Non-intrusive Load Monitoring Research Articles

Hollow village detection method based on non-intrusive power load monitoring

With the development of urbanization, the population and resources converge to the city, leading to different degrees of hollow village phenomenon in rural areas. Therefore, a hollow village detection model based on non-invasive load monitoring method is proposed in this paper. Firstly, the power load data of users is collected in rural areas, and the data is processed and analyzed. A data analysis algorithm based on the improved Attention mechanism is constructed, and the existing public data sets are used for verification and improvement. Six kinds of frequently used electrical appliances among rural users were selected as typical electrical appliances, the power load curves of selected typical electrical appliances were obtained, and the long and short term memory network algorithm was used to identify the types of household electrical appliances of rural users. After algorithm recognition and analysis, the usage characteristics of typical rural electric appliances are obtained, and it is taken as the electricity consumption behavior habits of rural residents. According to their electricity consumption habits, rural residents are divided into four categories: young people living alone, the elderly, two adults and a child, and vacant houses. According to the four groups of people, the index of hollow village is determined. By judging the corresponding household population of four groups of rural residents, the selected village is judged whether it is hollow village or whether there is a tendency of hollow village.

Features Extraction and Selection with the Scattering Transform for Electrical Load Classification

The Scattering Transform (ST) presents itself as an alternative approach to the classic methods that involve neural networks and deep learning techniques for the feature extraction and classification of electrical signals. Among its main advantages, one can emphasize that the coefficients of the ST are determined analytically and do not need to be learned, as typically performed in Convolutional Neural Networks (CNNs). Additionally, ST has time-shifting and small time-warping invariance, which reduces the need for precise temporal localization (detection) for subsequent classification. This paper originally proposes six feature extraction and selection methods applied to classification of Non-intrusive Load Monitoring (NILM) high-frequency signals. We visually analyze the separability among classes for the proposed Feature Extractors and validate the performance of the proposed methods varying several parameters for ST calculation, such as signal length, number of examples, and sampling frequency. The results outperform other state-of-the-art feature extraction techniques, reaching up to 100% of FScore for a publicly available dataset, demonstrating the feasibility and promising aspects of the ST for NILM problems.

Will NILM Technology Replace Multi-Meter Telemetry Systems for Monitoring Electricity Consumption?

The estimation of electric power utilization, its baseload, and its heating, light, ventilation, and air-conditioning (HVAC) power component, which represents a very large portion of electricity usage in commercial facilities, are important for energy consumption controls and planning. Non-intrusive load monitoring (NILM) is the analytical method used to monitor the energy and disaggregate total electrical usage into appliance-related signals as an alternative to installing multiple electricity meters in the building. However, despite considerable progress, there are a limited number of tools dedicated to the problem of reliable and complete energy disaggregation. This paper presents an experiment consisting in designing an electrical system with electrical energy receivers, and then starting NILM disaggregation using machine learning algorithms (MLA). The quality of this disaggregation was assessed using dedicated indicators. Subsequently, the quality of these MLA was also verified using the available BLUED data source. The results show that the proposed method guarantees non-intrusive load disaggregation but still requires further research and testing. Measurement data have been published as open research data and listed in the literature section repository.

A non-intrusive load decomposition method of resident by multi-scale attention mechanism

Non-intrusive load monitoring (NILM) is one of the important technologies in home energy management and power demand response scenario. However, the presence of multi-mode appliances and appliances with close power values have affected in diminishing the accuracy of identification based NILM algorithms. To tackle these challenges, the work proposes a resident load decomposition method combining multi-scale attention mechanism and convolutional neural network. At the first stage, the attention scores of the normal load data at the previous few moments of the attention model are smoothed dynamically against the abnormal scores at the current moment. The load identification attention model is optimized by constraint factors. Then, on this basis, convolution filters of different sizes are used to model the mixed load data of different electrical equipment, to mine more abundant characteristic information. Finally, to illustrate the proposed processes and validate its effectiveness, taking the PLAID data set as an example, the method proposed in the article is compared with respect to the existing NILM techniques. The experimental results show that the method based on the multi-scale attention mechanism in this paper can greatly improve the effect of load decomposition. Moreover, it reduces the confusion problem of electrical appliance identification with similar load characteristics.

