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Practical Nonintrusive Load Monitoring Approaches with Meaningful Performance Evaluation

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A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Electronic Sensors".

Deadline for manuscript submissions: closed (30 June 2023) | Viewed by 21342

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Dr. Lina Stankovic

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Guest Editor

Department of Electronic and Electrical Engineering, University of Strathclyde, Glasgow, UK
Interests: signal and information processing; NILM; responsible AI
Special Issues, Collections and Topics in MDPI journals

Dr. Lucas Pereira

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Guest Editor

ITI, LARSyS, Técnico Lisboa, Lisbon, Portugal
Interests: sensing and data acquisition; smart metering; smart grids; data analytics; computational sustainability; NILM; performance evaluation; data sets and data formats; value proposition
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Nonintrusive load monitoring (NILM) or load disaggregation has seen significant breakthroughs since its conception in the early 1980s, with a range of signal processing and machine learning methods proposed. Over the past 20 years, there has been a rapid emergence of smart buildings and active large-scale roll-out of smart metering within smart grids, as well as the growing availability of public datasets. This has shifted the focus of NILM research toward a more practical user-centered approach whereby the meter readings from the majority of the residential sector and small buildings are available at resolutions of one second to one hour, and those of smart buildings at a higher rate. Recent years have seen the emergence of supervised and unsupervised approaches for solving both classification and regression problems in detecting individual appliance usage and their energy consumption. However, performance is still poor for a number of commonly used appliances (e.g., washing machines), resulting in unreliable energy feedback; additionally, the algorithms are not always trustworthy in that they are not reproducible on the same dataset and parameters or replicable to other datasets, and their outcomes are not interpretable. Additionally, the performance metrics—especially in relation to deep learning approaches—are not amenable to comparison with others in the literature or indeed explainable to the end user. In summary, this Special Issue focuses on addressing the following topics:

- Reliable supervised NILM methods that are transferable to ‘unseen’ datasets or reliable unsupervised NILM methods that can operate on any dataset;
- Reliable NILM methods that focus on accurate disaggregation of challenging loads;
- User-centered NILM algorithms for residential and nonresidential buildings;
- Interpretable and explainable algorithms for NILM;
- Fair and explainable metrics for the evaluation of different NILM algorithms;
- Practical NILM deployments or large-scale trials;
- Practical applications of NILM disaggregated data (e.g., flexibility estimation, life cycle analysis);
- Novel datasets, data models, and toolkits for NILM research.

Dr. Lina Stankovic
Dr. Lucas Pereira
Guest Editors

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Keywords

nonintrusive load monitoring
load disaggregation
interpretable ML
energy efficiency
smart meters
energy utilization
machine learning
explainable AI (XAI)
performance evaluation
user-centered

Published Papers (11 papers)

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Research

16 pages, 5050 KiB

Open AccessArticle

A Field Study of Nonintrusive Load Monitoring Devices and Implications for Load Disaggregation

by Ebony Mayhorn, Joshua Butzbaugh and Alan Meier

Sensors 2023, 23(19), 8253; https://0-doi-org.brum.beds.ac.uk/10.3390/s23198253 - 05 Oct 2023

Cited by 1 | Viewed by 879

Abstract

Evaluations of nonintrusive load monitoring (NILM) algorithms and technologies have mostly occurred in constrained, artificial environments. However, few field evaluations of NILM products have taken place in actual buildings under normal operating conditions. This paper describes a field evaluation of a state-of-the-art NILM product, tested in eight homes. The match rate metric—a technique recommended by a technical advisory group—was used to measure the NILM’s success in identifying specific loads and the accuracy of the energy consumption estimates. A performance assessment protocol was also developed to address common issues with NILM mislabeling and ground-truth comparisons that have not been sufficiently addressed in past evaluations. The NILM product’s estimates were compared to the submetered consumption of eight major appliances. Overall, the product had good performance in disaggregating the energy consumption of the electric water heaters, which included both electric resistance and heat-pump water heaters, but only a fair accuracy with refrigerators, dryers, and air conditioners. The performance was poor for cooking equipment, furnace fans, clothes washers, and dishwashers. Moreover, the product was often unable to detect major loads in homes. Typically, two or more appliances were not detected in a home. At least two dryers, furnace fans, and air conditioners went undetected across the eight homes. On the other hand, the dishwasher was detected in all homes where available or monitored. The key findings were qualitatively compared to those of past field evaluations. Potential areas for improvement in NILM product performance were determined along with areas where complementary technologies may be able to aid in load-disaggregation applications. Full article

