A critical review of state-of-the-art non-intrusive load monitoring datasets

Iqbal, Hafiz Khurram; Malik, Farhan Hassan; Muhammad, Aoun; Qureshi, Muhammad Ali; Abbasi, Muhammad Nawaz; Chishti, Abdul Rehman

doi:10.1016/j.epsr.2020.106921

Cited by 52 publications

(20 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Since the current work is the first to consider merging ELD datasets, there are research limitations; thus, we suggest conducting further investigations in the following areas: (i) consideration of more ELD datasets based on a summarized list of datasets from a review article [ 16 ]. (ii) evaluation of the performance of the proposed approach and existing works in low-frequency (i.e., aggregated electricity data) ELD datasets.…”

Section: Discussionmentioning

confidence: 99%

“…As mentioned above, one review article summarized 42 benchmark ELD datasets [ 16 ]. Five of these datasets were selected to exemplify the performance of the proposed powerline noise transformation approach.…”

Section: Datasets and Methodologymentioning

confidence: 99%

“…A critical review summarized 42 ELD datasets developed by the scientific community [ 16 ]. These datasets are heterogeneous in nature with varying factors such as location, type of space (e.g., residential, commercial, and industrial), electric appliance, powerline cable, AC power source, and smart meter.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Handling Data Heterogeneity in Electricity Load Disaggregation via Optimized Complete Ensemble Empirical Mode Decomposition and Wavelet Packet Transform

Chui

Gupta

Liu

et al. 2021

Sensors

View full text Add to dashboard Cite

Global warming is a leading world issue driving the common social objective of reducing carbon emissions. People have witnessed the melting of ice and abrupt changes in climate. Reducing electricity usage is one possible method of slowing these changes. In recent decades, there have been massive worldwide rollouts of smart meters that automatically capture the total electricity usage of houses and buildings. Electricity load disaggregation (ELD) helps to break down total electricity usage into that of individual appliances. Studies have implemented ELD models based on various artificial intelligence techniques using a single ELD dataset. In this paper, a powerline noise transformation approach based on optimized complete ensemble empirical model decomposition and wavelet packet transform (OCEEMD–WPT) is proposed to merge the ELD datasets. The practical implications are that the method increases the size of training datasets and provides mutual benefits when utilizing datasets collected from other sources (especially from different countries). To reveal the effectiveness of the proposed method, it was compared with CEEMD–WPT (fixed controlled coefficients), standalone CEEMD, standalone WPT, and other existing works. The results show that the proposed approach improves the signal-to-noise ratio (SNR) significantly.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Datasets and Methodologymentioning

confidence: 99%

See 1 more Smart Citation

Handling Data Heterogeneity in Electricity Load Disaggregation via Optimized Complete Ensemble Empirical Mode Decomposition and Wavelet Packet Transform

Chui

Gupta

Liu

et al. 2021

Sensors

View full text Add to dashboard Cite

show abstract

“…A number of datasets are widely used in related research on energy data analytics so far (e.g., [20][21][22][23][24]). Collecting such datasets, however, is often motivated by the desire to capture a large continuous stream of electrical energy consumption readings for data processing tasks like pattern recognition or forecasting.…”

Section: Data Acquisition Conceptmentioning

confidence: 99%

The Impact of Ambient Sensing on the Recognition of Electrical Appliances

2021

View full text Add to dashboard Cite

Smart spaces are characterized by their ability to capture a holistic picture of their contextual situation. This often includes the detection of the operative states of electrical appliances, which in turn allows for the recognition of user activities and intentions. For electrical appliances with largely different power consumption characteristics, their types and operational times can be easily inferred from data collected at a single metering point (typically, a smart meter). However, a disambiguation between consumers of the same type and model, yet located in different areas of a smart building, is not possible this way. Likewise, small consumers (e.g., wall chargers) are often indiscernible from measurement noise and spurious power consumption events of other appliances. As a consequence thereof, we investigate how additional sensing modalities, i.e., data beyond electrical signals, can be leveraged to improve the appliance detection accuracy. Through a set of practical experiments, recording ambient influences in eight dimensions and testing their effects on 21 appliance types, we evaluate the importance of such added features in the context of appliance recognition. Our results show that electrical power measurements already yield a high appliance recognition accuracy, yet further accuracy improvements are possible when considering ambient parameters as well.

show abstract

“…A small note on the sampling rate of the publicly available datasets is in order. A very recent review [19] provides a description of well known publicly available datasets for energy disaggregation, most of which are either of low frequency (≤1 Hz) or of high frequency (≥10 KHz), with minimal representative datasets in the range of 10 Hz to 500 Hz. This is why there are not many results for NILM in this frequency range.…”

Section: Related Workmentioning

confidence: 99%

Evaluation of Features Generated by a High-End Low-Cost Electrical Smart Meter

et al. 2021

View full text Add to dashboard Cite

The problem of energy disaggregation is the separation of an aggregate energy signal into the consumption of individual appliances in a household. This is useful, since the goal of energy efficiency at the household level can be achieved through energy-saving policies towards changing the behavior of the consumers. This requires as a prerequisite to be able to measure the energy consumption at the appliance level. The purpose of this study is to present some initial results towards this goal by making heavy use of the characteristics of a particular din-rail meter, which is provided by Meazon S.A. Our thinking is that meter-specific energy disaggregation solutions may yield better results than general-purpose methods, especially for sophisticated meters. This meter has a 50 Hz sampling rate over 3 different lines and provides a rather rich set of measurements with respect to the extracted features. In this paper we aim at evaluating the set of features generated by the smart meter. To this end, we use well-known supervised machine learning models and test their effectiveness on certain appliances when selecting specific subsets of features. Three algorithms are used for this purpose: the Decision Tree Classifier, the Random Forest Classifier, and the Multilayer Perceptron Classifier. Our experimental study shows that by using a specific set of features one can enhance the classification performance of these algorithms.

show abstract

A critical review of state-of-the-art non-intrusive load monitoring datasets

Cited by 52 publications

References 23 publications

Handling Data Heterogeneity in Electricity Load Disaggregation via Optimized Complete Ensemble Empirical Mode Decomposition and Wavelet Packet Transform

Handling Data Heterogeneity in Electricity Load Disaggregation via Optimized Complete Ensemble Empirical Mode Decomposition and Wavelet Packet Transform

The Impact of Ambient Sensing on the Recognition of Electrical Appliances

Evaluation of Features Generated by a High-End Low-Cost Electrical Smart Meter

Contact Info

Product

Resources

About