Non-Intrusive Energy Disaggregation Using Non-Negative Matrix Factorization With Sum-to-k Constraint

Energy disaggregation algorithms disintegrate aggregate demand into appliance-level demands. Among various energy disaggregation approaches, non-intrusive load monitoring (NILM) algorithms requiring a single sensor have gained much attention in recent years. Various machine learning and optimization-based NILM approaches are available in the literature, but bulk training data and high computational time are their respective drawbacks. Considering these drawbacks, we devised an event matching energy disaggregation algorithm (EMEDA) for NILM of multistate household appliances using smart meter data. Having limited training data, K-means clustering was employed to estimate appliance power states. These power states were accumulated to generate an event database (EVD) containing all combinations of appliance operations in their various states. Prior to matching, the test samples of aggregate demand events were decreased by event-driven data compression for computational effectiveness. The compressed test events were matched in the sorted EVD to assess the contribution of each appliance in the aggregate demand. To counter the effects of transient spikes and/or dips that occurred during the state transition of appliances, a post-processing algorithm was also developed. The proposed approach was validated using the low-rate data of the Reference Energy Disaggregation Dataset (REDD). With better energy disaggregation performance, the proposed EMEDA exhibited reductions of 97.5 and 61.7% in computational time compared with the recent smart event-based optimization and optimization-based load disaggregation approaches, respectively.

Energy disaggregation technology is a key technology to realise real-time non-intrusive load monitoring (NILM). Current energy disaggregation methods use the same scale to extract features from the sequence, which makes part of the local features lost, resulting in low recognition accuracy of electrical appliances. Aiming at the low accuracy of non-intrusive energy disaggregation with low-frequency sampling, a sequential multi-scale residual network is proposed. Multi-scale residual network extracts multi-scale feature information through multi-scale convolution, and uses residual learning to deepen the network structure to further improve the performance of the algorithm. Different scales features are captured by multi-scale convolution to improve the recognition accuracy of electrical appliances with low using frequency. Sequence-to-sequence energy disaggregation mechanism can improve the disaggregation efficiency of the algorithm. The experimental comparison results show that the model can get better disaggregation effect, and can effectively identify the start-stop state of electrical appliances.

International audience

Large-scale smart energy metering deployment worldwide and integration of smart meters within the smart grid will enable two-way communication between the consumer and energy network, thus ensuring improved response to demand. Energy disaggregation or non-intrusive load monitoring (NILM), namely disaggregation of the total metered electricity consumption down to individual appliances using purely algorithmic tools, is gaining popularity as an added-value that makes the most of meter data. However, NILM remains a challenging problem since NILM is susceptible to sensor noise, unknown load noise, transient spikes, and fluctuations. In this paper, we tackle this problem using novel graph signal processing (GSP) concepts, applied at both, physical signal level via graph-based filtering and data level, via effective semi-supervised GSP-based feature matching. The proposed GSP-based method is generic and can be used to improve results of various event-based NILM approaches. We demonstrate significant improvement in performance using three state-of-the-art NILM methods, both supervised and unsupervised, and real-world active power consumption readings from the REDD and REFIT1 data sets, sampled at 1 and 8 s, respectively.

Non-intrusive load monitoring (NILM) is an important topic in smart-grid and\nsmart-home. Many energy disaggregation algorithms have been proposed to detect\nvarious individual appliances from one aggregated signal observation. However,\nfew works studied the energy disaggregation of plug-in electric vehicle (EV)\ncharging in the residential environment since EVs charging at home has emerged\nonly recently. Recent studies showed that EV charging has a large impact on\nsmart-grid especially in summer. Therefore, EV charging monitoring has become a\nmore important and urgent missing piece in energy disaggregation. In this\npaper, we present a novel method to disaggregate EV charging signals from\naggregated real power signals. The proposed method can effectively mitigate\ninterference coming from air-conditioner (AC), enabling accurate EV charging\ndetection and energy estimation under the presence of AC power signals.\nBesides, the proposed algorithm requires no training, demands a light\ncomputational load, delivers high estimation accuracy, and works well for data\nrecorded at the low sampling rate 1/60 Hz. When the algorithm is tested on\nreal-world data recorded from 11 houses over about a whole year (total 125\nmonths worth of data), the averaged error in estimating energy consumption of\nEV charging is 15.7 kwh/month (while the true averaged energy consumption of EV\ncharging is 208.5 kwh/month), and the averaged normalized mean square error in\ndisaggregating EV charging load signals is 0.19.\n

