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1
Ma, Hao, Juncheng Jia, Xinhao Yang, Weipeng Zhu, and Hong Zhang. "MC-NILM: A Multi-Chain Disaggregation Method for NILM." Energies 14, no. 14 (July 18, 2021): 4331. http://dx.doi.org/10.3390/en14144331.
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Non-intrusive load monitoring (NILM) is an approach that helps residents obtain detailed information about household electricity consumption and has gradually become a research focus in recent years. Most of the existing algorithms on NILM build energy disaggregation models independently for an individual appliance while neglecting the relation among them. For this situation, this article proposes a multi-chain disaggregation method for NILM (MC-NILM). MC-NILM integrates the models generated by existing algorithms and considers the relation among these models to improve the performance of energy disaggregation. Given the high time complexity of searching for the optimal MC-NILM structure, this article proposes two methods to reduce the time complexity, the k-length chain method and the graph-based chain generation method. Finally, we use the Dataport and UK-DALE datasets to evaluate the feasibility, effectiveness, and generality of the MC-NILM.
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Bousbiat, Hafsa, Yassine Himeur, Iraklis Varlamis, Faycal Bensaali, and Abbes Amira. "Neural Load Disaggregation: Meta-Analysis, Federated Learning and Beyond." Energies 16, no. 2 (January 16, 2023): 991. http://dx.doi.org/10.3390/en16020991.
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Non-intrusive load monitoring (NILM) techniques are central techniques to achieve the energy sustainability goals through the identification of operating appliances in the residential and industrial sectors, potentially leading to increased rates of energy savings. NILM received significant attention in the last decade, reflected by the number of contributions and systematic reviews published yearly. In this regard, the current paper provides a meta-analysis summarising existing NILM reviews to identify widely acknowledged findings concerning NILM scholarship in general and neural NILM algorithms in particular. In addition, this paper emphasizes federated neural NILM, receiving increasing attention due to its ability to preserve end-users’ privacy. Typically, by combining several locally trained models, federated learning has excellent potential to train NILM models locally without communicating sensitive data with cloud servers. Thus, the second part of the current paper provides a summary of recent federated NILM frameworks with a focus on the main contributions of each framework and the achieved performance. Furthermore, we identify the non-availability of proper toolkits enabling easy experimentation with federated neural NILM as a primary barrier in the field. Thus, we extend existing toolkits with a federated component, made publicly available and conduct experiments on the REFIT energy dataset considering four different scenarios.
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Kaselimi, Maria, Eftychios Protopapadakis, Athanasios Voulodimos, Nikolaos Doulamis, and Anastasios Doulamis. "Towards Trustworthy Energy Disaggregation: A Review of Challenges, Methods, and Perspectives for Non-Intrusive Load Monitoring." Sensors 22, no. 15 (August 5, 2022): 5872. http://dx.doi.org/10.3390/s22155872.
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Non-intrusive load monitoring (NILM) is the task of disaggregating the total power consumption into its individual sub-components. Over the years, signal processing and machine learning algorithms have been combined to achieve this. Many publications and extensive research works are performed on energy disaggregation or NILM for the state-of-the-art methods to reach the desired performance. The initial interest of the scientific community to formulate and describe mathematically the NILM problem using machine learning tools has now shifted into a more practical NILM. Currently, we are in the mature NILM period where there is an attempt for NILM to be applied in real-life application scenarios. Thus, the complexity of the algorithms, transferability, reliability, practicality, and, in general, trustworthiness are the main issues of interest. This review narrows the gap between the early immature NILM era and the mature one. In particular, the paper provides a comprehensive literature review of the NILM methods for residential appliances only. The paper analyzes, summarizes, and presents the outcomes of a large number of recently published scholarly articles. Furthermore, the paper discusses the highlights of these methods and introduces the research dilemmas that should be taken into consideration by researchers to apply NILM methods. Finally, we show the need for transferring the traditional disaggregation models into a practical and trustworthy framework.
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Shedge, Dr D. K., Pradnya Jadhav, Shantanu Jagtap, and Arbaz Naddaf. "IoT Based Approach for Load Controlling and Smart Home Security Systems." International Journal for Research in Applied Science and Engineering Technology 10, no. 6 (June 30, 2022): 367–70. http://dx.doi.org/10.22214/ijraset.2022.43696.
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Abstract: Appliance load monitoring in smart homes has been gaining importance due to its significant advantages in achieving an energy efficient smart grid. The methods to manage such processes can be classified into hardware-based methods, including intrusive load monitoring (ILM) and software-based methods referring to non-intrusive load monitoring (NILM). ILM is based on low-end meter devices attached to home appliances in opposition to NILM techniques, where only a single point of sensing is needed. Although ILM solutions can be relatively expensive, they provide higher efficiency and reliability than NILMs. Moreover, future solutions are expected to be hybrid, combining the benefits of NILM along with individual power measurement by smart plugs and smart appliances. This paper proposes a novel ILM approach for load monitoring that aims to develop an activity recognition system based on IoT architecture. The proposed IoT architecture consists of the appliances layer, perception layer, communication network layer, middleware layer, and application layer. The main function of the appliance recognition module is to label sensor data and allow the implementation of different home applications. Keywords: Smart Homes , Home Security, Load Controlling .