Methodology for the disaggregation and forecast of demand flexibility in large consumers with the application of non-intrusive load monitoring techniques

Technological advances, innovation and the new industry 4.0 paradigm guide Distribution System Operators towards a competitive market that requires the articulation of flexible demand response systems. The lack of measurement and standardization systems in the industry process chain in developing countries prevents the penetration of demand management models, generating inefficiency in the analysis and processing of information to validate the flexibility potential that large consumers can contribute to the network operator. In this sense, the research uses as input variables the energy and power of the load profile provided by the utility energy meter to obtain the disaggregated forecast in quarter-hour intervals in 4-time windows validated through metrics and its results evaluated by the RMS error to get the total error generated by the methodology with the application of Machine Learning and Big Data techniques in the Python computational tool through Combinatorial Disaggregation Optimization and Factorial Hidden Markov models.

Variational Regression for Multi-Target Energy Disaggregation.

Non-intrusive load monitoring systems that are based on deep learning methods produce high-accuracy end use detection; however, they are mainly designed with the one vs. one strategy. This strategy dictates that one model is trained to disaggregate only one appliance, which is sub-optimal in production. Due to the high number of parameters and the different models, training and inference can be very costly. A promising solution to this problem is the design of an NILM system in which all the target appliances can be recognized by only one model. This paper suggests a novel multi-appliance power disaggregation model. The proposed architecture is a multi-target regression neural network consisting of two main parts. The first part is a variational encoder with convolutional layers, and the second part has multiple regression heads which share the encoder's parameters. Considering the total consumption of an installation, the multi-regressor outputs the individual consumption of all the target appliances simultaneously. The experimental setup includes a comparative analysis against other multi- and single-target state-of-the-art models.

A Non-Intrusive Load Monitoring Model for Electric Vehicles Based on Multi-Kernel Conventional Neural Network

With the widespread use of electric vehicles (EVs), the charging behavior of these resources has brought a large amount of load growth to the grid, leading to a series of problems such as increased peak valley load difference and line flow violation. Non-intrusive load monitoring (NILM) is a key technology that can be employed to monitor the multi-source load data information in the power grid and support the high-proportion access of electric vehicles. However, traditional NILM approaches are designed to identify the operation of household appliances and cannot be applied at the substation level directly due to frequent and intricate switching events of electrical equipment at this stage. In this paper, a NILM algorithm that can be applied for the monitoring of the charging behavior of electric vehicles at the substation level is proposed to support the high-proportion injection of distributed energy resources. The proposed approach employs a deep learning framework and a multi-kernel convolutional neural network (multi-kernel CNN) framework is used. The performance of the proposed method is verified on the self-organized datasets based on Pecan Street data and results showed that the obtained f1 score is over 90% for both the training sets and testing sets.

Deep Adaptive Ensemble Filter for Non-Intrusive Residential Load Monitoring.

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%.

Application of Self-supervised Learning in Non-intrusive Load Monitoring

With the proposal of smart grid, the demand of both source and load for fine monitoring and control of power load is becoming increasingly prominent. Non-intrusive load monitoring is a technical means to better meet this demand. However, the research at home and abroad focuses on the existing data sets and labeled data to improve the accuracy of load identification, while the research on the training method of the model under the massive unlabeled monitoring data in the actual scene is still in a relatively blank stage. Aiming at the problem of how to make full use of unlabeled monitoring data for model training, a non-intrusive-load monitoring method based on self-supervised learning is proposed in this paper. This method designs a self-supervised learning task, so that the model can make full use of the massive unlabeled monitoring data for training, eliminating the step of manually labelling the data; Based on the encoder-decoder structure, a deep learning model is established, and the load is identified through the load characteristic vector output by the encoder, so that the method has generalization performance. In this paper, AMPds2 data set is used to verify the method, and test examples verify the effectiveness of the method.

Very low-resolution residential load disaggregation with unsupervised graph signal processing

An unsupervised graph signal processing-based non-intrusive load monitoring algorithm is used to disaggregate very low-resolution 15-min real power signals from advanced metering infrastructure smart meters. This study presents the application of load disaggregation to new datasets that are more representative of single-family houses within the U.S. than those previously presented in the graph signal processing-based load disaggregation literature. Filtering is performed on signals within the signal database used for labeling reconstructed signals resulting from disaggregation. Results demonstrate improved accuracy due to filtering target device signals. Level 2 electric vehicle chargers are demonstrated to be the most promising candidate for very low-resolution load disaggregation using graph signal processing, with most houses demonstrating FM > 0.8 and absolute percent error in energy estimation less than 25%. The implications of these results on demand response assessment are presented, with an estimated 1350 MWh of energy available during peak demand periods from EV chargers for demand response applications in regions with acceptable disaggregation accuracy.

A Semi-Supervised Approach for Improving Generalization in Non-Intrusive Load Monitoring.