Sparse coding-based transfer learning for energy disaggregation

Sequence to point learning based on bidirectional dilated residual network for non-intrusive load monitoring

Multivariate exploration of non-intrusive load monitoring via spatiotemporal pattern network

Energy disaggregation (or Non-Intrusive Load Monitoring (NILM)) is the process of deducing individual load profiles from aggregate measurements using different machine learning and pattern recognition tools. Existing disaggregation algorithms can be categorized into either supervised approaches or unsupervised ones. Supervised approaches require external information represented in either sub-metered loads or hand-labeled observations while unsupervised algorithms utilize only unlabeled aggregate data. We observed that very few works attempt to utilize both labeled and unlabeled data. In this paper, we introduce a semi-supervised learning tool, namely self-training, to the energy disaggregation problem. Semi-Supervised Learning (SSL) tools leverage both external and internal structural information in order to enhance the learning process and/or reduce the required labeling effort. We also provide test results of the utilized SSL tool compared with a traditional classification component of an event-based NILM system. Results show that even a simple SSL tool is able to reduce the required labeling effort and provides a learning disaggregation system whose performance gradually increases as it observes more unlabeled aggregate measurements.

The rapid development of technology in the electrical energy sector within the last 20 years has led to growing electric power needs through the increased number of electrical appliances and automation of tasks. In parallel the global climate protection goals, energy conservation and efficient energy management arise interest for reduction of the overall energy consumption. These requirements have led to the recent adoption of smart-meters and smart-grids, as well as to the rise of Load Monitoring (LM) using energy disaggregation, also referred to as Non-Intrusive Load Monitoring (NILM), which enables appliance-specific energy monitoring by only observing the aggregated energy consumption of a household. The real-time information on appliance level can be used to get deeper insights in the origin of energy consumption and to make optimization, strategic load scheduling and demand management feasible. The three main contributions are as follows: First, a generalized up-to-date review of NILM approaches including a high-level taxonomy of NILM methodologies is provided. Second, previously published results are grouped based on the experimental setup which allows direct comparison. Third, the article is accompanied by a software implementation of the described NILM approaches.

Non-Intrusive load monitoring (NILM) is an emerging technology for extracting potent information from a consumer's electric load profile. The NILM techniques are gaining popularity among researchers as they reduce the requirement for external hardware for load monitoring. These techniques require only the smart meter's data, thus reducing the need for other measuring and sensing devices. In this paper, the research status of non-intrusive load monitoring has been reviewed. The utilisation of NILM techniques for event detection, load monitoring and energy disaggregation has been discussed in this paper. Furthermore, the research gap and future aspects are discussed. This paper has a great utility factor for upcoming researchers interested in NILM techniques.

Energy-saving schemes are nowadays a major worldwide concern. As the building sector is a major energy consumer, and hence greenhouse gas emitter, research in home energy management systems (HEMS) has increased substantially during the last years. One of the primary purposes of HEMS is monitoring electric consumption and disaggregating this consumption across different electric appliances. Non-intrusive load monitoring (NILM) enables this disaggregation without having to resort in the profusion of specific meters associated with each device. This paper proposes a low-complexity and low-cost NILM framework based on radial basis function neural networks designed by a multi-objective genetic algorithm (MOGA), with design data selected by an approximate convex hull algorithm. Results of the proposed framework on residential house data demonstrate the designed models’ ability to disaggregate the house devices with excellent performance, which was consistently better than using other machine learning algorithms, obtaining F1 values between 68% and 100% and estimation accuracy values ranging from 75% to 99%. The proposed NILM approach enabled us to identify the operation of electric appliances accounting for 66% of the total consumption and to recognize that 60% of the total consumption could be schedulable, allowing additional flexibility for the HEMS operation. Despite reducing the data sampling from one second to one minute, to allow for low-cost meters and the employment of low complexity models and to enable its real-time implementation without having to resort to specific hardware, the proposed technique presented an excellent ability to disaggregate the usage of devices.