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Bousbiat, Hafsa, Gerhard Leitner, and Wilfried Elmenreich. "Ageing Safely in the Digital Era: A New Unobtrusive Activity Monitoring Framework Leveraging on Daily Interactions with Hand-Operated Appliances." Sensors 22, no. 4 (February 9, 2022): 1322. http://dx.doi.org/10.3390/s22041322.
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Supporting the elderly to maintain their independence, safety, and well-being through Active Assisted Living (AAL) technologies, is gaining increasing momentum. Recently, Non-intrusive Load Monitoring (NILM) approaches have become the focus of these technologies due to their non-intrusiveness and reduced price. Whilst some research has been carried out in this respect; it still is challenging to design systems considering the heterogeneity and complexity of daily routines. Furthermore, scholars gave little attention to evaluating recent deep NILM models in AAL applications. We suggest a new interactive framework for activity monitoring based on custom user-profiles and deep NILM models to address these gaps. During evaluation, we consider four different deep NILM models. The proposed contribution is further assessed on two households from the REFIT dataset for a period of one year, including the influence of NILM on activity monitoring. To the best of our knowledge, the current study is the first to quantify the error propagated by a NILM model on the performance of an AAL solution. The results achieved are promising, particularly when considering the UNET-NILM model, a multi-task convolutional neural network for load disaggregation, that revealed a deterioration of only 10% in the f1-measure of the framework’s overall performance.
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Wilhelm, Sebastian, and Jakob Kasbauer. "Exploiting Smart Meter Power Consumption Measurements for Human Activity Recognition (HAR) with a Motif-Detection-Based Non-Intrusive Load Monitoring (NILM) Approach." Sensors 21, no. 23 (December 1, 2021): 8036. http://dx.doi.org/10.3390/s21238036.
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Numerous approaches exist for disaggregating power consumption data, referred to as non-intrusive load monitoring (NILM). Whereas NILM is primarily used for energy monitoring, we intend to disaggregate a household’s power consumption to detect human activity in the residence. Therefore, this paper presents a novel approach for NILM, which uses pattern recognition on the raw power waveform of the smart meter measurements to recognize individual household appliance actions. The presented NILM approach is capable of (near) real-time appliance action detection in a streaming setting, using edge computing. It is unique in our approach that we quantify the disaggregating uncertainty using continuous pattern correlation instead of binary device activity states. Further, we outline using the disaggregated appliance activity data for human activity recognition (HAR). To evaluate our approach, we use a dataset collected from actual households. We show that the developed NILM approach works, and the disaggregation quality depends on the pattern selection and the appliance type. In summary, we demonstrate that it is possible to detect human activity within the residence using a motif-detection-based NILM approach applied to smart meter measurements.
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Rafati, Amir, Hamid Reza Shaker, and Saman Ghahghahzadeh. "Fault Detection and Efficiency Assessment for HVAC Systems Using Non-Intrusive Load Monitoring: A Review." Energies 15, no. 1 (January 4, 2022): 341. http://dx.doi.org/10.3390/en15010341.
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Heat, ventilation, and air conditioning (HVAC) systems are some of the most energy-intensive equipment in buildings and their faulty or inefficient operation can significantly increase energy waste. Non-Intrusive Load Monitoring (NILM), which is a software-based tool, has been a popular research area over the last few decades. NILM can play an important role in providing future energy efficiency feedback and developing fault detection and diagnosis (FDD) tools in smart buildings. Therefore, the review of NILM-based methods for FDD and the energy efficiency (EE) assessment of HVACs can be beneficial for users as well as buildings and facilities operators. To the best of the authors’ knowledge, this paper is the first review paper on the application of NILM techniques in these areas and highlights their effectiveness and limitations. This review shows that even though NILM could be successfully implemented for FDD and the EE evaluation of HVACs, and enhance the performance of these techniques, there are many research opportunities to improve or develop NILM-based FDD methods to deal with real-world challenges. These challenges and future research works are also discussed in-depth.
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Machlev, Ram, Juri Belikov, Yuval Beck, and Yoash Levron. "MO-NILM: A multi-objective evolutionary algorithm for NILM classification." Energy and Buildings 199 (September 2019): 134–44. http://dx.doi.org/10.1016/j.enbuild.2019.06.046.
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Biansoongnern, Somchai, and Boonyang Plangklang. "An Alternative Low-Cost Embedded NILM System for Household Energy Conservation with a Low Sampling Rate." Symmetry 14, no. 2 (January 29, 2022): 279. http://dx.doi.org/10.3390/sym14020279.
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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.
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Kee, Keh-Kim, Yun Seng Lim, Jianhui Wong, and Kein-Huat Chua. "Impact of NILM-based energy efficiency on environmental degradation and kuznets hypothesis analysis." Bulletin of Electrical Engineering and Informatics 11, no. 1 (February 1, 2022): 1–8. http://dx.doi.org/10.11591/eei.v11i1.3136.