(This article belongs to the Special Issue Practical Nonintrusive Load Monitoring Approaches with Meaningful Performance Evaluation)

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24 pages, 3528 KiB

Open AccessArticle

Towards Feasible Solutions for Load Monitoring in Quebec Residences

by Sayed Saeed Hosseini, Benoit Delcroix, Nilson Henao, Kodjo Agbossou and Sousso Kelouwani

Sensors 2023, 23(16), 7288; https://0-doi-org.brum.beds.ac.uk/10.3390/s23167288 - 21 Aug 2023

Viewed by 864

Abstract

For many years, energy monitoring at the most disaggregate level has been mainly sought through the idea of Non-Intrusive Load Monitoring (NILM). Developing a practical application of this concept in the residential sector can be impeded by the technical characteristics of case studies. Accordingly, several databases, mainly from Europe and the US, have been publicly released to enable basic research to address NILM issues raised by their challenging features. Nevertheless, the resultant enhancements are limited to the properties of these datasets. Such a restriction has caused NILM studies to overlook residential scenarios related to geographically-specific regions and existent practices to face unexplored situations. This paper presents applied research on NILM in Quebec residences to reveal its barriers to feasible implementations. It commences with a concise discussion about a successful NILM idea to highlight its essential requirements. Afterward, it provides a comparative statistical analysis to represent the specificity of the case study by exploiting real data. Subsequently, this study proposes a combinatory approach to load identification that utilizes the promise of sub-meter smart technologies and integrates the intrusive aspect of load monitoring with the non-intrusive one to alleviate NILM difficulties in Quebec residences. A load disaggregation technique is suggested to manifest these complications based on supervised and unsupervised machine learning designs. The former is aimed at extracting overall heating demand from the aggregate one while the latter is designed for disaggregating the residual load. The results demonstrate that geographically-dependent cases create electricity consumption scenarios that can deteriorate the performance of existing NILM methods. From a realistic standpoint, this research elaborates on critical remarks to realize viable NILM systems, particularly in Quebec houses. Full article

(This article belongs to the Special Issue Practical Nonintrusive Load Monitoring Approaches with Meaningful Performance Evaluation)

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26 pages, 3534 KiB

Open AccessArticle

Explainability-Informed Feature Selection and Performance Prediction for Nonintrusive Load Monitoring

by Rachel Stephen Mollel, Lina Stankovic and Vladimir Stankovic

Sensors 2023, 23(10), 4845; https://0-doi-org.brum.beds.ac.uk/10.3390/s23104845 - 17 May 2023

Cited by 1 | Viewed by 1188

Abstract

With the massive, worldwide, smart metering roll-out, both energy suppliers and users are starting to tap into the potential of higher resolution energy readings for accurate billing, improved demand response, improved tariffs better tuned to users and the grid, and empowering end-users to know how much their individual appliances contribute to their electricity bills via nonintrusive load monitoring (NILM). A number of NILM approaches, based on machine learning (ML), have been proposed over the years, focusing on improving the NILM model performance. However, the trustworthiness of the NILM model itself has hardly been addressed. It is important to explain the underlying model and its reasoning to understand why the model underperforms in order to satisfy user curiosity and to enable model improvement. This can be done by leveraging naturally interpretable or explainable models as well as explainability tools. This paper adopts a naturally interpretable decision tree (DT)-based approach for a NILM multiclass classifier. Furthermore, this paper leverages explainability tools to determine local and global feature importance, and design a methodology that informs feature selection for each appliance class, which can determine how well a trained model will predict an appliance on any unseen test data, minimising testing time on target datasets. We explain how one or more appliances can negatively impact classification of other appliances and predict appliance and model performance of the REFIT-data trained models on unseen data of the same house and on unseen houses on the UK-DALE dataset. Experimental results confirm that models trained with the explainability-informed local feature importance can improve toaster classification performance from 65% to 80%. Additionally, instead of one five-classifier approach incorporating all five appliances, a three-classifier approach comprising a kettle, microwave, and dishwasher and a two-classifier comprising a toaster and washing machine improves classification performance for the dishwasher from 72% to 94% and the washing machine from 56% to 80%. Full article

