Non-intrusive Load Monitoring Algorithms Research Articles

Non-intrusive load monitoring algorithm based on household electricity use habits

The construction of smart grid is an important part of improving the utilization rate of electric energy. As an important way for the construction of smart grid, non-intrusive load decomposition methods have been extensively studied. In this type of method, limited by transmission cost and network bandwidth, low-frequency data has been widely used in practical applications. However, the accuracy of device identification in this case faces challenges. Due to the relatively single characteristics of low-frequency data, it is difficult to express the operating status of complex electrical appliances, resulting in low decomposition performance. In this paper, a non-intrusive load is proposed based on household electrical habits by studying the relationship between household electricity consumption habits and load status decomposition method. The Gaussian mixture model and time information are used to model the probability distribution of the electrical appliance state. This probability distribution is then used as the observation probability distribution of the factor hidden Markov model. In such a way, the BH-FHMM model is proposed. Finally, load decomposition is carried out through the load decomposition process of the FHMM model. In order to verify the performance of the proposed method, an experimental comparison is conducted based on the REDD data set. According to the results, a significant improvement in equipment recognition accuracy is obtained.

Non-Intrusive Load Monitoring Based on Deep Pairwise-Supervised Hashing to Detect Unidentified Appliances

Non-intrusive load monitoring (NILM) is a fast developing technique for appliances operation recognition in power system monitoring. At present, most NILM algorithms rely on the assumption that all fluctuations in the data stream are triggered by identified appliances. Therefore, NILM of identifying unidentified appliances is still an open challenge. To pursue a scalable solution to energy monitoring for contemporary unidentified appliances, we propose a voltage-current (V-I) trajectory enabled deep pairwise-supervised hashing (DPSH) method for NILM. DPSH performs simultaneous feature learning and hash-code learning with deep neural networks, which shows higher identification accuracy than a benchmark method. DPSH can generate different hash codes to distinguish identified appliances. For unidentified appliances, it generates completely new codes that are different from codes of multiple identified appliances to distinguish them. Experiments on public datasets show that our method can get better F1-score than the benchmark method to achieve state-of-the-art performance in the identification of unidentified appliances, and this method maintains high sustainability to identify other unidentified appliances through retraining. DPSH can be resilient against appliance changes in the house.

A Scalable Real-Time Non-Intrusive Load Monitoring System for the Estimation of Household Appliance Power Consumption

Smart-meter technology advancements have resulted in the generation of massive volumes of information introducing new opportunities for energy services and data-driven business models. One such service is non-intrusive load monitoring (NILM). NILM is a process to break down the electricity consumption on an appliance level by analyzing the total aggregated data measurements monitored from a single point. Most prominent existing solutions use deep learning techniques resulting in models with millions of parameters and a high computational burden. Some of these solutions use the turn-on transient response of the target appliance to calculate its energy consumption, while others require the total operation cycle. In the latter case, disaggregation is performed either with delay (in the order of minutes) or only for past events. In this paper, a real-time NILM system is proposed. The scope of the proposed NILM algorithm is to detect the turning-on of a target appliance by processing the measured active power transient response and estimate its consumption in real-time. The proposed system consists of three main blocks, i.e., an event detection algorithm, a convolutional neural network classifier and a power estimation algorithm. Experimental results reveal that the proposed system can achieve promising results in real-time, presenting high computational and memory efficiency.

Computational Cost Analysis and Data-Driven Predictive Modeling of Cloud-Based Online-NILM Algorithm

Online non-intrusive load monitoring algorithms have captivated academia and industries as parsimonious solutions for household energy efficiency monitoring as well as a safety control, anomaly detection, and demand-side management. However, the computational energy cost for executing such algorithms should not overcome the promised energy efficiency from the disaggregated appliance specific consumption information feed-backs. Moreover, the energy efficiency of cloud computing systems is also becoming a concern for the environment due to carbon emission. This study analyzes the energy spent to execute NILM algorithms via computation cost estimation and prediction using computing system-level power monitoring and data-driven approaches. A generic framework for an automated algorithm cost monitoring and modeling methodologies is devised for large load scale deployment of Cloud-based Online-NILM algorithms. The efficacy of the proposed approach was examined and validated on two computing system use-cases, i.e., Dedicated Server and Cloud Virtual Server. The prediction models, developed using statistical and machine learning tools, demonstrate the promising applicability of the data-driven approach with a very high prediction accuracy without detailed knowledge of the computing systems and the algorithm.