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Nonintrusive load monitoring (NILM) breaks down the aggregated electrical consumption data into individual appliances. The feedback of disaggregated data to the consumers enables awareness and behaviour change to conserve electricity, consequently reducing CO2 emissions to the environment. However, the limited literature regarding the impact of NILM and Kuznets hypothesis (EKC) analysis on CO2 emissions reduction has restricted policymakers in developing effective mitigation measures. This work aims to assess the impact of NILM-based based energy efficiency (EE) on environmental improvement. The combined approach of scenario simulation and EKC analysis was adopted to gauge the effectiveness of NILM that leads to sustainable development. The monotonically increase relationship between environmental degradation and economic growth in Malaysia without peaking beyond 2030 implies that the current mitigation measures and policies imposed may not effectively cope with the future power demands for sustainable development. NILM-based EE measures could be a great potential for reducing CO2 emissions by 10.2%. The inverted-U curves and reduced turning points of environmental degradation from the income level of USD 20,063.36 to USD 16,305.19. Therefore, NILM approach can accelerate sustainable development with lower environmental deterioration. The work may beneficial to policymakers to analyse the impact and effectiveness of mitigation measures quantitatively.
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Desai, Sanket, Rabei Alhadad, Abdun Mahmood, Naveen Chilamkurti, and Seungmin Rho. "Multi-State Energy Classifier to Evaluate the Performance of the NILM Algorithm." Sensors 19, no. 23 (November 28, 2019): 5236. http://dx.doi.org/10.3390/s19235236.
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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.
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Pujić, Dea, Nikola Tomašević, and Marko Batić. "A Semi-Supervised Approach for Improving Generalization in Non-Intrusive Load Monitoring." Sensors 23, no. 3 (January 28, 2023): 1444. http://dx.doi.org/10.3390/s23031444.
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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.
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Renaux, Douglas Paulo Bertrand, Fabiana Pottker, Hellen Cristina Ancelmo, André Eugenio Lazzaretti, Carlos Raiumundo Erig Lima, Robson Ribeiro Linhares, Elder Oroski, et al. "A Dataset for Non-Intrusive Load Monitoring: Design and Implementation." Energies 13, no. 20 (October 15, 2020): 5371. http://dx.doi.org/10.3390/en13205371.
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A NILM dataset is a valuable tool in the development of Non-Intrusive Load Monitoring techniques, as it provides a means of evaluation of novel techniques and algorithms, as well as for benchmarking. The figure of merit of a NILM dataset includes characteristics such as the sampling frequency of the voltage, current, or power, the availability of indications (ground-truth) of load events during recording, the variety and representativeness of the loads, and the variety of situations these loads are subject to. Considering such aspects, the proposed LIT-Dataset was designed, populated, evaluated, and made publicly available to support NILM development. Among the distinct features of the LIT-Dataset is the labeling of the load events at sample level resolution and with an accuracy and precision better than 5 ms. The availability of such precise timing information, which also includes the identification of the load and the sort of power event, is an essential requirement both for the evaluation of NILM algorithms and techniques, as well as for the training of NILM systems, particularly those based on Machine Learning.
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Kee, Keh-Kim, Yun Seng Lim, Jianhui Wong, and Kein Huat Chua. "Impact of nonintrusive load monitoring on CO2 emissions in Malaysia." Bulletin of Electrical Engineering and Informatics 10, no. 4 (August 1, 2021): 1803–10. http://dx.doi.org/10.11591/eei.v10i4.2979.
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Nonintrusive load monitoring (NILM) based energy efficiency can conserve electricity by creating awareness with the behaviour change and shrinking CO2 emissions to the environment. However, the lack of effective models and strategies is problematic for policymakers to forecast quantitatively CO2 emissions. This paper aims to study the impact of NILM on CO2 emissions in Malaysia. Firstly, the predictive models were established based on Malaysia open data from 1996 to 2018. After that, scenario simulations were conducted to predict CO2 emissions and NILM impact on environmental degradation in 2019-2030. The results revealed that a 12% reduction in electricity consumption due to NILM could contribute to a 10.2% shrinkage of the total CO2 emissions. The result also statistically confirmed Malaysia to achieve a 45% reduction of CO2 intensity in 2030. With NILM, the carbon reduction can be further enhanced to 60.2%. The outcomes provide valuable references and supporting evidence for policymakers in planning effective carbon emission control policies and energy efficiency measures. The work can be extended by developing a decision support system and user interfaces access via the cloud.
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Ruano, Antonio, Alvaro Hernandez, Jesus Ureña, Maria Ruano, and Juan Garcia. "NILM Techniques for Intelligent Home Energy Management and Ambient Assisted Living: A Review." Energies 12, no. 11 (June 10, 2019): 2203. http://dx.doi.org/10.3390/en12112203.
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The ongoing deployment of smart meters and different commercial devices has made electricity disaggregation feasible in buildings and households, based on a single measure of the current and, sometimes, of the voltage. Energy disaggregation is intended to separate the total power consumption into specific appliance loads, which can be achieved by applying Non-Intrusive Load Monitoring (NILM) techniques with a minimum invasion of privacy. NILM techniques are becoming more and more widespread in recent years, as a consequence of the interest companies and consumers have in efficient energy consumption and management. This work presents a detailed review of NILM methods, focusing particularly on recent proposals and their applications, particularly in the areas of Home Energy Management Systems (HEMS) and Ambient Assisted Living (AAL), where the ability to determine the on/off status of certain devices can provide key information for making further decisions. As well as complementing previous reviews on the NILM field and providing a discussion of the applications of NILM in HEMS and AAL, this paper provides guidelines for future research in these topics.
