Non-intrusive Load Monitoring Research Articles

An intelligent non-intrusive load monitoring model based on power encoding and convolutional state modules

Abstract Non-Intrusive Load Monitoring (NILM) identifies device power consumption or on/off states solely based on total power data, which is highly valuable for consumers to understand their appliance usage behavior and take necessary measures to reduce energy consumption, especially for the benefit of energy consumers' living production. However, a challenge faced by NILM is the tendency to focus excessively on power disaggregation while neglecting the disaggregation of on/off states, leading to lower classification accuracy, particularly owning to imbalanced states. This study proposes a model that integrates the power and on/off states to simultaneously disaggregate the power and device on/off states. The model comprises two main modules: a power encoding module for power disaggregation, and a convolutional state module for on/off state disaggregation. The power encoding module utilizes BERT-LSTM and Long Short-Term Memory (LSTM) networks for initial energy disaggregation. In contrast, the convolutional state module employs Convolutional Neural Networks (CNNs) for device state disaggregation. The output of the power-encoding module is multiplied by the probability of on/off states to obtain the final power.The proposed model is evaluated using the REDD and UK-DALE datasets. Compared to the baseline models, the results show an improvement in the device state classification average accuracy from 0.948 to 0.957, and a decrease in the average error between the real power and disaggregated power from 26.356W to 25.108W. Additionally, real-world experiments conducted using the designed platform for collecting and disaggregating power data achieve an average accuracy of 0.997. The proposed model demonstrates competitiveness in the NILM field and underscores its significance in aiding energy-consumption reduction efforts.

Transient dataset of household appliances with Intensive switching events

With the development of Non-Intrusive Load Monitoring (NILM), it has become feasible to perform device identification, energy consumption decomposition, and load switching detection using Deep Learning (DL) methods. Similar to other machine learning problems, the research and validation of NILM necessitate substantial data support. Moreover, different regions exhibit distinct characteristics in their electricity environments. Therefore, there is a need to provide open datasets tailored to different regions. In this paper, we introduce the Transient Dataset of Household Appliances with Intensive Switching Events (TDHA25). This dataset comprises switch instantaneous data from 10 typical household appliances in China. The TDHA dataset features a high sampling rate, accurate labelling, and realistic representation of actual appliance start-up waveforms. Additionally, appliance switching is achieved through precise control of relay switches, thus mitigating interference caused by mechanical switches. By furnishing such a dataset, we aim not only to enhance the recognition accuracy of existing NILM algorithms but also to facilitate the application of NILM algorithms in regions sharing similar electricity consumption characteristics to those of China.

Learning-Based Non-Intrusive Electric Load Monitoring for Smart Energy Management.

State-of-the-art smart cities have been calling for economic but efficient energy management over a large-scale network, especially for the electric power system. It is a critical issue to monitor, analyze, and control electric loads of all users in the system. In this study, a non-intrusive load monitoring method was designed for smart power management using computer vision techniques popular in artificial intelligence. First of all, one-dimensional current signals are mapped onto two-dimensional color feature images using signal transforms (including the wavelet transform and discrete Fourier transform) and Gramian Angular Field (GAF) methods. Second, a deep neural network with multi-scale feature extraction and attention mechanism is proposed to recognize all electrical loads from the color feature images. Third, a cloud-based approach was designed for the non-intrusive monitoring of all users, thereby saving energy costs during power system control. Experimental results on both public and private datasets demonstrate that the method achieves superior performances compared to its peers, and thus supports efficient energy management over a large-scale Internet of Things network.

Resilient data-driven non-intrusive load monitoring for efficient energy management using machine learning techniques

The integration of smart homes into smart grids presents numerous challenges, particularly in managing energy consumption efficiently. Non-intrusive load management (NILM) has emerged as a viable solution for optimizing energy usage. However, as smart grids incorporate more distributed energy resources, the complexity of demand-side management and energy optimization escalates. Various techniques have been proposed to address these challenges, but the evolving grid necessitates intelligent optimization strategies. This article explores the potential of data-driven NILM (DNILM) by leveraging multiple machine learning algorithms and neural network architectures for appliance state monitoring and predicting future energy consumption. It underscores the significance of intelligent optimization techniques in enhancing prediction accuracy. The article compares several data-driven mechanisms, including decision trees, sequence-to-point models, denoising autoencoders, recurrent neural networks, long short-term memory, and gated recurrent unit models. Furthermore, the article categorizes different forms of NILM and discusses the impact of calibration and load division. A detailed comparative analysis is conducted using evaluation metrics such as root-mean-square error, mean absolute error, and accuracy for each method. The proposed DNILM approach is implemented using Python 3.10.5 on the REDD dataset, demonstrating its effectiveness in addressing the complexities of energy optimization in smart grid environments.

