Non-intrusive Load Monitoring System Research Articles

Multi-Time-Scale Shapelet-Based Feature Extraction for Non-Intrusive Load Monitoring

The performance of non-intrusive load monitoring (NILM) system is closely related to the uniqueness and comprehensiveness of the load features extracted from the equipment. However, existing feature extraction methods for NILM are difficult to extract interpretable intuitive features representing the uniqueness of load, and lack of comprehensive attention to the features on different time scales. Therefore, this paper proposes a multi-time-scale shapelet based feature extraction framework. First, shapelet, the most unique segment of a sequence, which can distinguish it from other sequences is introduced as a feature and multi-time-scale current shapelets are proposed to extract comprehensive characteristics of electric appliances and offer intuitive interpretability. Meanwhile, a shapelet transform is constructed, so that the essence of shapelet based NILM is transformed from sequence matching to a general classification problem. Next, an ensemble bagging classifier is proposed on multi-time scales to effectively balance the classification errors on each time scale and enhance the performance of NILM system. In addition, this framework can support small sample training to facilitate rapid deployment of NILM applications at the initial practical stage. The results show that this framework can effectively focus on the uniqueness of load on different time scales, provide interpretability for NILM, and achieve good classification effects.

Reliable assessment of uncertainty for appliance recognition in NILM using conformal prediction

AbstractA primary task of Non‐intrusive Load Monitoring (NILM) is the identification of appliances that are switched on or off. However, state‐of‐the‐art machine learning methods such as deep learning do not express uncertainty of their predictions. Especially in cases where appliances are confused, it is desirable that an NILM system can suggest multiple possible predictions to the end‐user, including its confidence and credibility of any given prediction. This can be achieved using conformal prediction, being an effective way to quantify uncertainty of a given machine learning model. In this work, conformal prediction is introduced for NILM and applied to a neural network. The approach is explained and supported by several examples.

Towards Feasible Solutions for Load Monitoring in Quebec Residences.

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.

DeepEdge-NILM: A case study of non-intrusive load monitoring edge device in commercial building

Non-intrusive load monitoring (NILM) has become an emerging technology in the energy sector for its effectiveness in the energy disaggregation of individual loads from the measured aggregated energy in main power supply of building. NILM has also evolved gradually over the past few decades, due to the significant advancements in artificial intelligence (AI), embedded/edge devices, and internet of things (IoT). From the review of literature, we observed that most of the NILM systems were only capable of monitoring different loads and it fails to detect and disaggregate the energy consumption of similar loads effectively. Further, most of works in the literature were focused on improving the performance of energy disaggregation of different loads in residential buildings and very limited works have been carried out for the similar loads in NILM system. Therefore, there is a necessity to study and evaluate the state of the art NILM algorithms for similar loads in real case scenarios for effective implementation in the commercial and industrial buildings. In this paper, we present a case study of the developed DeepEdge-NILM device which has been installed in a commercial building (office environment), where multiple similar and identical air conditioners (AC's) are used in the monitoring environment. A deep learning framework, Long Short-Term Memory (LSTM) has been implemented on the new set of features which comprises of electrical features and intrinsic features derived from the basic electric features using geometric mean for capturing the signature of similar loads effectively. From our case study, we observed that the performance of the deployed NILM system outperforms the baseline systems that have been developed using basic electrical features significantly. Finally, the limitations and future scope of the NILM edge device in the commercial buildings are also discussed.

A New NILM System Based on the SFRA Technique and Machine Learning.

In traditional nonintrusive load monitoring (NILM) systems, the measurement device is installed upstream of an electrical system to acquire the total aggregate absorbed power and derive the powers absorbed by the individual electrical loads. Knowing the energy consumption related to each load makes the user aware and capable of identifying malfunctioning or less-efficient loads in order to reduce consumption through appropriate corrective actions. To meet the feedback needs of modern home, energy, and assisted environment management systems, the nonintrusive monitoring of the power status (ON or OFF) of a load is often required, regardless of the information associated with its consumption. This parameter is not easy to obtain from common NILM systems. This article proposes an inexpensive and easy-to-install monitoring system capable of providing information on the status of the various loads powered by an electrical system. The proposed technique involves the processing of the traces obtained by a measurement system based on Sweep Frequency Response Analysis (SFRA) through a Support Vector Machine (SVM) algorithm. The overall accuracy of the system in its final configuration is between 94% and 99%, depending on the amount of data used for training. Numerous tests have been conducted on many loads with different characteristics. The positive results obtained are illustrated and commented on.