PUMPNET: a deep learning approach to pump operation detection

Non-urgent high energy-consuming residential appliances, such as pool pumps, may significantly affect the peak to average ratio (PAR) of energy demand in smart grids. Effective load monitoring is an important step to provide efficient demand response (DR) to PAR. In this paper, we focus on pool pump analytics and present a deep learning framework, PUMPNET, to identify the pool pump operation patterns from power consumption data. Different from conventional time-series based Non-intrusive Load Monitoring (NILM) methods, our approach transfers the time-series data into image-like (date-time matrix) data. Then a U-shaped fully convolutional neural network is developed to detect and segment the image-like data in pixel level for operation detection. Our approach identify whether pool pumps operate given thirty-minute interval aggregated active power consumption data in kilowatt-hours only. Furthermore, the PUMPNET algorithm could identify pool pump operation status with high accuracy in the low-frequency sampling scenario for thousands of household, compared to traditional NILM algorithms which process high sampling rate data and can only apply to limited number of households. Experiments on real-world data validate the promising results of the proposed PUMPNET model.

Selection of Features Based on Electric Power Quantities for Non-Intrusive Load Monitoring

Non-intrusive load monitoring (NILM) is a process of determining the operating states and the energy consumption of single electric devices using a single energy meter providing aggregate load measurements. Due to the large spread of power electronic-based and nonlinear devices connected to the network, the time signals of both voltage and current are typically non-sinusoidal. The effectiveness of a NILM algorithm strongly depends on determining a set of discriminative features. In this paper, voltage and current signals were combined to define, according to the definitions provided in Standard IEEE 1459, different power quantities, that can be used to distinguish different types of appliance. Multi-layer perceptron (MLP) classifiers were trained to solve the appliance detection problem as a multi-class event classification problem, varying the electric features in input. This allowed to select an optimal set of features guarantying good classification performance in identifying typical electric loads.

Building a Graph Signal Processing Model Using Dynamic Time Warping for Load Disaggregation.

Building on recent unsupervised Non-intrusive load monitoring (NILM) algorithms that use graph Laplacian regularization (GLR) and achieve state-of-the-art performance, in this paper, we propose a novel unsupervised approach to design an underlying graph to model the correlation within time-series smart meter measurements. We propose a variable-length data segmentation approach to extract potential events, assign all measurements associated with an identified event to each graph node, employ dynamic time warping to define the adjacency matrix of the graph, and propose a robust cluster labeling approach. Our simulation results on four different datasets show up to 10% improvement in classification performance over competing approaches.

A New Convolutional Neural Network-Based System for NILM Applications

Electrical load planning and demand response programs are often based on the analysis of individual load-level measurements obtained from houses or buildings. The identification of individual appliances’ power consumption is essential, since it allows improvements, which can reduce the appliances’ power consumption. In this article, the problem of identifying the electrical loads connected to a house, starting from the total electric current measurement, is investigated. The proposed system is capable of extracting the energy demand of each individual device using a nonintrusive load monitoring (NILM) technique. An NILM algorithm based on a convolutional neural network is proposed. The proposed algorithm allows simultaneous detection and classification of events without having to perform double processing. As a result, the calculation times can be reduced. Another important advantage is that only the acquisition of current is required. The proposed measurement system is also described in this article. Measurements are conducted using a test system, which is capable of generating the electrical loads found on a typical house. The most important experimental results are also included and discussed in the article.

Self-organizing probability neural network-based intelligent non-intrusive load monitoring with applications to low-cost residential measuring devices

Non-intrusive load monitoring (NILM) is a critical technique for advanced smart grid management due to the convenience of monitoring and analysing individual appliances’ power consumption in a non-intrusive fashion. Inspired by emerging machine learning technologies, many recent non-intrusive load monitoring studies have adopted artificial neural networks (ANN) to disaggregate appliances’ power from the non-intrusive sensors’ measurements. However, back-propagation ANNs have a very limit ability to disaggregate appliances caused by the great training time and uncertainty of convergence, which are critical flaws for low-cost devices. In this paper, a novel self-organizing probabilistic neural network (SPNN)-based non-intrusive load monitoring algorithm has been developed specifically for low-cost residential measuring devices. The proposed SPNN has been designed to estimate the probability density function classifying the different types of appliances. Compared to back-propagation ANNs, the SPNN requires less iterative synaptic weights update and provides guaranteed convergence. Meanwhile, the novel SPNN has less space complexity when compared with conventional PNNs by the self-organizing mechanism which automatically edits the neuron numbers. These advantages make the algorithm especially favourable to low-cost residential NILM devices. The effectiveness of the proposed algorithm is demonstrated through numerical simulation by using the public REDD dataset. Performance comparisons with well-known benchmark algorithms have also been provided in the experiment section.