Enhancing NILM classification via robust principal component analysis dimension reduction

Non-intrusive load monitoring (NILM) techniques estimate the consumption of individual appliances in a household or facility, based on aggregated reading from a centralized meter. Usually, NILM techniques are shown to be improved when various power features and additional power quality parameters are included. However, adding power features leads to increased time complexity which is a disadvantage to real-time operation. Previous attempt to operate a principal component analysis (PCA) method to reduce the dimension of the problem managed to improve the run time but with considerably low accuracy. To this end, we utilize a robust PCA approach, to mitigate the influence of outliers in the data as a measure for improved performance. The proposed procedure achieves extraordinary results with accuracy over 96% for 600 hours long record of power quality measurements of the consumption of seven appliances from the standard AMPds dataset.

Unknown appliances detection for non-intrusive load monitoring based on vision transformer with an additional detection head

Non-intrusive load monitoring (NILM) offers precise insights into equipment-level energy consumption by analyzing current and voltage data from residential smart meters, thus emerging as a potential strategy for demand-side management in power systems. However, a prevalent limitation in current NILM techniques is the presupposition of a known inventory of household appliances, an assumption that often becomes impractical due to the regular introduction of new appliances by consumers. To address this challenge, our approach integrates a vision transformer network with an additional detection head (ViTD), utilizing V-I trajectory images. Initially, the ViT model is trained to classify known appliances. Subsequently, an additional detection head is incorporated to manipulate the embedded features, encouraging the formation of distinct, compact class centers for the known appliance categories. During testing, samples are identified as either known or unknown appliances based on their proximity to these class centers. We utilize two public datasets, PLAID and WHITED, to demonstrate the effectiveness and superiority of our proposed method.

A Multi-Task Deep Learning Approach for Non-Intrusive Load Monitoring of Multiple Appliances

A Multi-Task Deep Learning Approach for Non-Intrusive Load Monitoring of Multiple Appliances

Evaluation of Deep Learning-Based Non-Intrusive Thermal Load Monitoring

Non-Intrusive Load Monitoring (NILM), which provides sufficient load for the energy consumption of an entire building, has become crucial in improving the operation of energy systems. Although NILM can decompose overall energy consumption into individual electrical sub-loads, it struggles to estimate thermal-driven sub-loads such as occupants. Previous studies proposed Non-Intrusive Thermal Load Monitoring (NITLM), which disaggregates the overall thermal load into sub-loads; however, these studies evaluated only a single building. The results change for other buildings due to individual building factors, such as floor area, location, and occupancy patterns; thus, it is necessary to analyze how these factors affect the accuracy of disaggregation for accurate monitoring. In this paper, we conduct a fundamental evaluation of NITLM in various realistic office buildings to accurately disaggregate the overall thermal load into sub-loads, focusing on occupant thermal load. Through experiments, we introduce NITLM with deep learning models and evaluate these models using thermal load datasets. These thermal load datasets are generated by a building energy simulation, and its inputs for the simulation were derived from realistic data like HVAC on/off data. Such fundamental evaluation has not been done before, but insights obtained from the comparison of learning models are necessary and useful for improving learning models. Our experimental results shed light on the deep learning-based NITLM models for building-level efficient energy management systems.