Non-Intrusive Residential Load Monitoring System Using Appliance: Based Energy Disaggregation Models

Non-Intrusive Residential Load Monitoring System Using Appliance: Based Energy Disaggregation 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 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.

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.

A Review of Non-Intrusive Load Monitoring Applications in Industrial and Residential Contexts

Load monitoring systems make it possible to obtain information on the status of the various loads powered by an electrical system. The term “electrical load” indicates any device or circuit that absorbs energy from the system to which it is connected, and which therefore influences electrical quantities such as power, voltage, and current. These monitoring systems, designed for applications related to energy efficiency, can also be used in other applications. This article analyzes in detail how the information derived from Non-Intrusive Load Monitoring (NILM) systems can be used in order to create Energy Management Systems (EMS), Demand Response (DR), anomaly detection, maintenance, and Ambient Assisted Living (AAL).

A Comprehensive Survey for Non-Intrusive Load Monitoring

Energy-saving and efficiency are as important as benefiting from new energy sources to supply increasing energy demand globally. Energy demand and resources for energy saving should be managed effectively. Therefore, electrical loads need to be monitored and controlled. Demand-side energy management plays a vital role in achieving this objective. Energy management systems schedule an optimal operation program for these loads by obtaining more accurate and precise residential and commercial loads information. Different intellegent measurement applications and machine learning algorithms have been proposed for the measurement and control of electrical devices/loads used in buildings. Of these, nonintrusive load monitoring (NILM) is widely used to monitor loads and gather precise information about devices without affecting consumers. NILM is a load monitoring method that uses a total power or current signal taken from a single point in residential and commercial buildings. Therefore, its installation and maintenance costs are low compared to other load monitoring methods. This method consists of signal processing and machine learning processes such as event detection (optional), feature extraction and device identification after the total power or current signal is acquired. Up to now, many techniques have been proposed for each processes in the literature. In this paper, techniques used in NILM systems are classified and a comprehensive review is presented.

An Alternative Low-Cost Embedded NILM System for Household Energy Conservation with a Low Sampling Rate

The measurement of the energy consumption of electrical appliances, where the meter is installed at a single point on the main input circuit of the building, is called non-intrusive load monitoring (NILM). The NILM method can distinguish the loads that are currently active and break down how the loads consume electricity. A microcontroller with embedded software was selected to read the data into the NILM method process at a low sampling rate every 1 s or 1 Hz. The measured data and the data obtained by the NILM algorithm were displayed via an internet platform. This article presents an alternative low-cost embedded NILM system for household energy conservation with a low sampling rate, which could identify electrical appliances such as an air conditioner, refrigerator, television, electric kettle, electric iron, microwave oven, rice cooker, and washing machine. Four features of symmetry pattern were extracted, containing information on the value of active power change, the value of reactive power change, the number of intersection points between the active power data and the reference line, and an estimation of an equation for the starting characteristics of the electrical equipment. The proposed NILM system was tested in a selected test house that used a single-phase power system. A typical meter was also installed to compare the results with the proposed NILM. The validity of the tests was checked for 1 month in 3 houses to analyze the results. The proposed method was able to detect 91.3% of total events. The accuracy of the average ability of the system to disaggregate devices was 0.897. The accuracy value for total power consumption was 0.927. The continuous data recording of the NILM method provides information on the behavior of electrical appliances that can be used for maintenance and warnings.