A Multi-Agent NILM Architecture for Event Detection and Load Classification

A multi-agent architecture for a Non-Intrusive Load Monitoring (NILM) solution is presented and evaluated. The underlying rationale for such an architecture is that each agent (load event detection, feature extraction, and classification) outperforms others of the same type in particular scenarios; hence, by combining the expertise of these agents, the system presents an improved performance. Known NILM algorithms, as well as new algorithms, proposed by the authors, were individually evaluated and compared. The proposed architecture considers a NILM system composed of Load Monitoring Modules (LMM) that report to a Center of Operations, required in larger facilities. For the purposed of evaluating and comparing performance, five load event detect agents, five feature extraction agents, and five classification agents were studied so that the best combinations of agents could be implemented in LMMs. To evaluate the proposed system, the COOLL and the LIT-Dataset were used. Performance improvements were detected in all scenarios, with power-ON and power-OFF detection improving up to 13%, while classification accuracy improved up to 9.4%.

A Novel Transfer Learning-Based Intelligent Nonintrusive Load-Monitoring With Limited Measurements

In this article, the real-time nonintrusive load-monitoring (NILM) problem with limited measurements, i.e., low sampling rate data, is investigated. NILM is a technique to identify the various types of appliances by analyzing the voltage and current features collected by sensors, such as smart outlets. It is one of the most important topics in smart grid management and optimization. Although recent NILM methods developed several general models that can be used for various scenarios, these algorithms either require high-frequency measurement devices or have been limited to use for some specific appliances. A real-time intelligent NILM algorithm that is able to be transferred among different appliances with low-frequency data is still lacking and desperately needed. In this article, a novel online learning-based intelligent NILM algorithm has been developed that can, online, infer a variety of appliances using a transferred model with limited measurements. Especially, the developed algorithm integrates the emerging transfer learning technique along with deep neural networks. Two neural networks are used in two different stages: 1) the long short-term memory (LSTM) neural network to extract the lower level spatial and temporal features from the gray-scale image generated by the measurements and 2) the probabilistic neural network (PNN) to classify the appliance type as well as transfer between appliances. Finally, the algorithm is implemented into practical smart outlet hardware to demonstrate its effectiveness.

Smart Distribution Boards (Smart DB), Non-Intrusive Load Monitoring (NILM) for Load Device Appliance Signature Identification and Smart Sockets for Grid Demand Management.

Traditionally, the choices to balance the grid and meet its peaking power needs are by installing more spinning reserves or perform load shedding when it becomes too much. This problem becomes worse as more intermittent renewable energy resources are installed, forming a substantial amount of total capacity. Advancements in Energy Storage System (ESS) provides the utility new ways to balance the grid and to meet its peak demand by storing un-used off peak energy for peak usage. Large sized ESS—mega watt (MW) level—are installed by different utilities at their substations to provide the high speed grid stabilization to balance the grid to avoid installing more capacity or triggering any current load shedding schemes. However, such large sized ESS systems and their required inverters are costly to install, require much space and their efficacy could also be limited due to network fault current limits and impedances. In this paper, we propose a novel approach and trial for 3000+ homes in Singapore of achieving a large capacity of demand management by developing a smart distribution board (DB) in each home with the high speed metering sensors (>6 kHz sampling rate) and non-intrusive load monitoring (NILM) algorithm, that can assist home users to perform the load/appliance profile identification with daily usage patterns and allow targeted load interruption using the smart sockets/plugs provided. By allowing load shedding at device or appliance level, while knowing their usage profile and preferences, this can allow such an approach to become part of a new voluntary interruptible load management system (ILMS) that requires little user intervention, while minimizing disruption to them, allowing ease of mass participation and thus achieving the intended MW demand management capacities for the grid. This allows for a more cost effective way to better balance the grid without the need for generation capacity growth, large ESS investment while improving the way to perform load shedding without disruptions to entire districts. Simply, home users can now know and participate with the grid in interruptible load (IL) schemes to target specific home appliance, such as water heaters or air conditioning, allowing interruptions during certain times of the day, instead of the entire house, albeit with the right incentives. This allows utilities to achieve MW capacity load shedding with millions of appliances with their preferences, and most importantly, with minimal disruptions to their consumers quality of life. In our paper, we will also consider coupling a small sized Home Energy Storage System (HESS) to amplify the demand management capacity. The proposed approach does not require any infrastructure or wiring changes and is highly scalable. Simulation results demonstrate the effectiveness of the NILM algorithm and achieving high capacity grid demand management. This approach of taking user preferences for appliance level load shedding was developed from the results of a survey of 500 households that indicates >95% participation if they were able to control their choices, possibly allowing this design to be the most successful demand management program than any large ESS solution for the utility. The proposed system has the ability to operate in centralized as part of a larger Energy Management System (EMS) Supervisory Control And Data Acquisition (SCADA) that decide what to dispatch as well as in autonomous modes making it simpler to manage than any MW level large ESS setup. With the availability of high-speed sampling at the DB level, it can rely on EMS SCADA dispatch or when disconnected, rely on the decaying of the grid frequency measured at the metering point in the Smart DB. Our simulation results demonstrate the effectiveness of our proposed approach for fast grid balancing.