MMNet-NILM: Multi-Target MobileNets for non-intrusive load monitoring

Energy is a critical resource for daily activities and lifestyles with direct impacts on the economy, health and environment. Therefore, monitoring its efficient use is essential to reduce energy waste and lessen related concerns such as global warming and climate change. One of the prominent and evolving solutions is Non-Intrusive Load Monitoring (NILM) smart meters, which enables consumers to track their per-appliance energy consumption more effectively. Some recent approaches have proposed deep learning as a powerful tool for energy disaggregation. However, it is difficult to employ these models in resource-constrained end devices for effective energy monitoring. In this paper, we explore and evaluate a lightweight improved model for multi-target non-intrusive load monitoring based on MobileNet architectures. With extensive experiments using the ENERTALK dataset, the results show that MobileNetV3-large is the most appealing for energy disaggregation as it requires about 55% less storage for trained model and about 6% less training time than MobileNetV2 with almost the same performance. On average, version 3 large has a 17.63% reduction in SAE and requires 54.21% and 8.93% less space and less training time than version 2, respectively. Moreover, the average performance is boosted using an ensemble multi-target MobileNet model across all houses, leading to significant reduction of MAE, SAE, and RMSE errors of about 6%, 48%, and 4%, respectively. In comparison to other work, the proposed MMNet-NILM shows superior performance for the majority of appliances in terms of all considered evaluation metrics.

Beyond privacy and security: Exploring ethical issues of smart metering and non-intrusive load monitoring

Artificial intelligence is believed to facilitate cost-effective and clean energy by optimizing consumption, reducing emissions, and enhancing grid reliability. Approaches such as non-intrusive load monitoring (NILM) offer energy efficiency insights but raise ethical concerns. In this paper, we identify most prominent ethical and societal issues by surveying relevant literature on smart metering and NILM. We combine these findings with empirical insights gained from qualitative workshops conducted with an electricity supplier piloting the use of AI for power load disaggregation. Utilizing the requirements for trustworthy AI, we show that while issues related to privacy and security are the most widely discussed, there are many other equally important ethical and societal issues that need to be addressed, such as algorithmic bias, uneven access to infrastructure, or loss of human control and autonomy. In total, we identify 19 such overarching themes and explore how they align with practitioners' perspectives and how they embody the seven core requirements for trustworthy AI systems defined by the Ethics Guidelines for Trustworthy AI.

How Can Non-Intrusive Load Monitoring Contribute to the Assessment of the Smart Readiness Indicator?

The Smart Readiness Indicator (SRI) is a framework introduced by the EU in 2018 to assess smart buildings in various aspects. However, the SRI has been criticized for several limitations, including its ambiguous service definitions. This paper proposes the application of Non-Intrusive-Load Monitoring (NILM) technology to enhance SRI evaluation on the example of SRI service E-12. NILM can be used to disaggregate energy consumption data to end use levels and allows for granular non-intrusive energy consumption measurement. The study involves a rigorous methodology using open sensor data and NILM algorithms to evaluate device-specific energy consumption We evaluate the IDEAL dataset and three different frequencies (5s, 15min, 1h), three different algorithms (CO, RNN, Seq2Point) and one data imputation strategies (forward filling). The results show that with a higher frequency, the performance metrics (F-score, normalized absolute error) increase. Regarding further considerations, we identify a trade-off between resource and energy efficiency, as well as privacy considerations with increasing measurement frequency. To achieve its aims for awareness, the SRI needs to consider interoperability and appropriate aggregations (frequency and spatial).

Non-Intrusive Load Identification Based on Retrainable Siamese Network.

Non-intrusive load monitoring (NILM) can identify each electrical load and its operating state in a household by using the voltage and current data measured at a single point on the bus, thereby behaving as a key technology for smart grid construction and effective energy consumption. The existing NILM methods mainly focus on the identification of pre-trained loads, which can achieve high identification accuracy and satisfying outcomes. However, unknown load identification is rarely involved among those methods and the scalability of NILM is still a crucial problem at the current stage. In light of this, we have proposed a non-intrusive load identification method based on a Siamese network, which can be retrained after the detection of an unknown load to increase the identification accuracy for unknown loads. The proposed Siamese network comprises a fixed convolutional neural network (CNN) and two retrainable back propagation (BP) networks. When an unknown load is detected, the low-dimensional features of its voltage-current (V-I) trajectory are extracted by using the fixed CNN model, and the BP networks are retrained online. The finetuning of BP network parameters through retraining can improve the representation ability of the network model; thus, a high accuracy of unknown load identification can be achieved by updating the Siamese network in real time. The public WHITED and PLAID datasets are used for the validation of the proposed method. Finally, the practicality and scalability of the method are demonstrated using a real-house environment test to prove the ability of online retraining on an embedded Linux system with STM32MP1 as the core.