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Du, Zhekai, Jingjing Li, Lei Zhu, Ke Lu, and Heng Tao Shen. "Adversarial Energy Disaggregation." ACM/IMS Transactions on Data Science 2, no. 4 (November 30, 2021): 1–16. http://dx.doi.org/10.1145/3477301.
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Energy disaggregation, also known as non-intrusive load monitoring (NILM), challenges the problem of separating the whole-home electricity usage into appliance-specific individual consumptions, which is a typical application of data analysis. NILM aims to help households understand how the energy is used and consequently tell them how to effectively manage the energy, thus allowing energy efficiency, which is considered as one of the twin pillars of sustainable energy policy (i.e., energy efficiency and renewable energy). Although NILM is unidentifiable, it is widely believed that the NILM problem can be addressed by data science. Most of the existing approaches address the energy disaggregation problem by conventional techniques such as sparse coding, non-negative matrix factorization, and the hidden Markov model. Recent advances reveal that deep neural networks (DNNs) can get favorable performance for NILM since DNNs can inherently learn the discriminative signatures of the different appliances. In this article, we propose a novel method named adversarial energy disaggregation based on DNNs. We introduce the idea of adversarial learning into NILM, which is new for the energy disaggregation task. Our method trains a generator and multiple discriminators via an adversarial fashion. The proposed method not only learns shared representations for different appliances but captures the specific multimode structures of each appliance. Extensive experiments on real-world datasets verify that our method can achieve new state-of-the-art performance.
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Feng, Ruijue, Zhidong Wang, Zhifeng Li, Haixia Ma, Ruiyuan Chen, Zhengbin Pu, Ziqiu Chen, and Xianyu Zeng. "A Hybrid Cryptography Scheme for NILM Data Security." Electronics 9, no. 7 (July 10, 2020): 1128. http://dx.doi.org/10.3390/electronics9071128.
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Using fine-grained data analysis, non-invasive load monitoring (NILM) can reveal the detail of electricity customers’ habits, which is helpful in the improvement of refined management and better user experience. However, the possibility of electricity customers’ privacy leak is also gradually increasing, and the security of NILM data has become a priority problem to be solved. To protect the privacy disclosure of NILM data, this paper analyzes the NILM privacy leak problems and ways in which information leak occurs faced by NILM data. On the basis of the comprehensive survey of cryptographic algorithms to choose the most appropriate data security method for NILM, a hybrid cryptography scheme was proposed to protect the data security. In the scheme, symmetric algorithm AES (Advanced Encryption Standard) was used to encrypt data for high efficiency, and asymmetric algorithm RSA (Rivest-Shamir-Adleman) was used to encrypt AES key for identity authentication. The classical algorithm HMAC-SHA1 (Hash Message Authentication Codes-Secure Hash Algorithm 1) was further developed to guarantee the integrity of data. By transplanting the algorithm into STM32 MCU (STMicroelectronics 32 bit Micro Controller Unit) for performance test and using Visual studio 2017 + QT tools to develop the test interface, one optimal operation mode was selected for the scheme. At the same time, the effectiveness of the scheme was verified, and the scheme computing cost depended on the efficiency of encryption and decryption, or signature and verification of the RSA algorithm.
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Shin, Changho, Seungeun Rho, Hyoseop Lee, and Wonjong Rhee. "Data Requirements for Applying Machine Learning to Energy Disaggregation." Energies 12, no. 9 (May 5, 2019): 1696. http://dx.doi.org/10.3390/en12091696.
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Energy disaggregation, or nonintrusive load monitoring (NILM), is a technology for separating a household’s aggregate electricity consumption information. Although this technology was developed in 1992, its practical usage and mass deployment have been rather limited, possibly because the commonly used datasets are not adequate for NILM research. In this study, we report the findings from a newly collected dataset that contains 10 Hz sampling data for 58 houses. The dataset not only contains the aggregate measurements, but also individual appliance measurements for three types of appliances. By applying three classification algorithms (vanilla DNN (Deep Neural Network), ML (Machine Learning) with feature engineering, and CNN (Convolutional Neural Network) with hyper-parameter tuning) and a recent regression algorithm (Subtask Gated Network) to the new dataset, we show that NILM performance can be significantly limited when the data sampling rate is too low or when the number of distinct houses in the dataset is too small. The well-known NILM datasets that are popular in the research community do not meet these requirements. Our results indicate that higher quality datasets should be used to expedite the progress of NILM research.
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Tri Atmaja, Sigit, and Abdul Halim. "Steady State Modification Method Based On Backpropagation Neural Network For Non-Intrusive Load Monitoring (NILM)." MATEC Web of Conferences 218 (2018): 02013. http://dx.doi.org/10.1051/matecconf/201821802013.
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Household electric power sector is highlighted as one of significant contributors to national energy consumption. To reduce electric energy usage in this sector, a technique called Non-Intrusive Load Monitoring (NILM) has been developed recently. NILM is a load disaggregating and monitoring tool that can be used to identify the daily usage behavior of individual electric appliance. Different to conventional method, NILM promises the reduction of sensor deployment significantly. NILM commonly uses either transient or steady state signal. Based on load/appliance signal condition, many NILM’s research results have been published. In this paper, steady state modification method of backpropagation neural network (NN) is applied for developing NILM. We use steady state signal to disaggregate the sum of load power signal. In the proposed method, NN is explored for feature extraction of electric power consumption of individual appliance. The presented method is powerful for load power signal which has almost same value. To verify the effectiveness of proposed method, data provided by tracebase.org has been used. The presented method can be applied for local data. It is obvious from simulation results that the proposed method could improve the recognition rate of appliances until 100 %.