(This article belongs to the Special Issue Practical Nonintrusive Load Monitoring Approaches with Meaningful Performance Evaluation)

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20 pages, 2301 KiB

Open AccessArticle

Apply Graph Signal Processing on NILM: An Unsupervised Approach Featuring Power Sequences

by Bochao Zhao, Xuhao Li, Wenpeng Luan and Bo Liu

Sensors 2023, 23(8), 3939; https://0-doi-org.brum.beds.ac.uk/10.3390/s23083939 - 12 Apr 2023

Cited by 2 | Viewed by 1606

Abstract

As a low-cost demand-side management application, non-intrusive load monitoring (NILM) offers feedback on appliance-level electricity usage without extra sensors. NILM is defined as disaggregating loads only from aggregate power measurements through analytical tools. Although low-rate NILM tasks have been conducted by unsupervised approaches based on graph signal processing (GSP) concepts, enhancing feature selection can still contribute to performance improvement. Therefore, a novel unsupervised GSP-based NILM approach with power sequence feature (STS-UGSP) is proposed in this paper. First, state transition sequences (STS) are extracted from power readings and featured in clustering and matching, instead of power changes and steady-state power sequences featured in other GSP-based NILM works. When generating graph in clustering, dynamic time warping distances between STSs are calculated for similarity quantification. After clustering, a forward-backward power STS matching algorithm is proposed for searching each STS pair of an operational cycle, utilizing both power and time information. Finally, load disaggregation results are obtained based on STS clustering and matching results. STS-UGSP is validated on three publicly accessible datasets from various regions, generally outperforming four benchmarks in two evaluation metrics. Besides, STS-UGSP estimates closer energy consumption of appliances to the ground truth than benchmarks. Full article

(This article belongs to the Special Issue Practical Nonintrusive Load Monitoring Approaches with Meaningful Performance Evaluation)

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16 pages, 1581 KiB

Open AccessArticle

Deep Adaptive Ensemble Filter for Non-Intrusive Residential Load Monitoring

by Nasrin Kianpoor, Bjarte Hoff and Trond Østrem

Sensors 2023, 23(4), 1992; https://0-doi-org.brum.beds.ac.uk/10.3390/s23041992 - 10 Feb 2023

Cited by 3 | Viewed by 1065

Abstract

Identifying flexible loads, such as a heat pump, has an essential role in a home energy management system. In this study, an adaptive ensemble filtering framework integrated with long short-term memory (LSTM) is proposed for identifying flexible loads. The proposed framework, called AEFLSTM, takes advantage of filtering techniques and the representational power of LSTM for load disaggregation by filtering noise from the total power and learning the long-term dependencies of flexible loads. Furthermore, the proposed framework is adaptive and searches ensemble filtering techniques, including discrete wavelet transform, low-pass filter, and seasonality decomposition, to find the best filtering method for disaggregating different flexible loads (e.g., heat pumps). Experimental results are presented for estimating the electricity consumption of a heat pump, a refrigerator, and a dishwasher from the total power of a residential house in British Columbia (a publicly available use case). The results show that AEFLSTM can reduce the loss error (mean absolute error) by 57.4%, 44%, and 55.5% for estimating the power consumption of the heat pump, refrigerator, and dishwasher, respectively, compared to the stand-alone LSTM model. The proposed approach is used for another dataset containing measurements of an electric vehicle to further support the validity of the method. AEFLSTM is able to improve the result for disaggregating an electric vehicle by 22.5%. Full article

(This article belongs to the Special Issue Practical Nonintrusive Load Monitoring Approaches with Meaningful Performance Evaluation)

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16 pages, 956 KiB

Open AccessArticle

2D Transformations of Energy Signals for Energy Disaggregation

by Pascal A. Schirmer and Iosif Mporas

Sensors 2022, 22(19), 7200; https://0-doi-org.brum.beds.ac.uk/10.3390/s22197200 - 22 Sep 2022