Perfednilm: a practical personalized federated learning-based non-intrusive load monitoring

Non-Intrusive Load Monitoring (NILM) is a valuable technique for breaking down overall power consumption into the energy usage of individual appliances. Understanding power usage patterns through NILM plays an important role in reducing energy costs and achieving carbon reduction goals. However, privacy concerns often deter consumers from sharing their electricity consumption data. To address these privacy concerns, Federated Learning (FL) has been introduced in NILM, which enables the training of NILM models while keeping power consumers’ data locally. However, FL’s reliance on a single global model leads to poor performance on clients with unique power consumption patterns. In response to this challenge, we present a Personalized Federated Learning NILM algorithm (PerFedNILM), a practical personalized FL approach for NILM. PerFedNILM limits the local update bias across clients and trains personalized models for individual clients to improve load-monitoring performance. In addition, it mitigates the negative impact of client dropout, which is a common issue in practice. Our experiments on using real-world energy data demonstrate that PerFedNILM outperforms previous FL-based NILM methods, especially in client dropout scenarios.

Enhancing non-intrusive load monitoring with weather and calendar feature integration in DAE

The construction of modern power system is key to achieving dual carbon goals, where non-intrusive load monitoring (NILM) plays a vital role in enhancing energy utilization efficiency and energy management. For example, to enable prosumers to better understand the extent of their flexible loads for demand response and peer-to-peer trading, it is essential to be aware of the types and states of loads using the method of NILM. To improve the predictive accuracy and implementation effectiveness of NILM technology, this paper proposes a novel NILM method integrating meteorological and calendar features. It delves deeply into the close connection between external factors such as temperature, precipitation, wind speed, and holidays, and the energy consumption of electrical appliances, constructing additional associative mappings in the training of the Denoising Autoencoder (DAE) model. Test results on the UK-DALE public dataset show that the NILM method proposed in this paper has significant advantages over traditional NILM methods that consider only single-dimensional electrical data features, in terms of load pattern recognition and accuracy in load energy consumption monitoring. This confirms the potential of multi-dimensional feature fusion technology in the application of NILM.

Comparative Analysis of Machine Learning Techniques for Non-Intrusive Load Monitoring

Non-intrusive load monitoring (NILM) has emerged as a pivotal technology in energy management applications by enabling precise monitoring of individual appliance energy consumption without the requirements of intrusive sensors or smart meters. In this technique, the load disaggregation for the individual device is accrued by the recognition of their current signals by employing machine learning (ML) methods. This research paper conducts a comprehensive comparative analysis of various ML techniques applied to NILM, aiming to identify the most effective methodologies for accurate load disaggregation. The study employs a diverse dataset comprising high-resolution electricity consumption data collected from an Estonian household. The ML algorithms, including deep neural networks based on long short-term memory networks (LSTM), extreme gradient boost (XgBoost), logistic regression (LR), and dynamic time warping with K-nearest neighbor (DTW-KNN) are implemented and evaluated for their performance in load disaggregation. Key evaluation metrics such as accuracy, precision, recall, and F1 score are utilized to assess the effectiveness of each technique in capturing the nuanced energy consumption patterns of diverse appliances. Results indicate that the XgBoost-based model demonstrates superior performance in accurately identifying and disaggregating individual loads from aggregated energy consumption data. Insights derived from this research contribute to the optimization of NILM techniques for real-world applications, facilitating enhanced energy efficiency and informed decision-making in smart grid environments.

Research on non-intrusive load identification method based on multi-feature fusion with improved shufflenetv2

Non-intrusive load monitoring is a significant advancement in energy conservation and smart electricity usage as it enables the identification of load status and equipment type without the need for extensive sensing instruments. As one of the important steps of non-intrusive load monitoring, non-intrusive load identification plays a crucial role in the correct identification of electrical appliances. In this paper, a novel approach is presented to amalgamate multiple appliance features into a new identification feature, which is then used for appliance recognition through an improved lightweight deep learning model. This addresses the issue of low accuracy when using a single feature for appliance identification. The core concept involves initially capturing the current data of an appliance during steady state operation and fusing the image features of the appliance encoded by Gramian summation angular field, Gramian difference angular field, and Markov transition field into new recognition features for each appliance using average weighting. Subsequently, a shufflenetv2 lightweight deep learning model based on the squeeze -and-excitation module is used to mine the constructed load feature information for the load classification task. The method is then experimentally validated on the Self-test Dataset, PLAID, and WHITED dataset, resulting in recognition accuracies of 100%, 98.214%, and 99.745%, respectively. These results demonstrate the effectiveness of the proposed method in significantly improving recognition performance compared to the original approach.