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Peng, Ce, Guoying Lin, Shaopeng Zhai, Yi Ding, and Guangyu He. "Non-Intrusive Load Monitoring via Deep Learning Based User Model and Appliance Group Model." Energies 13, no. 21 (October 28, 2020): 5629. http://dx.doi.org/10.3390/en13215629.
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Non-Intrusive Load Monitoring (NILM) increases awareness on user energy usage patterns. In this paper, an efficient and highly accurate NILM method is proposed featuring condensed representation, super-state and fusion of two deep learning based models. Condensed representation helps the two models perform more efficiently and preserve longer-term information, while super-state helps the model to learn correlations between appliances. The first model is a deep user model that learns user appliances usage patterns to predict the next appliance usage behavior based on past behaviors by capturing the dynamics of user behaviors history and appliances usage habits. The second model is a deep appliance group model that learns the characteristics of appliances with temporal and electrical information. These two models are then fused to perform NILM. The case study based on REFIT datasets demonstrates that the proposed NILM method outperforms two state-of-the-art benchmark methods.
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Kim, Jin-Gyeom, and Bowon Lee. "Appliance Classification by Power Signal Analysis Based on Multi-Feature Combination Multi-Layer LSTM." Energies 12, no. 14 (July 21, 2019): 2804. http://dx.doi.org/10.3390/en12142804.
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The imbalance of power supply and demand is an important problem to solve in power industry and Non Intrusive Load Monitoring (NILM) is one of the representative technologies for power demand management. The most critical factor to the NILM is the performance of the classifier among the last steps of the overall NILM operation, and therefore improving the performance of the NILM classifier is an important issue. This paper proposes a new architecture based on the RNN to overcome the limitations of existing classification algorithms and to improve the performance of the NILM classifier. The proposed model, called Multi-Feature Combination Multi-Layer Long Short-Term Memory (MFC-ML-LSTM), adapts various feature extraction techniques that are commonly used for audio signal processing to power signals. It uses Multi-Feature Combination (MFC) for generating the modified input data for improving the classification performance and adopts Multi-Layer LSTM (ML-LSTM) network as the classification model for further improvements. Experimental results show that the proposed method achieves the accuracy and the F1-score for appliance classification with the ranges of 95–100% and 84–100% that are superior to the existing methods based on the Gated Recurrent Unit (GRU) or a single-layer LSTM.
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Zhao, Qiang, Yao Xu, Zhenfan Wei, and Yinghua Han. "Non-Intrusive Load Monitoring Based on Deep Pairwise-Supervised Hashing to Detect Unidentified Appliances." Processes 9, no. 3 (March 11, 2021): 505. http://dx.doi.org/10.3390/pr9030505.
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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.
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Lu, Jiangang, Ruifeng Zhao, Bo Liu, Zhiwen Yu, Jinjiang Zhang, and Zhanqiang Xu. "An Overview of Non-Intrusive Load Monitoring Based on V-I Trajectory Signature." Energies 16, no. 2 (January 13, 2023): 939. http://dx.doi.org/10.3390/en16020939.
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Non-intrusive load monitoring (NILM) can obtain fine-grained electricity consumption information of each appliance by analyzing the voltage and current data measured at a single point on the bus, which is of great significance for promoting and improving the efficiency and sustainability of the power grid and enhancing the energy efficiency of users. NILM mainly includes data collection and preprocessing, event detection, feature extraction, and appliance identification. One of the most critical steps in NILM is signature extraction, which is the basis for all algorithms to achieve good state detection and energy disaggregation. With the generalization of machine learning algorithms, different algorithms have also been used to extract unique signatures of appliances. Recently, the development and deployment of the voltage–current (V-I) trajectory signatures applied for appliance identification motivated us to present a comprehensive review in this domain. The V-I trajectory signatures have the potential to be an intermediate domain between computer vision and NILM. By identifying the V-I trajectory, we can detect the operating state of the appliance. We also summarize existing papers based on V-I trajectories and look forward to future research directions that help to promote the field’s development.
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Lin, Yu-Hsiu, and Yu-Chen Hu. "Electrical Energy Management Based on a Hybrid Artificial Neural Network-Particle Swarm Optimization-Integrated Two-Stage Non-Intrusive Load Monitoring Process in Smart Homes." Processes 6, no. 12 (November 23, 2018): 236. http://dx.doi.org/10.3390/pr6120236.
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Concerning electrical energy used in today’s modern society, electrical energy demands requested from downstream sectors in a smart grid are continuously increasing. One way to meet the electrical demands requested is to monitor and manage industrial, commercial, as well as residential electrical appliances efficiently in response to Demand Response (DR) programs for Demand-Side Management (DSM). Monitoring and managing electrical appliances that consume electrical energy in fields of interest can be realized through use of Energy Management Systems (EMS) with Non-Intrusive Load Monitoring (NILM). This paper presents an Internet of Things (IoT)-oriented Home EMS (HEMS). Also, a novel hybrid Artificial Neural Network-Particle Swarm Optimization (ANN-PSO)-integrated NILM approach is proposed and used to model and identify electrical appliances for DSM in the HEMS. ANN can be applied in NILM as a load identification task. Nevertheless, the performance of ANN used for load identification depends on three principal design factors: The network topology designed, the type of activation functions chosen, and the training algorithm adopted. As a result, PSO is conducted and used to incorporate meta-heuristics with ANN considering the three principal design factors relating to an ANN design. The HEMS with the novel hybrid ANN-PSO-integrated NILM proposed in this paper was deployed and evaluated in a realistic residential house environment. As the experimentation reported in this paper shows, the presented HEMS utilizing the proposed novel hybrid ANN-PSO-integrated NILM to model and identify monitored electrical appliances is feasible and workable, with an overall classification rate of 91.67% in load classification for DSM.
