Event-based Non-intrusive Load Monitoring Research Articles

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.

Robust event detection for residential load disaggregation

Nonintrusive load monitoring (NILM) can facilate the transition to energy-efficient and low-carbon buildings. Event detection is the first and most critical step in event-based NILM and can improve the efficiency and performance of NILM by decreasing inference times to the number of events with transient features extracted from events. However, existing event detection methods with fixed parameters may fail to achieve high accuracy in the case of uncertain and complicated residential load changes such as high fluctuation, long transition, and near simultaneity in both power and time dimensions. Besides, it is difficult to transfer the fixed parameter to new households with different load profiles. Furthermore, most of these methods prove that they are able to detect events but not able to extract features for load disaggregation. This paper proposes a robust event detection method with adaptive parameters to deal with such issues. Specifically, a window with adaptive margins, multi-window screening, and adaptive threshold method is proposed to detect events in aggregated load data with high sampling rate (>1 Hz). The proposed method captures the transitions by adaptively tuning parameters including window width, margin width, and thresholds. It can also achieve good performance with blind parameter setting so that it is suitable for unknown households or datasets. Furthermore, it captures complete transitions that are indispensable for transient feature extraction. Case studies on a 20 Hz dataset, the 50 Hz LIFTED dataset, and the 60 Hz BLUED dataset show that the proposed method can robustly outperform other state-of-the-art event detection methods. The robust performance of the proposed method is also verified by a cross validation of parameters among different datasets. Lastly, the proposed event detection method is demonstrated to have the merits of improving the performance of load disaggregation.

Quantification of Disaggregation Difficulty With Respect to the Number of Smart Meters

A promising approach toward efficient energy management is non-intrusive load monitoring (NILM), that is to extract the consumption of appliances by analyzing the aggregated consumption signal. The large number of appliances and the presence of appliances with close consumption values are known to limit the performance of event-based NILM methods. To tackle these challenges, one could enhance the feature space which in turn results in extra hardware costs, installation complexity, and concerns regarding the consumer’s privacy. This has led to the emergence of an alternative approach, namely semi-intrusive load monitoring, where appliances are partitioned into blocks and the consumption of each block is monitored via separate power smart meters. While a greater number of smart meters can result in more accurate disaggregation, it increases the monetary cost of load monitoring, indicating a trade-off that represents an important gap in this field. In this paper, we take a comprehensive approach to close this gap by establishing a so-called notion of “disaggregation difficulty metric (DDM),” which quantifies how difficult it is to monitor the events of any given group of appliances based on both their power values and the consumer’s usage behavior. Thus, DDM in essence quantifies how much is expected to be gained in terms of disaggregation accuracy of a generic event-based algorithm by installing smart meters on the blocks of any partition of the appliances. Experimental results based on the REDD dataset illustrate the practicality of the proposed approach in addressing the aforementioned trade-off.

Comparative Evaluation of Non-Intrusive Load Monitoring Methods Using Relevant Features and Transfer Learning

Non-Intrusive Load Monitoring (NILM) refers to the analysis of the aggregated current and voltage measurements of Home Electrical Appliances (HEAs) recorded by the house electrical panel. Such methods aim to identify each HEA for a better control of the energy consumption and for future smart grid applications. Here, we are interested in an event-based NILM pipeline, and particularly in the HEAs’ recognition step. This paper focuses on the selection of relevant and understandable features for efficiently discriminating distinct HEAs. Our contributions are manifold. First, we introduce a new publicly available annotated dataset of individual HEAs described by a large set of electrical features computed from current and voltage measurements in steady-state conditions. Second, we investigate through a comparative evaluation a large number of new methods resulting from the combination of different feature selection techniques with several classification algorithms. To this end, we also investigate an original feature selection method based on a deep neural network architecture. Then, through a machine learning framework, we study the benefits of these methods for improving Home Electrical Appliance (HEA) identification in a supervised classification scenario. Finally, we introduce new transfer learning results, which confirm the relevance and the robustness of the selected features learned from our proposed dataset when they are transferred to a larger dataset. As a result, the best investigated methods outperform the previous state-of-the-art results and reach a maximum recognition accuracy above 99% on the PLAID evaluation dataset.