Non-intrusive load monitoring (NILM) considers different approaches for disaggregating energy consumption in residential, tertiary, and industrial buildings to enable smart grid services. The main feature of NILM is that it can break down the bulk electricity demand, as recorded by conventional smart meters, into the consumption of individual appliances without the need for additional meters or sensors. Furthermore, NILM can identify when an appliance is in use and estimate its real-time consumption based on its unique consumption patterns. However, NILM is based on machine learning methods and its performance is dependent on the quality of the training data for each appliance. Therefore, a common problem with NILM systems is that they may not generalize well to new environments where the appliances are unknown, which hinders their widespread adoption and more significant contributions to emerging smart grid services. The main goal of the presented research is to apply a domain adversarial neural network (DANN) approach to improve the generalization of NILM systems. The proposed semi-supervised algorithm utilizes both labeled and unlabeled data and was tested on data from publicly available REDD and UK-DALE datasets. The results show a 3% improvement in generalization performance on highly uncorrelated data, indicating the potential for real-world applications.

Efficient deep generative model for short-term household load forecasting using non-intrusive load monitoring

Home energy management systems (HEMS) enable key strategies and methods to improve residential efficiency and energy utilization. To make informed decisions, HEMS depend on energy monitoring and forecasting systems. However, short-term load forecasting (STLF) at the household level is challenging due to the high uncertainty in load demand caused by the difficulty of expecting customer behavior. Traditional solutions only use household load data at low temporal resolution and cannot adequately consider the users’ power consumption profile, limiting prediction accuracy. This paper addresses these issues and proposes a two-stage approach for the household STLF that leverages past and future appliance load data. In the first stage, the past and future appliance load data is estimated from the aggregate load measurement through a non-intrusive load monitoring (NILM) approach based on a deep generative model using variational autoencoders (VAE). This granular information is then augmented through a novel household load predictive model. We implement six configurations combining three stage-1 NILM models with two stage-2 predictive models based on VAE and temporal convolutional networks. Given the volatile nature of residential loads, identifying the running appliances and their consumption benefits the household STLF. The proposed approach is designed to leverage short-term appliance load forecasts rather than being limited to historical features, which is a key advantage, especially when multi-state appliances are used. The proposed approach is compared to state-of-the-art approaches on the UK-DALE and REFIT datasets and yields competitive results with improvements of 16% and 19% on average for the MAE and MAPE metrics, respectively.

Neural Load Disaggregation: Meta-Analysis, Federated Learning and Beyond

Non-intrusive load monitoring (NILM) techniques are central techniques to achieve the energy sustainability goals through the identification of operating appliances in the residential and industrial sectors, potentially leading to increased rates of energy savings. NILM received significant attention in the last decade, reflected by the number of contributions and systematic reviews published yearly. In this regard, the current paper provides a meta-analysis summarising existing NILM reviews to identify widely acknowledged findings concerning NILM scholarship in general and neural NILM algorithms in particular. In addition, this paper emphasizes federated neural NILM, receiving increasing attention due to its ability to preserve end-users’ privacy. Typically, by combining several locally trained models, federated learning has excellent potential to train NILM models locally without communicating sensitive data with cloud servers. Thus, the second part of the current paper provides a summary of recent federated NILM frameworks with a focus on the main contributions of each framework and the achieved performance. Furthermore, we identify the non-availability of proper toolkits enabling easy experimentation with federated neural NILM as a primary barrier in the field. Thus, we extend existing toolkits with a federated component, made publicly available and conduct experiments on the REFIT energy dataset considering four different scenarios.

An Overview of Non-Intrusive Load Monitoring Based on V-I Trajectory Signature

Non-intrusive load monitoring (NILM) can obtain fine-grained electricity consumption information of each appliance by analyzing the voltage and current data measured at a single point on the bus, which is of great significance for promoting and improving the efficiency and sustainability of the power grid and enhancing the energy efficiency of users. NILM mainly includes data collection and preprocessing, event detection, feature extraction, and appliance identification. One of the most critical steps in NILM is signature extraction, which is the basis for all algorithms to achieve good state detection and energy disaggregation. With the generalization of machine learning algorithms, different algorithms have also been used to extract unique signatures of appliances. Recently, the development and deployment of the voltage–current (V-I) trajectory signatures applied for appliance identification motivated us to present a comprehensive review in this domain. The V-I trajectory signatures have the potential to be an intermediate domain between computer vision and NILM. By identifying the V-I trajectory, we can detect the operating state of the appliance. We also summarize existing papers based on V-I trajectories and look forward to future research directions that help to promote the field’s development.