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Athanasiadis, Christos, Dimitrios Doukas, Theofilos Papadopoulos, and Antonios Chrysopoulos. "A Scalable Real-Time Non-Intrusive Load Monitoring System for the Estimation of Household Appliance Power Consumption." Energies 14, no. 3 (February 1, 2021): 767. http://dx.doi.org/10.3390/en14030767.
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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.
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Hur, Cheong-Hwan, Han-Eum Lee, Young-Joo Kim, and Sang-Gil Kang. "Semi-Supervised Domain Adaptation for Multi-Label Classification on Nonintrusive Load Monitoring." Sensors 22, no. 15 (August 4, 2022): 5838. http://dx.doi.org/10.3390/s22155838.
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Nonintrusive load monitoring (NILM) is a technology that analyzes the load consumption and usage of an appliance from the total load. NILM is becoming increasingly important because residential and commercial power consumption account for about 60% of global energy consumption. Deep neural network-based NILM studies have increased rapidly as hardware computation costs have decreased. A significant amount of labeled data is required to train deep neural networks. However, installing smart meters on each appliance of all households for data collection requires the cost of geometric series. Therefore, it is urgent to detect whether the appliance is used from the total load without installing a separate smart meter. In other words, domain adaptation research, which can interpret the huge complexity of data and generalize information from various environments, has become a major challenge for NILM. In this research, we optimize domain adaptation by employing techniques such as robust knowledge distillation based on teacher–student structure, reduced complexity of feature distribution based on gkMMD, TCN-based feature extraction, and pseudo-labeling-based domain stabilization. In the experiments, we down-sample the UK-DALE and REDD datasets as in the real environment, and then verify the proposed model in various cases and discuss the results.
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de Aguiar, Everton Luiz, André Eugenio Lazzaretti, Bruna Machado Mulinari, and Daniel Rodrigues Pipa. "Scattering Transform for Classification in Non-Intrusive Load Monitoring." Energies 14, no. 20 (October 18, 2021): 6796. http://dx.doi.org/10.3390/en14206796.
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Nonintrusive Load Monitoring (NILM) uses computational methods to disaggregate and classify electrical appliances signals. The classification is usually based on the power signatures of the appliances obtained by a feature extractor. State-of-the-art results were obtained extracting NILM features with convolutional neural networks (CNN). However, it depends on the training process with large datasets or data augmentation strategies. In this paper, we propose a feature extraction strategy for NILM using the Scattering Transform (ST). The ST is a convolutional network analogous to CNN. Nevertheless, it does not need a training process in the feature extraction stage, and the filter coefficients are analytically determined (not empirically, like CNN). We perform tests with the proposed method on different publicly available datasets and compare the results with state-of-the-art deep learning-based and traditional approaches (including wavelet transform and V-I representations). The results show that ST classification accuracy is more robust in terms of waveform parameters, such as signal length, sampling frequency, and event location. Besides, ST overcame the state-of-the-art techniques for single and aggregated loads (accuracies above 99% for all evaluated datasets), in different training scenarios with single and aggregated loads, indicating its feasibility in practical NILM scenarios.
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Mukhtaruddin, Azharudin, Fakroul Ridzuan Hashim, Mat Kamil Awang, Husin Mamat, and Hafizi Zakaria. "Development of site-specific non-intrusive load monitoring for maximum demand control." Indonesian Journal of Electrical Engineering and Computer Science 23, no. 3 (September 1, 2021): 1814. http://dx.doi.org/10.11591/ijeecs.v23.i3.pp1814-1824.
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Demand-side load management (DSM) requires greater role-play by end-users. To lower the investment for this load management concept, non-intrusive load management (NILM) was introduced as the solution. However, most of the mathematical techniques used in NILM are complex. This may hinder users from actively take part in the energy management effort. This paper explores the possibilities of applying change point detection techniques with help of differentiation and application of filters. These filters were selected strictly based on site-specific conditions. As part of the NILM implementation, a new and practical technique was developed for this paper. It was found that the developed technique, despite its simplicity it can identify the electrical equipment which added the significant load demand. The performance of the technique was found to be satisfactory as compared to results reported by other researchers.
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de Souza, Wesley Angelino, Fernando Deluno Garcia, Fernando Pinhabel Marafão, Luiz Carlos Pereira da Silva, and Marcelo Godoy Simões. "Load Disaggregation Using Microscopic Power Features and Pattern Recognition." Energies 12, no. 14 (July 10, 2019): 2641. http://dx.doi.org/10.3390/en12142641.
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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.
