Fully Convolutional Denoising AutoEncoder (FCNdAE) as multi-target NILM model. The network performs multiple disaggregations using a conditioning input that allows the specification of the target appliance. Experiments compare this approach with several single-target and multi-target models using public residential data from households and non-residential data from a hospital facility. Results show that the multi-target FCNdAE model enhances the disaggregation accuracy compared to previous models, particularly in non-residential data, and improves computational efficiency by reducing the number of trainable weights below 2 million and inference time below 0.25 s for several sequence lengths. Furthermore, the conditioning input helps the user to interpret the model and gain insight into its internal behaviour when predicting the energy demand of different appliances.
... Furthermore, NILM can enhance protection plans, improve load forecasting accuracy, and serve as a benchmark for grid management. With real-time NILM, utilities can recommend specific appliance operations, such as switching off air conditioners during peak hours, to manage the power load more effectively [2]. ...
... Over the past decades, NILM has found applications in various fields, including public administration and energy management, such as optimizing load schedules in smart grids and improving customer satisfaction [1]. In private sectors, NILM technology is used for fault detection and diagnosis in both industrial and residential settings [2], and it can also evaluate socioeconomic information and consumer behavioral patterns [5]. ...
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Non-intrusive load monitoring (NILM), as a key load monitoring technology, can much reduce the deployment cost of traditional power sensors. Previous research has largely focused on developing cloud-exclusive NILM algorithms, which often result in high computation costs and significant service delays. To address these issues, we propose a three-tier framework to enhance the real-world applicability of NILM systems through edge-cloud collaboration. Considering the computational resources available at both the edge and cloud, we implement a lightweight NILM model at the edge and a deep learning based model at the cloud, respectively. In addition to the differential model implementations, we also design a NILM-specific deployment scheme that integrates Gunicorn and NGINX to bridge the gap between theoretical algorithms and practical applications. To verify the effectiveness of the proposed framework, we apply real-world NILM scenario settings and implement the entire process of data acquisition, model training, and system deployment. The results demonstrate that our framework can achieve high decomposition accuracy while significantly reducing the cloud workload and communication overhead under practical considerations.
... However, installing additional meters to the power grid costs extra money. As a solution, the study [99] presented work to detect anomalies based on the appliances' power distribution and participation index. Further, the consumer can obtain feedback about the devices in the grid using the mobile application. ...
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... In the context of "carbon neutral, carbon peak" energy requirements, in order to achieve a carbon peak and carbon neutrality, countries are accelerating the intelligent transformation of the power grid in order to build a new power system with new energy as the theme [1]. In recent years, more and more scholars have devoted their attention to the study of smart grids and new power systems, aiming to achieve more efficient and beneficial energy management initiatives through intelligent facilities and solutions [2,3]. Each household user, as both a consumer and a demander of energy, constitutes an important part of the total energy consumption of the country [4]. ...
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... The software enables numerous NILM devices to be networked together to simultaneously deliver data on loads and there are available tools for fault detection, operational state determination, and energy scorekeeping are available through a graphical user interface. [11] III. CONCLUSION ...
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... This provides operators with a convenient way to understand the status of the network, improving the efficiency of demand-side management [36]. A well-designed NILM system may be able to provide timely alarms that allow the user to prevent dangerous failures of their devices [37][38][39][40][41][42][43][44][45][46][47][48]. Finally, NILM systems and the consumption profiles of the individual household appliances that derive from them were proposed for monitoring the Activities of Daily Livings (ADL) in order to serve Ambient Assisted Living (AAL) systems [49][50][51][52][53][54]. ...
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