STM32-Based Appliance Analysis and Identification Device

The most generally used technique of load power monitoring is non-intrusive load monitoring, which requires only one device to be mounted on the bus to monitor the current parameters and the working state of various types of appliances within the total load. It is required to investigate a cost-effective non-intrusive load monitoring and identification system that can perform a range of duties such as fault monitoring, energy monitoring, and fault analysis without requiring a significant number of sensing components. Measurement of electrical values of commonly used home appliances during stable operation, followed by feature extraction and electrical feature analysis to identify appliance types, can help residential users understand appliance habits and consciously reduce consumption and losses while enabling fault detection. The STM32F103RCT6 core control chip and the SUI-101 energy metering module are used in this system to monitor and evaluate load characteristics using the Modbus-RTU communication protocol. The active and reactive power of the load is measured and recorded in the learning mode; in the analysis and identification mode, the electrical parameters of the current appliance, such as current, voltage, active power, reactive power, frequency, and power factor, can be displayed in real-time, and the corresponding load can be deduced using binary simulation and the Euclidean distance matching method. The device has a short learning time and good identification accuracy for typical household appliances, according to the system test, and can satisfy the analysis and recognition of electrical appliances in a regular domestic setting. The current device design combines the advantage of cheap cost, low power consumption, and portability, making it a viable alternative for domestic appliance identification and monitoring.

Nonintrusive Load Monitoring Using an LSTM With Feedback Structure

Many non-intrusive load monitoring (NILM) studies use high-frequency data to classify the device’s ON/OFF state. However, these approaches cannot be applied in real-world situations due to increased network traffic and database capacity issues. For these reasons, when trying to perform NILM with low-frequency data, the power usage pattern that changes over time disappears and features cannot be properly obtained to classify devices. In this article, we propose a novel NILM model that can learn datasets with imbalanced data classes. The model extracts features using long-short term memory (LSTM) and improve the feature representation ability of LSTM through the feedback of predictions. The experiment is conducted using the REDD dataset and the Living-lab validation dataset. In the REDD dataset, the proposed method outperforms conventional methods 10%–20% on the Majority device and 50%–60% on the Minority device. Living-lab validation results show that the performance of the proposed method outperforms other previously proposed NILM systems in low-frequency data and can be applied to real-world NILM situations.

Measurements for non-intrusive load monitoring through machine learning approaches

The topic of non-intrusive load monitoring (NILM) has seen a significant increase in research interest over the past decade, which has led to a significant increase in the performance of these systems. Nowadays, NILM systems are used in numerous applications, in particular by energy companies that provide users with an advanced management service of different consumption. These systems are mainly based on artificial intelligence algorithms that allow the disaggregation of energy by processing the absorbed power signal over more or less long time intervals (generally from fractions of an hour up to 24 h). Less attention was paid to the search for solutions that allow non-intrusive monitoring of the load in (almost) real time, that is, systems that make it possible to determine the variations in loads in extremely short times (seconds or fractions of a second). This paper proposes possible approaches for non-intrusive load monitoring systems operating in real time, analysing them from the point of view of measurement. The measurement and post-processing techniques used are illustrated and the results discussed. In addition, the work discusses the use of the results obtained to train machine learning algorithms that allow you to convert the measurement results into useful information for the user.

Energy disaggregation using variational autoencoders

Non-intrusive load monitoring (NILM) is a technique that uses a single sensor to measure the total power consumption of a building. Using an energy disaggregation method, the consumption of individual appliances can be estimated from the aggregate measurement. Recent disaggregation algorithms have significantly improved the performance of NILM systems. However, the generalization capability of these methods to different houses as well as the disaggregation of multi-state appliances are still major challenges. In this paper we address these issues and propose an energy disaggregation approach based on the variational autoencoders framework. The probabilistic encoder makes this approach an efficient model for encoding information relevant to the reconstruction of the target appliance consumption. In particular, the proposed model accurately generates more complex load profiles, thus improving the power signal reconstruction of multi-state appliances. Moreover, its regularized latent space improves the generalization capabilities of the model across different houses. The proposed model is compared to state-of-the-art NILM approaches on the UK-DALE and REFIT datasets, and yields competitive results. The mean absolute error reduces by 18% on average across all appliances compared to the state-of-the-art. The F1-Score increases by more than 11%, showing improvements for the detection of the target appliance in the aggregate measurement.