A weakly supervised active learning framework for non-intrusive load monitoring

Energy efficiency is at a critical point now with rising energy prices and decarbonisation of the residential sector to meet the global NetZero agenda. Non-Intrusive Load Monitoring is a software-based technique to monitor individual appliances inside a building from a single aggregate meter reading and recent approaches are based on supervised deep learning. Such approaches are affected by practical constraints related to labelled data collection, particularly when a pre-trained model is deployed in an unknown target environment and needs to be adapted to the new data domain. In this case, transfer learning is usually adopted and the end-user is directly involved in the labelling process. Unlike previous literature, we propose a combined weakly supervised and active learning approach to reduce the quantity of data to be labelled and the end user effort in providing the labels. We demonstrate the efficacy of our method comparing it to a transfer learning approach based on weak supervision. Our method reduces the quantity of weakly annotated data required by up to 82.6–98.5% in four target domains while improving the appliance classification performance.

AugLPN-NILM: Augmented lightweight parallel network for NILM embedding attention module over sequence to point

Non-Intrusive Load Monitoring (NILM), also called energy disaggregation, is a feasible strategy for identifying appliance-by-appliance power consumption given the aggregated power demand of electrical loads. A non-intrusive sequence to point load disaggregation parallel model (AugLPN-NILM) is proposed in this paper. Spatial-temporal feature fusion is introduced in our AugLPN to offer more differentiated features, which alleviates the weakness of time domain feature loss, therefore, can boost the performance of energy disaggregation. Most current methods have large performance variations of predicting power consumption for different appliances, the parallel architecture in the AugLPN not only greatly reduces the variations with a relatively small number of parameters to tune, but also accurately predicts the on/off state of appliances across unseen houses or buildings. Furthermore, we present an enhanced attention module, which is applied to activate or restrain features adaptively and especially improve decomposition accuracy of type II appliances. In addition, an effective postprocessing algorithm is proposed to further optimize the disaggregation results. The experiments executed on two publicly accessible datasets, UK-DALE and REDD, demonstrate our proposed AugLPN model’s superiority compared with some existing algorithms in all the identical experimental conditions. The exact code for reproducing the results in this paper is available at https://github.com/linfengYang/AugLPN_NILM.

Low Complexity Energy Disaggregation Algorithm for Non-intrusive Load Monitoring

Energy Disaggregation is the efficient technique to detect the energy profile of individual electric load by disaggregating the overall power consumption. The benefits of energy disaggregation are not only limited to residents but also helps to improve the building efficiency through load identification process. The idea of this paper is to provide a widespread review of energy disaggregation and present a scheme for non-intrusive load monitoring at a building level model. As the scheme involves a simple regression model with better accuracy, it is less complex to achieve energy disaggregation for the real time load identification in educational institution. Various metrics involved in the assessment of model performance like accuracy, standard errors values and computation time were evaluated and demonstrated for validity. A broad investigation is done for building level energy disaggregation and probable elucidations were discussed for future research initiation.

A versatile, low-cost monitoring device suitable for non-intrusive load monitoring research purposes

Energy systems monitoring is a key factor in reducing global energy consumption. Although intrusive approaches can be adopted, Non-Intrusive Load Monitoring (NILM), which targets estimation of individual-appliance consumption from aggregated electricity measurements, is the main choice for retrofitting the plethora of non-smart electrical installations. Contemporary NILM requirements include high-frequency power-line sampling, efficient disaggregation algorithms that can, potentially, run locally, on the monitoring device, to offload the remote fog/cloud infrastructure, and various open datasets to allow the development of such algorithms. Towards all these directions, in this paper, a versatile monitoring device to support NILM research is presented. It offers high sampling frequency, sufficiency of local resources, and has been designed to be used in different environments (residential, commercial, industrial). Actually, it was deployed in all these environments, with multiple copies of it being used in two different industrial facilities for over two years. The developed device’s cost has been kept low, its accuracy has been experimentally verified to be sufficiently high, while its key hardware characteristics, in the context of NILM research, compare favorably with those of other devices in the literature. Along with the device, a residential, high-frequency dataset is also presented and made publicly available.