Event Matching Classification Method for Non-Intrusive Load Monitoring

Nowadays, energy management aims to propose different strategies to utilize available energy resources, resulting in sustainability of energy systems and development of smart sustainable cities. As an effective approach toward energy management, non-intrusive load monitoring (NILM), aims to infer the power profiles of appliances from the aggregated power signal via purely analytical methods. Existing NILM methods are susceptible to various issues such as the noise and transient spikes of the power signal, overshoots at the mode transition times, close consumption values by different appliances, and unavailability of a large training dataset. This paper proposes a novel event-based NILM classification algorithm mitigating these issues. The proposed algorithm (i) filters power signals and accurately detects all events; (ii) extracts specific features of appliances, such as operation modes and their respective power intervals, from their power signals in the training dataset; and (iii) labels with high accuracy each detected event of the aggregated signal with an appliance mode transition. The algorithm is validated using REDD with the results showing its effectiveness to accurately disaggregate low-frequency measured data by existing smart meters.

Simultaneous disaggregation of multiple appliances based on non-intrusive load monitoring

Non-intrusive load monitoring (NILM) can achieve the disaggregation of power consumption through electricity data at the power entrance without changing the existing circuit structure, which is helpful for saving energy, reducing emission, and improving the utilization of electric energy. This paper studies event-based NILM, which can disaggregate multiple appliances that are switched simultaneously. That is a mixed linear integer programming (MILP) model, where the 0–1 indicates whether the appliances were switched in the event process. And the 0–1 variable constraints of the power feature vectors during the event are constructed. The proposed MILP runs only when events occur, which can reduce the computational complexity. In event detection, non-dominated sorting genetic algorithm II is used to select the parameters of cumulative sum (CUSUM), such as threshold and the length of sliding windows, which realizes the tradeoff between precision and recall. The case studies demonstrate that the improved CUSUM has a precision of 92% in event detection; the proposed event-based NILM approach on the REDD database and laboratory data achieves an accuracy of over 90% on power draws; the appliance with long switching process, such as the inverter air conditioner, can also be monitored by the sequential combination.

Comparative Evaluation of Machine Learning Models and Input Feature Space for Non-intrusive Load Monitoring

Recent advancement in computational capabilities has accelerated the research and development of non-intrusive load disaggregation. Non-intrusive load monitoring (NILM) offers many promising applications in the context of energy efficiency and conservation. Load classification is a key component of NILM that relies on different artificial intelligence techniques, e.g., machine learning. This study employs different machine learning models for load classification and presents a comprehensive performance evaluation of the employed models along with their comparative analysis. Moreover, this study also analyzes the role of input feature space dimensionality in the context of classification performance. For the above purposes, an event-based NILM methodology is presented and comprehensive digital simulation studies are carried out on a low sampling real-world electricity load acquired from four different households. Based on the presented analysis, it is concluded that the presented methodology yields promising results and the employed machine learning models generalize well for the invisible diverse testing data. The multi-layer perceptron learning model based on the neural network approach emerges as the most promising classifier. Furthermore, it is also noted that it significantly facilitates the classification performance by reducing the input feature space dimensionality.

Adaptive Weighted Recurrence Graphs for Appliance Recognition in Non-Intrusive Load Monitoring

To this day, hyperparameter tuning remains a cumbersome task in Non-Intrusive Load Monitoring (NILM) research, as researchers and practitioners are forced to invest a considerable amount of time in this task. This paper proposes adaptive weighted recurrence graph blocks (AWRG) for appliance feature representation in event-based NILM. An AWRG block can be combined with traditional deep neural network architectures such as Convolutional Neural Networks for appliance recognition. Our approach transforms one cycle per activation current into an weighted recurrence graph and treats the associated hyper-parameters as learn-able parameters. We evaluate our technique on two energy datasets, the industrial dataset LILACD and the residential PLAID dataset. The outcome of our experiments shows that transforming current waveforms into weighted recurrence graphs provides a better feature representation and thus, improved classification results. It is concluded that our approach can guarantee uniqueness of appliance features, leading to enhanced generalisation abilities when compared to the widely researched V-I image features. Furthermore, we show that the initialisation parameters of the AWRG's have a significant impact on the performance and training convergence.