Non-intrusive load monitoring (NILM) or energy disaggregation, aims to disaggregate a household's electricity consumption into constituent appliances. More than three decades of work in NILM has resulted in the development of several novel algorithmic approaches. However, despite these advancements, two core challenges still exist: i) disaggregating low power consumption appliances and ii) distinguishing between multiple instances of similar appliances. These challenges are becoming increasingly important due to an increasing number of appliances and increased usage of electronics in homes. Previous approaches have attempted to solve these problems using expensive hardware involving high sampling rates better suited to laboratory settings, or using additional number of sensors, limiting the ease of deployment. In this work, we explore using commercial-off-the-shelf (COTS) power line communication (PLC) modems as an inexpensive and easy to deploy alternative solution to these problems. We use the reduction in bandwidth between two PLC modems, caused due to the change in PLC modulation scheme when different appliances are operated as a signature for an appliance. Since the noise generated in the powerline is dependent both on type and location of an appliance, we believe that our technique based on PLC modems can be a promising addition for solving NILM.

Non-intrusive load monitoring (NILM) technology enables the estimation of both the power consumption and real-time operational states of individual (or target) appliances by analyzing voltage and aggregate current data at the power inlet. The notable performance of modern deep neural networks in energy disaggregation and appliance identification largely stems from the availability of large-scale, appliance-level consumption data with fully accurate annotations. For the widely adopted event-based appliance identification methods, as the construction of customized label data relying on intrusive sub-metering is cost-prohibitive, the aggregate power profiles are often used to acquire this dataset, which tends to result in inaccurate labels. In this work, we propose a contrastive partial label learning method for appliance identification, where each training instance is associated with a coarse candidate label set, which consists of several possible labels including the ground truth. Firstly, a momentum contrastive learning module is employed, which enriches the feature learning space by maintaining a dynamic memory queue of positive and negative samples. This mechanism enables the model to learn more discriminative and consistent representations for the same appliance class, thereby enhancing intra-class compactness and inter-class separability in the embedding space. Secondly, a specific label disambiguation cross-entropy loss function is designed. By dynamically restricting the predicted results only to the candidate label set, it enables the model to progressively increase the predicted probability of the true label during training, thereby effectively facilitating label disambiguation. By integrating the momentum contrastive learning with the label disambiguation cross-entropy loss, the proposed method effectively addresses ambiguously labeled data and achieves comparable appliance identification accuracy in real-world NILM scenarios. Extensive experiments on publicly available NILM datasets validate the effectiveness of the proposed method under the coarse label data.

Non-intrusive Load Monitoring (NILM) proves effective in disaggregating energy consumption data for individual household appliances. Traditional NILM techniques rely on one-dimensional (1D) time series signal (TSS) analysis to predict the energy usage of distinct appliances. Nonetheless, these approaches have limitations in handling data with non-linear behavior and complex patterns. To address this challenge, prior studies have proposed converting 1D TSS into two-dimensional (2D) images using techniques like Gramian Angular Field (GAF), Markov Transition Field (MTF), and Recurrence Plot (RP). Recent research underscores the effectiveness of the Gramian Angular Summation Field (GASF) and Gramian Angular Difference Field (GADF), both variants of GAF, in the NILM domain. This study assesses the performance of three distinct 1D to 2D conversion approaches for energy disaggregation: GADF, GASF, and an enhanced hybrid method integrating GADF and GASF. Performance evaluation utilizes matrices, including accuracy, precision, recall, and the F1 score. The experiments are conducted on a publicly accessible real-world energy dataset encompassing different appliances. The results demonstrate that the combined GADF and GASF method introduced in this study outperforms their individual counterparts with better performance and faster convergence, particularly in the context of energy disaggregation. The findings offer valuable insights for advancing the development of more accurate and efficient NILM techniques.