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Selvi, Dr N. Thamarai, Dr Revathi Shree R, and Dr Prakashiny S. "Cell pattern abnormalities in cervical pap smear in correlation with age and demography at a Tertiary care centre." Tropical Journal of Pathology and Microbiology 7, no. 1 (February 20, 2021): 33–39. http://dx.doi.org/10.17511/jopm.2021.i01.05.
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Introduction: Carcinoma Cervix is common all around the globe and ranked third amidst allmalignancies among women. The cervical mucosa undergoes morphologic variation with age andpractising cytopathologists is aware of these difference to make an accurate diagnosis. This studyaimed to detect abnormal cervical epithelial cell patterns in a rural population and compare lesionsor abnormal cell patterns among different age groups. Materials and Methods: This is a cross-sectional, descriptive study conducted in a tertiary care centre at the Department of Pathology over6 months. 408 women were included in the study. Data were entered in Microsoft Excel andanalyzed in SPSS software. Results: Out of 408 women included in the study, the most commonage group of the presentation was 31 to 40 years (36%), followed by 20 to 30 years (24%). NILM-Inflammatory was the most common finding (50%), followed by NILM (36%). The most commonsymptoms of presentation were Menstrual abnormalities (21%), White discharge and pruritus(18%). Findings in younger women were most commonly NILM-Inflammatory & NILM whereas in thepost-menopausal age group, ASCUS, LSIL & HSIL. Conclusion: All women above 30 years of ageshould undergo routine cervical cancer screening, and should continue screening even in theperimenopausal and postmenopausal age. Most women who visited the gynaecology OPD were notaware of cervical cancer screening. Hence the general population has to be educated about thebenefits of pap smear test through medical camps and awareness programs.
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Lin, Yu-Hsiu. "A Parallel Evolutionary Computing-Embodied Artificial Neural Network Applied to Non-Intrusive Load Monitoring for Demand-Side Management in a Smart Home: Towards Deep Learning." Sensors 20, no. 6 (March 16, 2020): 1649. http://dx.doi.org/10.3390/s20061649.
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Non-intrusive load monitoring (NILM) is a cost-effective approach that electrical appliances are identified from aggregated whole-field electrical signals, according to their extracted electrical characteristics, with no need to intrusively deploy smart power meters (power plugs) installed for individual monitored electrical appliances in a practical field of interest. This work addresses NILM by a parallel Genetic Algorithm (GA)-embodied Artificial Neural Network (ANN) for Demand-Side Management (DSM) in a smart home. An ANN’s performance in terms of classification accuracy depends on its training algorithm. Additionally, training an ANN/deep NN learning from massive training samples is extremely computationally intensive. Therefore, in this work, a parallel GA has been conducted and used to integrate meta-heuristics (evolutionary computing) with an ANN (neurocomputing) considering its evolution in a parallel execution relating to load disaggregation in a Home Energy Management System (HEMS) deployed in a real residential field. The parallel GA that involves iterations to excessively cost its execution time for evolving an ANN learning model from massive training samples to NILM in the HEMS and works in a divide-and-conquer manner that can exploit massively parallel computing for evolving an ANN and, thus, reduce execution time drastically. This work confirms the feasibility and effectiveness of the parallel GA-embodied ANN applied to NILM in the HEMS for DSM.
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Sahrane, Selim, and Mourad Haddadi. "Near Real-Time Low Frequency Load Disaggregation." ENP Engineering Science Journal 1, no. 2 (December 31, 2021): 50–54. http://dx.doi.org/10.53907/enpesj.v1i2.15.
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Device-level power consumption information can lead to considerable energy savings. Smart meters are being adopted in several countries, but they are only capable of measuring the total power consumption. NonIntrusive Load Monitoring (NILM) aims to infer the power consumption of individual electrical loads by analyzing the aggregate power signal taken from a single-point measurement. Most existing NILM solutions are offline methods that do not allow the end-user to get real-time feedback on his energy consumption. In this paper, we present a near real-time NILM solution based on multi-label classification and multi-output regression. We use the multi-label classifier to predict the state of each load and use the multi-output regressor to estimate the disaggregated active power consumptions. We test our method using a publically available dataset of real power measurements. Performance results show that the proposed near real-time method can accurately estimate the energy consumption of the targeted loads with an average relative energy error of 1.55 %.
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Jiang, Lei, Jiaming Li, Suhuai Luo, Sam West, and Glenn Platt. "Power Load Event Detection and Classification Based on Edge Symbol Analysis and Support Vector Machine." Applied Computational Intelligence and Soft Computing 2012 (2012): 1–10. http://dx.doi.org/10.1155/2012/742461.
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Energy signature analysis of power appliance is the core of nonintrusive load monitoring (NILM) where the detailed data of the appliances used in houses are obtained by analyzing changes in the voltage and current. This paper focuses on developing an automatic power load event detection and appliance classification based on machine learning. In power load event detection, the paper presents a new transient detection algorithm. By turn-on and turn-off transient waveforms analysis, it can accurately detect the edge point when a device is switched on or switched off. The proposed load classification technique can identify different power appliances with improved recognition accuracy and computational speed. The load classification method is composed of two processes including frequency feature analysis and support vector machine. The experimental results indicated that the incorporation of the new edge detection and turn-on and turn-off transient signature analysis into NILM revealed more information than traditional NILM methods. The load classification method has achieved more than ninety percent recognition rate.