A Hybrid Event Detection Approach for Non-Intrusive Load Monitoring

Non-intrusive load monitoring (NILM) is a practical method to provide appliance-level electricity consumption information. Event detection, as an important part of event-based NILM methods, has a direct impact on the accuracy of the ultimate load disaggregation results in the entire NILM framework. This paper presents a hybrid event detection approach for relatively complex household load datasets that include appliances with long transients, high fluctuations, and/or near-simultaneous actions. The proposed approach includes a base algorithm based on moving average change with time limit, and two auxiliary algorithms based on derivative analysis and filtering analysis. The structure, steps, and working principle of this approach are described in detail. The proposed approach does not require additional information about household appliances, nor does it require any training sets. Case studies on different datasets are conducted to evaluate the performance of the proposed approach in comparison with several existing approaches including log likelihood ratio detector with maxima (LLD-Max) approach, active window-based (AWB) approach, and generalized likelihood ratio (GLR) approach. Results show that the proposed approach works well in detecting events in complex household load datasets and performs better than the existing approaches.

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.

Event-Detection Algorithms for Low Sampling Nonintrusive Load Monitoring Systems Based on Low Complexity Statistical Features

One of the key techniques toward energy efficiency and conservation is nonintrusive load monitoring (NILM) which lies in the domain of energy monitoring. Event detection is a core component of event-based NILM systems. This paper proposes two new low-complexity and computationally fast algorithms that detect the variations of load data and return the time occurrences of the corresponding events. The proposed algorithms are based on the phenomenon of a sliding window (SW) that tracks the statistical features of the acquired aggregated load data. The performance of the proposed algorithms is evaluated using real-world data and a comparative analysis has been carried out with one of the recently proposed event detection algorithms. Based on the simulations and sensitivity analysis, it is shown that the proposed algorithm can provide the results of up to 93% and 88% in terms of recall and precision, respectively.

A Supervised Event-Based Non-Intrusive Load Monitoring for Non-Linear Appliances

Smart meters generate a massive volume of energy consumption data which can be analyzed to recover some interesting and beneficial information. Non-intrusive load monitoring (NILM) is one important application fostered by the mass deployment of smart meters. This paper presents a supervised event-based NILM approach for non-linear appliance activities identification. Firstly, the additive properties (stating that, when a certain amount of specific appliances’ feature is added to their belonging network, an equal amount of change in the network’s feature can be observed) of three features (harmonic feature, voltage–current trajectory feature, and active–reactive–distortion (PQD) power curve features) were investigated through experiments. The results verify the good additive property for the harmonic features and Voltage–Current (U-I) trajectory features. In contrast, PQD power curve features have a poor additive property. Secondly, based on the verified additive property of harmonic current features and the representation of waveforms, a harmonic current features based approach is proposed for NILM, which includes two main processes: event detection and event classification. For event detection, a novel model is proposed based on the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm. Compared to other event detectors, the proposed event detector not only can detect both event timestamp and two adjacent steady states but also shows high detection accuracy over public dataset with F1-score up to 98.99%. Multi-layer perceptron (MLP) classifiers are then built for multi-class event classification using the harmonic current features and are trained using the data collected from the laboratory and the public dataset. The results show that the MLP classifiers have a good performance in classifying non-linear loads. Finally, the proposed harmonic current features based approach is tested in the laboratory through experiments, in which multiple on–off events of multiple appliances occur. The research indicates that clustering-based event detection algorithms are promising for future works in event-based NILM. Harmonic current features have perfect additive property, and MLP classifier using harmonic current features can accurately identify typical non-linear and resistive loads, which could be integrated with other approaches in the future.