Cited by 2 | Viewed by 1434

Abstract

The aim of Non-Intrusive Load Monitoring is to estimate the energy consumption of individual electrical appliances by disaggregating the overall power consumption that has been sampled from a smart meter at a house or commercial/industrial building. Last decade’s developments in deep learning and the utilization of Convolutional Neural Networks have improved disaggregation accuracy significantly, especially when utilizing two-dimensional signal representations. However, converting time series’ to two-dimensional representations is still an open challenge, and it is not clear how it influences the performance of the energy disaggregation. Therefore, in this article, six different two-dimensional representation techniques are compared in terms of performance, runtime, influence on sampling frequency, and robustness towards Gaussian white noise. The evaluation results show an advantage of two-dimensional imaging techniques over univariate and multivariate features. In detail, the evaluation results show that: first, the active and reactive power-based signatures double Fourier based signatures, as well as outperforming most of the other approaches for low levels of noise. Second, while current and voltage signatures are outperformed at low levels of noise, they perform best under high noise conditions and show the smallest decrease in performance with increasing noise levels. Third, the effect of the sampling frequency on the energy disaggregation performance for time series imaging is most prominent up to 1.2 kHz, while, above 1.2 kHz, no significant improvements in terms of performance could be observed. Full article

(This article belongs to the Special Issue Practical Nonintrusive Load Monitoring Approaches with Meaningful Performance Evaluation)

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16 pages, 606 KiB

Open AccessArticle

A Data-Centric Analysis of the Impact of Non-Electric Data on the Performance of Load Disaggregation Algorithms

by João Góis, Lucas Pereira and Nuno Nunes

Sensors 2022, 22(18), 6914; https://0-doi-org.brum.beds.ac.uk/10.3390/s22186914 - 13 Sep 2022

Cited by 1 | Viewed by 1343

Abstract

Recent research on non-intrusive load monitoring, or load disaggregation, suggests that the performance of algorithms can be affected by factors beyond energy data. In particular, by incorporating non-electric data in load disaggregation analysis, such as building and consumer characteristics, the estimation accuracy of consumption data may be improved. However, this association has rarely been explored in the literature. This work proposes a data-centric methodology for measuring the effect of non-electric characteristics on load disaggregation performance. A real-world dataset is considered for evaluating the proposed methodology, using various appliances and sample rates. The methodology results indicate that the non-electric characteristics may have varying effects on the performances of different building appliances. Therefore, the proposed methodology can be relevant for complementing load disaggregation analysis. Full article

(This article belongs to the Special Issue Practical Nonintrusive Load Monitoring Approaches with Meaningful Performance Evaluation)

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18 pages, 883 KiB

Open AccessArticle

Deep Learning-Based Non-Intrusive Commercial Load Monitoring

by Mengran Zhou, Shuai Shao, Xu Wang, Ziwei Zhu and Feng Hu

Sensors 2022, 22(14), 5250; https://0-doi-org.brum.beds.ac.uk/10.3390/s22145250 - 13 Jul 2022

Cited by 6 | Viewed by 2169

Abstract

Commercial load is an essential demand-side resource. Monitoring commercial loads helps not only commercial customers understand their energy usage to improve energy efficiency but also helps electric utilities develop demand-side management strategies to ensure stable operation of the power system. However, existing non-intrusive methods cannot monitor multiple commercial loads simultaneously and do not consider the high correlation and severe imbalance among commercial loads. Therefore, this paper proposes a deep learning-based non-intrusive commercial load monitoring method to solve these problems. The method takes the total power signal of the commercial building as input and directly determines the state and power consumption of several specific appliances. The key elements of the method are a new neural network structure called TTRNet and a new loss function called MLFL. TTRNet is a multi-label classification model that can autonomously learn correlation information through its unique network structure. MLFL is a loss function specifically designed for multi-label classification tasks, which solves the imbalance problem and improves the monitoring accuracy for challenging loads. To validate the proposed method, experiments are performed separately in seen and unseen scenarios using a public dataset. In the seen scenario, the method achieves an average F1 score of 0.957, which is 7.77% better than existing multi-label classification methods. In the unseen scenario, the average F1 score is 0.904, which is 1.92% better than existing methods. The experimental results show that the method proposed in this paper is both effective and practical. Full article

(This article belongs to the Special Issue Practical Nonintrusive Load Monitoring Approaches with Meaningful Performance Evaluation)

