A Scalable Real-Time Non-Intrusive Load Monitoring System for the Estimation of Household Appliance Power Consumption

Athanasiadis, Christos; Doukas, Dimitrios I.; Papadopoulos, Theofilos A.; Chrysopoulos, Antonios C.

doi:10.3390/en14030767

Cited by 82 publications

(50 citation statements)

References 47 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%

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%

Noninvasive Detection of Appliance Utilization Patterns in Residential Electricity Demand

et al. 2021

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“…Table 9 reports the calculated Recall (also called True Positive rate-TPR) and accuracy metrics for event detection and classification respectively for [13], [14] and [18] and also other solutions applied to higher frequency data [19]- [21]. It can be seen that the proposed algorithm achieves good results while being simple and computationally efficient.…”

Section: Performance With Blued Datasetmentioning

confidence: 98%

“…Literature reports few contributions for NILM algorithms applied to BLUED data at the same frequency of 1 Hz [13], [14], [18].…”

Section: Performance With Blued Datasetmentioning

confidence: 99%

“…In [18] an event detection algorithm is used to identify the time instant when a sudden increase of active power occurs, indicating a possible turn-on event, and a convolutional neural network (CNN) classifier is applied for event classification. Results reported for BLUED are limited to three appliances (washing machine, fridge, microwave).…”

Section: Performance With Blued Datasetmentioning

confidence: 99%

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NILM techniques applied to a real-time monitoring system of the electricity consumption

et al. 2021

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Non-Intrusive Load Monitoring (NILM) allows providing appliance-level electricity consumption information and decomposing the overall power consumption by using simple hardware (one sensor) with a suitable software. This paper presents a low-frequency NILM-based monitoring system suitable for a typical house. The proposed solution is a hybrid event-detection approach including an event-detection algorithm for devices with a finite number of states and an auxiliary algorithm for appliances characterized by complex patterns. The system was developed using data collected at households in Italy and tested also with data from BLUED, a widely used dataset of real-world power consumption data. Results show that the proposed approach works well in detecting and classifying what appliance is working and its consumption in complex household load dataset.

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“…On the other hand, NILM is a software-based approach. It requires only one sensor at the load bus terminal, and therefore the installation process is simplified and the corresponding costs are reduced [ 13 , 14 ]. A typical NILM software framework embraces the following three steps: event detection, feature extraction and load identification [ 15 ].…”

Section: Introductionmentioning

confidence: 99%

Non-Intrusive Load Monitoring for Residential Appliances with Ultra-Sparse Sample and Real-Time Computation

Tao

Fan

et al. 2021

Sensors

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To achieve the goal of carbon neutrality, the demand for energy saving by the residential sector has witnessed a soaring increase. As a promising paradigm to monitor and manage residential loads, the existing studies on non-intrusive load monitoring (NILM) either lack the scalability of real-world cases or pay unaffordable attention to identification accuracy. This paper proposes a high accuracy, ultra-sparse sample, and real-time computation based NILM method for residential appliances. The method includes three steps: event detection, feature extraction and load identification. A wavelet decomposition based standard deviation multiple (WDSDM) is first proposed to empower event detection of appliances with complex starting processes. The results indicate a false detection rate of only one out of sixteen samples and a time consumption of only 0.77 s. In addition, an essential feature for NILM is introduced, namely the overshoot multiple (which facilitates an average identification improvement from 82.1% to 100% for similar appliances). Moreover, the combination of modified weighted K-nearest neighbors (KNN) and overshoot multiples achieves 100% appliance identification accuracy under a sampling frequency of 6.25 kHz with only one training sample. The proposed method sheds light on highly efficient, user friendly, scalable, and real-world implementable energy management systems in the expectable future.

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A Scalable Real-Time Non-Intrusive Load Monitoring System for the Estimation of Household Appliance Power Consumption

Cited by 82 publications

References 47 publications

Noninvasive Detection of Appliance Utilization Patterns in Residential Electricity Demand

Noninvasive Detection of Appliance Utilization Patterns in Residential Electricity Demand

NILM techniques applied to a real-time monitoring system of the electricity consumption

Non-Intrusive Load Monitoring for Residential Appliances with Ultra-Sparse Sample and Real-Time Computation

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