A Fundamental Bound on Performance of Non-Intrusive Load Monitoring Algorithms with Application to Smart-Meter Privacy

We consider non-intrusive load monitoring by a sophisticated adversary that knows the load profiles of the appliances and wants to determine their start-finish times based on smart-meter readings. We prove that the expected estimation error of non-intrusive load monitoring algorithms is lower bounded by the trace of the inverse of the cross-correlation matrix between the derivatives of the load profiles of the appliances. This is an interesting observation illustrating that the derivatives of the load profiles are more important than the profiles themselves for non-intrusive load monitoring (i.e., small rapidly-changing loads are easier to identify than large, yet slowly-varying ones). This fundamental bound on the performance of non-intrusive load monitoring adversaries is used to develop privacy-preserving policies. Particularly, we devise a load-scheduling policy by maximizing the lower bound on the expected estimation error of non-intrusive load monitoring algorithms.

Multi-State Energy Classifier to Evaluate the Performance of the NILM Algorithm.

With the large-scale deployment of smart meters worldwide, research in non-intrusive load monitoring (NILM) has seen a significant rise due to its dual use of real-time monitoring of end-user appliances and user-centric feedback of power consumption usage. NILM is a technique for estimating the state and the power consumption of an individual appliance in a consumer’s premise using a single point of measurement device such as a smart meter. Although there are several existing NILM techniques, there is no meaningful and accurate metric to evaluate these NILM techniques for multi-state devices such as the fridge, heat pump, etc. In this paper, we demonstrate the inadequacy of the existing metrics and propose a new metric that combines both event classification and energy estimation of an operational state to give a more realistic and accurate evaluation of the performance of the existing NILM techniques. In particular, we use unsupervised clustering techniques to identify the operational states of the device from a labeled dataset to compute a penalty threshold for predictions that are too far away from the ground truth. Our work includes experimental evaluation of the state-of-the-art NILM techniques on widely used datasets of power consumption data measured in a real-world environment.

Novel Temporal Perturbation-Based Privacy-Preserving Mechanism for Smart Meters

Smart meters provide strong data support for the intelligent construction of power grids but expose users’ privacy-sensitive information to adversaries. Thus, several mechanisms have been proposed for smart meter privacy protection. However, these existing mechanisms either lack consideration of the protection for customer power consumption mode or focus only on data security and ignore the data availability for intelligent services of smart meters. In this study, we propose a temporal perturbation-based privacy-preserving mechanism to achieve a balance between privacy security and data availability. The time disturbance model improves privacy security by staggering the acquisition and release time of smart meter data, destructs the load characteristics hidden in the power waveform, and realizes the fuzzification of real consumption events. Moreover, data availability for metering and billing, electronic scheduling and management, and value-added services is guaranteed. We analyze data availability and privacy security from theoretical and experimental perspectives, deduce a data availability analysis model, and introduce a non-intrusive load monitoring algorithm as a testing method for evaluating security performance. Evaluation results show that the proposed scheme performs well in protecting users’ consumption pattern and is resistant to attacks by load detection; it also ensures data availability when used for intelligent services.

Unsupervised algorithm for disaggregating low-sampling-rate electricity consumption of households

Non-intrusive load monitoring (NILM) has been extensively researched over the last decade. The objective of NILM is to identify the power consumption of individual appliances and to detect when particular devices are on or off from measuring the power consumption of an entire house. This information allows households to receive customized advice on how to better manage their electrical consumption. In this paper, we present an alternative NILM method that breaks down the aggregated power signal into categories of appliances. The ultimate goal is to use this approach for demand-side management to estimate potential flexibility within the electricity consumption of households. Our method is implemented as an algorithm combining NILM and load profile simulation. This algorithm, based on a Markov model, allocates an activity chain to each inhabitant of the household, deduces from the whole-house power measurement and statistical data the appliance usage, generate the power profile accordingly and finally returns the share of energy consumed by each appliance category over time. To analyze its performance, the algorithm was benchmarked against several state-of-the-art NILM algorithms and tested on three public datasets. The proposed algorithm is unsupervised; hence it does not require any labeled data, which are expensive to acquire. Although better performance is shown for the supervised algorithms, our proposed unsupervised algorithm achieves a similar range of uncertainty while saving on the cost of acquiring labeled data. Additionally, our method requires lower computational power compared to most of the tested NILM algorithms. It was designed for low-sampling-rate power measurement (every 15 min), which corresponds to the frequency range of most common smart meters.

