Non-Intrusive Load Monitoring: A Multi-Agent Architecture and Results

Pöttker, Fabiana; Lazzaretti, André E.; Renaux, Douglas P. B.; Linhares, Robson R.; Lima, Carlos R. Erig; Ancelmo, Hellen Cristina; Mulinari, Bruna Machado

doi:10.1109/eecs.2018.00067

Cited by 4 publications

(8 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Such filters use the innovation signal as a reference for highlighting switching transients related to turn-on and turn-off events, with small temporal errors (around 1 ms). Even with smaller temporal differences in the location of events compared to RMS methods, a common limitation of DWT and Kalman filters, as discussed in [16], is their relatively low performance for certain types of turn-off events, in addition to a high rate of false positives, i.e., events falsely indicated as turn-on or turn-off.…”

Section: Review Of Techniques For Load Event Detection and Power Signature Recognitionmentioning

confidence: 99%

“…Generally, these methods are less susceptible to noise and present load recognition accuracies above 90%, even when classifying multiple loads, as discussed in [21]. In [9,16], the HCApP can also be used to extract features based on active, reactive, and apparent power of a signal to perform classification, even though some features can be extracted during transients. Still in this context, a Long-Short-Term Memory Recurrent Neural Network (LSTM-RNN) model is proposed in [22], to directly disaggregate load power signals.…”

Section: Review Of Techniques For Load Event Detection and Power Signature Recognitionmentioning

confidence: 99%

“…As can be observed, different methods present different advantages in terms of load detection and identification. In this sense, the main idea of this work is to present a combination of different state-of-the-art load event detection agents, feature extractors, and classifiers, extending our initial work [16], to improve the identification of different loads in a NILM system. To the extent of our knowledge, such combination is still unexplored in the NILM literature, although already used in other energy-related problems [30][31][32], and it can be seen as a significant advantage for NILM implementations.…”

Section: Review Of Techniques For Load Event Detection and Power Signature Recognitionmentioning

confidence: 99%

“…As can be observed, the optimal threshold selection for the first event (ON) is inefficient for another (OFF). Diversely, in the adaptive case, the threshold is proportional to the dispersion of the signal amplitude window [16]. By analyzing if the amplitude of a sample is higher than the threshold value, points of interest, such as load insertion or removal, can be identified.…”

Section: Discrete Wavelet Transformmentioning

confidence: 99%

“…The first detection agent, Kalman filter, had its internal parameters adjusted to maximize the variation of the innovation in the decoupling moments between the model and the measurements (transient in the network): number of harmonics varied from 8 to 10, based on our previous works with Kalman filter detection [16]; size of the input window fixed in the number of samples needed to represent one cycle of the power network; and minimum step of the input window displacement was set at 25% of the total window size (one cycle). Empirically, we reached R = 0.001 and Q = 0.1.…”

Section: Parameter Selectionmentioning

confidence: 99%

See 4 more Smart Citations

A Multi-Agent NILM Architecture for Event Detection and Load Classification

et al. 2020

Self Cite

View full text Add to dashboard Cite

A multi-agent architecture for a Non-Intrusive Load Monitoring (NILM) solution is presented and evaluated. The underlying rationale for such an architecture is that each agent (load event detection, feature extraction, and classification) outperforms others of the same type in particular scenarios; hence, by combining the expertise of these agents, the system presents an improved performance. Known NILM algorithms, as well as new algorithms, proposed by the authors, were individually evaluated and compared. The proposed architecture considers a NILM system composed of Load Monitoring Modules (LMM) that report to a Center of Operations, required in larger facilities. For the purposed of evaluating and comparing performance, five load event detect agents, five feature extraction agents, and five classification agents were studied so that the best combinations of agents could be implemented in LMMs. To evaluate the proposed system, the COOLL and the LIT-Dataset were used. Performance improvements were detected in all scenarios, with power-ON and power-OFF detection improving up to 13%, while classification accuracy improved up to 9.4%.

show abstract

Section: Review Of Techniques For Load Event Detection and Power Signature Recognitionmentioning

confidence: 99%

Section: Review Of Techniques For Load Event Detection and Power Signature Recognitionmentioning

confidence: 99%

Section: Review Of Techniques For Load Event Detection and Power Signature Recognitionmentioning

confidence: 99%

Section: Discrete Wavelet Transformmentioning

confidence: 99%

Section: Parameter Selectionmentioning

confidence: 99%

See 3 more Smart Citations

A Multi-Agent NILM Architecture for Event Detection and Load Classification

et al. 2020

Self Cite

View full text Add to dashboard Cite

show abstract

Data-Driven Analytics for Reliability in the Buildings-to-Grid Integrated System Framework: A Systematic Text-Mining-Assisted Literature Review and Trend Analysis

Bachoumis,

Mylonas,

Plakas

et al. 2023

IEEE Access

View full text Add to dashboard Cite

Data-driven machine learning-based methods have provided immense capabilities, revolutionizing sectors like the Buildings-to-grid (B2G) integrated system. Since the penetration rate of distributed energy resources increases towards a net-zero emissions power system, so does the need for advanced services that ensure B2G-integrated system reliability. The convergence of advancements in machine learning, computational resources at the entire cloud-edge continuum, and large datasets from sensing devices enable the development of these data-driven energy analytics services. This work conducts a systematic text-mining-based literature review to examine the diverse range and trends of machine learning methods used to enhance reliability in B2G-integrated systems. While traditional manual sampling and analysis approaches have limited the effectiveness of previous literature review papers in this field, this systematic literature review work aims to synthesize and summarize the existing body of research more efficiently and effectively. To achieve this, this study collected almost 10,500 papers from Scholar and Scopus databases. It employed text-mining-assisted BERTopic-based topic modelling and statistical trend analysis techniques to uncover semantic patterns and explore the temporal evolution of research themes. A two-dimensional taxonomy was derived to analyze the technical papers from a business and machine learning-related perspective. By quantifying the temporal trends within these topics, the study unveiled insights about the state-of-the-art analytics that ensure reliability in the B2G-integrated system domain while proposing future research directions.

show abstract

A Dataset for Non-Intrusive Load Monitoring: Design and Implementation

et al. 2020

View full text Add to dashboard Cite

A NILM dataset is a valuable tool in the development of Non-Intrusive Load Monitoring techniques, as it provides a means of evaluation of novel techniques and algorithms, as well as for benchmarking. The figure of merit of a NILM dataset includes characteristics such as the sampling frequency of the voltage, current, or power, the availability of indications (ground-truth) of load events during recording, the variety and representativeness of the loads, and the variety of situations these loads are subject to. Considering such aspects, the proposed LIT-Dataset was designed, populated, evaluated, and made publicly available to support NILM development. Among the distinct features of the LIT-Dataset is the labeling of the load events at sample level resolution and with an accuracy and precision better than 5 ms. The availability of such precise timing information, which also includes the identification of the load and the sort of power event, is an essential requirement both for the evaluation of NILM algorithms and techniques, as well as for the training of NILM systems, particularly those based on Machine Learning.

show abstract

Non-Intrusive Load Monitoring: A Multi-Agent Architecture and Results

Cited by 4 publications

References 16 publications

A Multi-Agent NILM Architecture for Event Detection and Load Classification

A Multi-Agent NILM Architecture for Event Detection and Load Classification

Data-Driven Analytics for Reliability in the Buildings-to-Grid Integrated System Framework: A Systematic Text-Mining-Assisted Literature Review and Trend Analysis

A Dataset for Non-Intrusive Load Monitoring: Design and Implementation

Contact Info

Product

Resources

About