Improving Event-Based Non-Intrusive Load Monitoring Using Graph Signal Processing

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 REFIT 1 data sets, sampled at 1 and 8 s, respectively. 1 The REFIT dataset used to generate the results can be accessed via DOI 10.15129/31da3ece-f902-4e95-a093-e0a9536983c4.

Event-Based Energy Disaggregation Algorithm for Activity Monitoring From a Single-Point Sensor

The massive deployment of smart meters and other customized meters has motivated the development of nonintrusive load monitoring (NILM) systems. This is the process of disaggregating the total energy consumption in a building into individual electrical loads using a single-point sensor. Most literature is oriented to energy saving. Nevertheless, activity of daily livings monitoring through NILM is recently receiving much interest. This proposal presents an event-based NILM algorithm of high performance for activity monitoring applications. This is divided into two stages: 1) an event detector and 2) an event classification algorithm. The first one does not need to be trained and shows a detection rate up to 94%. The event classification algorithm uses a novel load signature based on trajectories of active, reactive, and distortion power (PQD) to obtain general models of appliance classes using principal component analysis. The F1 score and the F0.5 score (the last one is more relevant to activity monitoring) draw values of 90.6% and 98.5, respectively.

Dynamic time warping based non-intrusive load transient identification

Non-intrusive load monitoring (NILM) is a novel and cost-effective technology for monitoring load electricity energy consumption details. In the event-based NILM, transient power waveform (TPW) time-series can be used as signatures to identify the transients of the electrical appliances in the aggregated load, and then to determine their operating states, estimate their power demand and cumulative energy consumption. In this paper, for load transient identification, the dynamic time warping (DTW) algorithm is adopted for the first time to measure the similarity between the variable-length raw TPW sample and template time-series. Accordingly, a nearest neighbor transient identification method is proposed to identify the appliance creating the TPW sample time-series, in which the DTW-based integrated distance is used to measure the similarity of TPW signatures. Three schemes to calculate the integrated distance are designed, combining multiple types of TPW signatures. Comparison tests with existing methods are conducted using public datasets. The comparison test results indicate that the proposed load transient identification method cannot only improve the accuracy of load transient identification, but also is easy to implement at a reasonable cost. Ultimately, the proposed method is implemented in an embedded system. The field test results show that it can identify the operating states of electrical appliances with high accuracy.

An error correction framework for sequences resulting from known state-transition models in Non-Intrusive Load Monitoring

Non-Intrusive Load Monitoring (NILM), the set of techniques used for disaggregating total electricity consumption in a building into its constituent electrical loads, has recently received renewed interest in the research community, partly due to the roll-out of smart metering technology worldwide. Event-based NILM approaches (i.e., those that are based on first segmenting the power time-series and associating each segment with the operation of electrical appliances) are a commonly implemented solution but are prone to the propagation of errors through the data processing pipeline. Thus, during energy estimation (the final step in the process), many corrections need to be made to account for errors incurred during segmentation, feature extraction and classification (the other steps typically present in event-based approaches). A robust framework for energy estimation should use the labels from classification to (1) model the different state transitions that can occur in an appliance; (2) account for any misclassifications by correcting event labels that violate the extracted model; and (3) accurately estimate the energy consumed by that appliance over a period of time. In this paper, we address the second problem by proposing an error-correcting algorithm which looks at sequences generated by Finite State Machines (FSMs) and corrects for errors in the sequence; errors are defined as state transitions that violate the said FSM. We evaluate our framework on simulated data and find that it improves energy estimation errors. We further test it on data from 43 appliances collected from 19 houses and find that the framework significantly improves errors in energy estimates when compared to the case with no correction in 19 appliances, leaves 17 appliances unchanged, and has a slightly negative impact on 6 appliances.