Real-time non-intrusive load monitoring: A light-weight and scalable approach

Non-intrusive load monitoring (NILM) is a topic that lately attracts both the academic and the industrial interest. NILM is used to reveal useful information regarding the consumption breakdown on appliance or activity level, thus can be a key solution to unlock various smart-home services and opportunities. To that end, deep learning has arisen as a prominent solution. Although most of the known solutions so far focus on a predefined number of home appliances, this paper proposes a multi-class NILM system which can detect in real-time any number of appliances and can be efficiently embedded into simple microprocessors. The key feature for the identification of the appliances is the processing of measured turn-on active power transient responses sampled at 100 Hz. The NILM system includes three stages; adaptive thresholding event detection method, convolutional neural network and k-nearest neighbors classifier. For future extensions, it is capable to automatically identify new appliances; thus, no retraining and additional modeling is required.

Non-intrusive load monitoring system for similar loads identification using feature mapping and deep learning techniques

In the recent years, non-intrusive load monitoring (NILM) technique has received much attention among researchers because of its effective monitoring of events and extraction of energy consumption of individual loads from aggregated energy consumption. The performance of the existing NILM algorithms may not be adequate while multiple similar type/same kind of loads are connected in an electrical network which cause overlapping of signatures of the individual loads of similar type/same kind. In the present study, multiple air conditioners of same model from the three different makes have been considered for NILM in a commercial/residential environment. Preliminary investigations by considering the general features such as apparent power and power factor for the purpose of load classification indicated that there is scope for improvement in their performance of the load classification. Hence, a new feature set consisting of power and intrinsic features which are derived from the electrical signals during transient and steady state operation of the loads has been proposed. The geometric mean between all the considered electrical features has been computed to capture the relationship between the features for more effective load classification. These studies with the new feature set indicated enhanced performance in load classification of same model and same make. Further, a feature mapping technique, viz. locality constrained linear coding (LLC) has been explored to represent the new set of features as a linear combination of basis vectors to make the features load independent for further improving the performance of load classification of loads of same model and same make. These additional studies indicated that the LLC based approach achieved further enhanced performance as compared to the baseline systems for load classification of loads of same model and same make in NILM systems.

Practical limits to the use of non-intrusive load monitoring in commercial buildings

A non-intrusive load monitoring system (NILM) was installed in three commercial buildings, and evaluated for its ability to disaggregate electric loads. The system largely failed to identify specific loads, leading the authors to identify three key factors that make non-intrusive load identification systematically difficult in mid-size commercial buildings, including the number and complexity of loads, difficulty in interpreting small changes in power consumption, and inability to identify continuously operating loads. Additionally, obtaining data sets for the evaluation of NILM technologies in actual buildings is hampered by disruptions to occupants, misidentification errors, measurement errors, and expense. Enhancements to basic NILM approaches include tagging key devices to facilitate identification, hybrid or supplemental metering, and applying insights from engineering knowledge and audits.

Toward Load Identification Based on the Hilbert Transform and Sequence to Sequence Long Short-Term Memory

Load identification is a core concept in non-intrusive load monitoring (NILM). Through NILM systems, users can check their home appliance usage habits and then adjust their behavior to save electricity. In this way, a NILM system offers an effective method to detect the event status of household appliances as well as individual loads' energy consumption. However, prior NILM methods have encountered a challenge in improving recognition accuracy for both linear load and non-linear load types. These methods used a representative feature, namely transient load signals. However, the transient signals on these loads differ in terms of transient time and transient shape, which is the main cause of reduced accuracy performance in load identification. To this end, this paper presents a novel method, HT-LSTM (Hilbert Transform Long Short-Term Memory), which enhances recognition of the various load types that contain the difference in the transient time and the transient shape of any load signal. The proposed method consists of two main parts: (i) generating a novel transient feature based on a Hilbert transform (HT), called APF (Amplitude-Phase-Frequency). APF features are sequential data, which is used for the classification model; and (ii) applying Sequence-to-Sequence Long Short-Term Memory (Seq2Seq LSTM) to identify appliances by using APF features as the input data. In this work, we evaluate the HT-LSTM method using two high-frequency public datasets, Building-Level fUlly-labeled dataset for Electricity Disaggregation (BLUED) and Plug Load Appliance Identification Dataset (PLAID). Also, we evaluate our method using a private dataset collected in the lab. Based on the experimental results obtained and comparison classification performance pointed, the proposed method outperforms previous methods of F-score measurement on both public datasets in load identification as well as the private dataset.