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15 pages, 3295 KiB

Open AccessArticle

A Bayesian Approach to Unsupervised, Non-Intrusive Load Disaggregation

by Luca Massidda and Marino Marrocu

Sensors 2022, 22(12), 4481; https://0-doi-org.brum.beds.ac.uk/10.3390/s22124481 - 14 Jun 2022

Cited by 9 | Viewed by 2040

Abstract

Estimating household energy use patterns and user consumption habits is a fundamental requirement for management and control techniques of demand response programs, leading to a growing interest in non-intrusive load disaggregation methods. In this work we propose a new methodology for disaggregating the electrical load of a household from low-frequency electrical consumption measurements obtained from a smart meter and contextual environmental information. The method proposed allows, with an unsupervised and non-intrusive approach, to separate loads into two components related to environmental conditions and occupants’ habits. We use a Bayesian approach, in which disaggregation is achieved by exploiting actual electrical load information to update the a priori estimate of user consumption habits, to obtain a probabilistic forecast with hourly resolution of the two components. We obtain a remarkably good accuracy for a benchmark dataset, higher than that obtained with other unsupervised methods and comparable to the results of supervised algorithms based on deep learning. The proposed procedure is of great application interest in that, from the knowledge of the time series of electricity consumption alone, it enables the identification of households from which it is possible to extract flexibility in energy demand and to realize the prediction of the respective load components. Full article

(This article belongs to the Special Issue Practical Nonintrusive Load Monitoring Approaches with Meaningful Performance Evaluation)

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14 pages, 3756 KiB

Open AccessArticle

ELECTRIcity: An Efficient Transformer for Non-Intrusive Load Monitoring

by Stavros Sykiotis, Maria Kaselimi, Anastasios Doulamis and Nikolaos Doulamis

Sensors 2022, 22(8), 2926; https://0-doi-org.brum.beds.ac.uk/10.3390/s22082926 - 11 Apr 2022

Cited by 26 | Viewed by 4058

Abstract

Non-Intrusive Load Monitoring (NILM) describes the process of inferring the consumption pattern of appliances by only having access to the aggregated household signal. Sequence-to-sequence deep learning models have been firmly established as state-of-the-art approaches for NILM, in an attempt to identify the pattern of the appliance power consumption signal into the aggregated power signal. Exceeding the limitations of recurrent models that have been widely used in sequential modeling, this paper proposes a transformer-based architecture for NILM. Our approach, called ELECTRIcity, utilizes transformer layers to accurately estimate the power signal of domestic appliances by relying entirely on attention mechanisms to extract global dependencies between the aggregate and the domestic appliance signals. Another additive value of the proposed model is that ELECTRIcity works with minimal dataset pre-processing and without requiring data balancing. Furthermore, ELECTRIcity introduces an efficient training routine compared to other traditional transformer-based architectures. According to this routine, ELECTRIcity splits model training into unsupervised pre-training and downstream task fine-tuning, which yields performance increases in both predictive accuracy and training time decrease. Experimental results indicate ELECTRIcity’s superiority compared to several state-of-the-art methods. Full article

(This article belongs to the Special Issue Practical Nonintrusive Load Monitoring Approaches with Meaningful Performance Evaluation)

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17 pages, 903 KiB

Open AccessArticle

Neural Fourier Energy Disaggregation

by Christoforos Nalmpantis, Nikolaos Virtsionis Gkalinikis and Dimitris Vrakas

Sensors 2022, 22(2), 473; https://0-doi-org.brum.beds.ac.uk/10.3390/s22020473 - 09 Jan 2022

Cited by 13 | Viewed by 2145

Abstract

Deploying energy disaggregation models in the real-world is a challenging task. These models are usually deep neural networks and can be costly when running on a server or prohibitive when the target device has limited resources. Deep learning models are usually computationally expensive and they have large storage requirements. Reducing the computational cost and the size of a neural network, without trading off any performance is not a trivial task. This paper suggests a novel neural architecture that has less learning parameters, smaller size and fast inference time without trading off performance. The proposed architecture performs on par with two popular strong baseline models. The key characteristic is the Fourier transformation which has no learning parameters and it can be computed efficiently. Full article

(This article belongs to the Special Issue Practical Nonintrusive Load Monitoring Approaches with Meaningful Performance Evaluation)

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