Leveraging sequence-to-sequence learning for online non-intrusive load monitoring in edge device

Non-intrusive load monitoring (NILM), extracting the appliances’ usage profiles by decomposing a household’s aggregate electricity consumption, has become increasingly important for home energy management. Studies show that users are more likely to adjust their usage behaviors if the real-time feedback on their energy consumption details is provided. Recent state-of-the-art deep learning models have delivered a good performance on load disaggregation; however, these models require tremendous computation and memory to tune huge numbers of parameters, which makes them difficult to be implemented on edge devices to derive real-time appliance usage information for users while keep user’s privacy. In this paper, a lightweight sequence-to-sequence neural network model consisting of only two convolutional layers and dense layers is proposed, which can provide users with both real-time appliance power usage and improved energy consumption profiles. The online results are produced by picking up every end-point of output sequences of the network, whereas the past energy profiles or offline results of appliance can be obtained by averaging all points of the output sequence. By implementing the model in edge device, the user’s privacy is maintained. The lightweight NILM algorithm is evaluated with low-frequency sampling data in two public available datasets: UKDALE and REFIT. Assessment results prove that the proposed model exhibits comparable or even better disaggregation performance with dramatically reduced model size and computation requirements, and thus demonstrates its application potential on edge devices.

A multi-task learning model for non-intrusive load monitoring based on discrete wavelet transform

A multi-task learning model for non-intrusive load monitoring based on discrete wavelet transform

Reserve Evaluation and Energy Management of Micro-grids in Joint Electricity Markets Based on Non-Intrusive Load Monitoring

The heating, ventilation, and air-conditioning (HVAC) units are regarded as major demand response (DR) resources in micro-grids. However, due to privacy concerns and technical constraints, it is difficult for the system operator to obtain the complete information on each individual appliance. In this paper, we present a non-intrusive load monitoring (NILM) based framework for the operation strategy of the micro-grid in the joint energy, reserve, and regulation markets. The NILM technologies enable the operator to disaggregate the power of the HVAC units from the reading of the smart meters. Hence, the operation state of the appliances and the behavior of consumers can be studied without obtaining detailed data of each individual appliance. An advanced NILM algorithm is proposed, and the Coupled Generative Adversarial Networks (C-GAN) are utilized to enhance the generalizability of the trained model. Based on the NILM result, a novel method based on Hidden Markov Model (HMM) is proposed to evaluate the upward and downward reserve capacity of the HVAC units. The evaluated reserve capacity can help the operator better bid in the joint market based on the proposed optimization model. The proposed framework and methodology are verified through case studies. The simulation result reveals that with NILM, the market operator can save more energy consumption costs and load curtailment costs and earn more revenues in the joint market by selling excessive energy and providing ancillary services.

An Efficient Lightweight Event Detection Algorithm for On-Site Non-Intrusive Load Monitoring

Non-intrusive load monitoring (NILM) aims to determine individual-appliance energy consumption with minimum cost by decomposing aggregated electricity measurements. Although important for achieving energy conservation and cost minimization, NILM requires high-frequency sampling rates to provide accurate results. This requirement significantly increases the need for storage and computational resources in the electric utility’s fog/cloud infrastructure and for bandwidth on the customer’s side. To resolve these issues, on- site disaggregation, i.e., on the monitoring device, can be employed. However, to keep device-cost low, lightweight NILM algorithms are needed. To this end, a lightweight event-detection algorithm designed to ease on- site implementation, on either software or hardware, is proposed. Event detection is the first, critical half of the well-established event-based NILM approach; it identifies appliance state changes (events). Although a few lightweight event-detection techniques, utilizing high-frequency data, have been presented in the literature, their performance is relatively low in complex-load cases. The proposed algorithm utilizes simple-to-compute features and employs multiple simple criteria to declare an event as detected and slope-coefficient inspection to identify steady states. Moreover, it can detect events with very small time difference between them. Comparisons show that its performance is superior even against more complex event-detection approaches, while its low computational cost is also verified.

Real-Time Non-Intrusive Load Monitoring for Low-Power Appliances Using Odd Current Harmonics

Non-intrusive load monitoring (NILM) systems have gained immense popularity in the last two decades. Although many solutions were developed using the NILM approach, the majority of solutions failed to identify low-power-consuming appliances. However, a NILM system deployed in a domestic installation should be capable of identifying low-power-consuming appliances precisely. Therefore, we propose two real-time NILM solutions that can identify low-power residential appliances. These two solutions rely on active power and harmonic information extracted from the measured current waveform. We analyzed the performances of the two solutions using simulations and real-world experiments. Results indicate that both solutions can correctly identify appliances with an average accuracy above 90 percent.

Non-Intrusive Load Monitoring: A Review

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.