Load Disaggregation Using Microscopic Power Features and Pattern Recognition

A new generation of smart meters are called cognitive meters, which are essentially based on Artificial Intelligence (AI) and load disaggregation methods for Non-Intrusive Load Monitoring (NILM). Thus, modern NILM may recognize appliances connected to the grid during certain periods, while providing much more information than the traditional monthly consumption. Therefore, this article presents a new load disaggregation methodology with microscopic characteristics collected from current and voltage waveforms. Initially, the novel NILM algorithm—called the Power Signature Blob (PSB)—makes use of a state machine to detect when the appliance has been turned on or off. Then, machine learning is used to identify the appliance, for which attributes are extracted from the Conservative Power Theory (CPT), a contemporary power theory that enables comprehensive load modeling. Finally, considering simulation and experimental results, this paper shows that the new method is able to achieve 95% accuracy considering the applied data set.

MO-NILM: A multi-objective evolutionary algorithm for NILM classification

Non-intrusive load monitoring (NILM) techniques estimate the consumption of individual appliances in a household or facility, based on readings of a centralized meter. In this work a new method for multi-dimensional NILM signals is proposed—the Multi-objective NILM (MO-NILM). While classical NILM algorithms are based on a single objective function, MO-NILM classifies NILM events by solving a multi-objective optimization problem. The main idea is to model each NILM feature as an objective function, and to mutually minimize these objectives based on the Non-dominated Sorting Genetic Algorithm II (NSGA-II). The presented algorithms can operate in real time using low sampling rates (0.25 Hz and lower) without training the system. In addition, the proposed algorithm is simple, and requires information on the average power signatures of each appliance. The method shows good performance in terms of standard measures when tested on the popular REDD and AMPds datasets.

A Generic Optimisation-Based Approach for Improving Non-Intrusive Load Monitoring

The large-scale deployment of smart metering worldwide has ignited renewed interest in electrical load disaggregation, or non-intrusive load monitoring (NILM). Most NILM algorithms disaggregate one appliance at a time, remove the estimated appliance contribution from the total load, and then move on to disaggregate the next appliance. On one hand, this is efficient since multi-class classification is avoided and analytical models for each appliance can be developed independently of other appliances with the benefit of being transferred to unseen houses that have different sets of appliances. On the other hand, however, these methods can significantly under- or over- estimate the total consumption since they do not minimize the difference between the measured aggregate load and the sum of estimated individual loads. Motivated by minimizing the latter difference without losing the benefits of existing NILM algorithms, we propose novel post-processing approaches for improving the accuracy of existing NILM. This is posed as an optimization problem to refine the final NILM result using regularization based on the level of confidence in the original NILM output. We propose a heuristic method to solve this (combinatorial) Boolean quadratic problem through relaxing zero-one constraint sets to compact zero-one intervals. Convex-based solutions, including norm-1, norm-2, and semi-definite programming-based relaxation, are proposed trading off accuracy and complexity. We demonstrate good performance of the proposed post-processing methods, applicable to any event-based NILM, compared with four state-of-the-art benchmarks, using public REFIT and REDD electrical load datasets.

Low-Complexity Non-Intrusive Load Monitoring Using Unsupervised Learning and Generalized Appliance Models

Awareness of electric energy usage has both societal and economic benefits, which include reduced energy bills and stress on non-renewable energy sources. In recent years, there has been a surge in interest in the field of load monitoring, also referred to as energy disaggregation, which involves methods and techniques for monitoring electric energy usage and providing appropriate feedback on usage patterns to homeowners. The use of unsupervised learning in non-intrusive load monitoring (NILM) is a key area of study, with practical solutions having wide implications for energy monitoring. In this paper, a low-complexity unsupervised NILM algorithm is presented, which is designed toward practical implementation. The algorithm is inspired by a fuzzy clustering algorithm called entropy index constraints competitive agglomeration, but facilitated and improved in a practical load monitoring environment to produce a set of generalized appliance models for the detection of appliance usage within a household. Experimental evaluation conducted using energy data from the reference energy data disaggregation dataset indicates that the algorithm has out-performance for event detection compared with recent state-of-the-art work for unsupervised NILM when considering common NILM metrics, such as accuracy, precision, recall, ${F}$ -measure, and total energy correctly assigned.