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Yu, Jinying, Yuchen Gao, Yuxin Wu, Dian Jiao, Chang Su, and Xin Wu. "Non-Intrusive Load Disaggregation by Linear Classifier Group Considering Multi-Feature Integration." Applied Sciences 9, no. 17 (August 30, 2019): 3558. http://dx.doi.org/10.3390/app9173558.
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Non-intrusive load monitoring (NILM) is a core technology for demand response (DR) and energy conservation services. Traditional NILM methods are rarely combined with practical applications, and most studies aim to disaggregate the whole loads in a household, which leads to low identification accuracy. In this method, the event detection method is used to obtain the switching event sets of all loads, and the power consumption curves of independent unknown electrical appliances in a period are disaggregated by utilizing comprehensive features. A linear discriminant classifier group based on multi-feature global similarity is used for load identification. The uniqueness of our algorithm is that it designs an event detector based on steady-state segmentation and a linear discriminant classifier group based on multi-feature global similarity. The simulation is carried out on an open source data set. The results demonstrate the effectiveness and high accuracy of the multi-feature integrated classification (MFIC) algorithm by using the state-of-the-art NILM methods as benchmarks.
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Çavdar, İsmail Hakkı, and Vahit Feryad. "Efficient Design of Energy Disaggregation Model with BERT-NILM Trained by AdaX Optimization Method for Smart Grid." Energies 14, no. 15 (July 30, 2021): 4649. http://dx.doi.org/10.3390/en14154649.
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One of the basic conditions for the successful implementation of energy demand-side management (EDM) in smart grids is the monitoring of different loads with an electrical load monitoring system. Energy and sustainability concerns present a multitude of issues that can be addressed using approaches of data mining and machine learning. However, resolving such problems due to the lack of publicly available datasets is cumbersome. In this study, we first designed an efficient energy disaggregation (ED) model and evaluated it on the basis of publicly available benchmark data from the Residential Energy Disaggregation Dataset (REDD), and then we aimed to advance ED research in smart grids using the Turkey Electrical Appliances Dataset (TEAD) containing household electricity usage data. In addition, the TEAD was evaluated using the proposed ED model tested with benchmark REDD data. The Internet of things (IoT) architecture with sensors and Node-Red software installations were established to collect data in the research. In the context of smart metering, a nonintrusive load monitoring (NILM) model was designed to classify household appliances according to TEAD data. A highly accurate supervised ED is introduced, which was designed to raise awareness to customers and generate feedback by demand without the need for smart sensors. It is also cost-effective, maintainable, and easy to install, it does not require much space, and it can be trained to monitor multiple devices. We propose an efficient BERT-NILM tuned by new adaptive gradient descent with exponential long-term memory (Adax), using a deep learning (DL) architecture based on bidirectional encoder representations from transformers (BERT). In this paper, an improved training function was designed specifically for tuning of NILM neural networks. We adapted the Adax optimization technique to the ED field and learned the sequence-to-sequence patterns. With the updated training function, BERT-NILM outperformed state-of-the-art adaptive moment estimation (Adam) optimization across various metrics on REDD datasets; lastly, we evaluated the TEAD dataset using BERT-NILM training.
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Chen, Kui Fu, and Yan Feng Li. "On the Integration Schemes of Retrieving Impulse Response Functions from Transfer Functions." Mathematical Problems in Engineering 2010 (2010): 1–9. http://dx.doi.org/10.1155/2010/143582.
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The numerical inverse Laplace transformation (NILM) makes use of numerical integration. Generally, a high-order scheme of numerical integration renders high accuracy. However, surprisingly, this is not true for the NILM to the transfer function. Numerical examples show that the performance of higher-order schemes is no better than that of the trapezoidal scheme. In particular, the solutions from high-order scheme deviate from the exact one markedly over the rear portion of the period of interest. The underlying essence is examined. The deviation can be reduced by decreasing the frequency-sampling interval.
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Herbert Tran, Erin E., Aaron W. Andersen, and Heidi Goodrich-Blair. "CpxRA Influences Xenorhabdus nematophila Colonization Initiation and Outgrowth in Steinernema carpocapsae Nematodes through Regulation of the nil Locus." Applied and Environmental Microbiology 75, no. 12 (April 17, 2009): 4007–14. http://dx.doi.org/10.1128/aem.02658-08.
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ABSTRACT The gammaproteobacterium Xenorhabdus nematophila mutualistically colonizes an intestinal region of a soil-dwelling nematode and is a blood pathogen of insects. The X. nematophila CpxRA two-component regulatory system is necessary for both of these host interactions (E. Herbert et al., Appl. Environ. Microbiol. 73:7826-7836, 2007). Mutualistic association of X. nematophila with its nematode host consists of two stages: initiation, where a small number of bacterial cells establish themselves in the colonization site, and outgrowth, where these cells grow to fill the space. In this study, we show that the Cpx system is necessary for both of these stages. X. nematophila ΔcpxR1 colonized fewer nematodes than its wild-type parent and did not achieve as high a density as did the wild type within a portion of the colonized nematodes. To test whether the ΔcpxR1 host interaction phenotypes are due to its overexpression of mrxA, encoding the type I pilin subunit protein, we assessed the colonization phenotype of a ΔcpxR1 ΔmrxA1 double mutant. This mutant displayed the same colonization defect as ΔcpxR1, indicating that CpxR negative regulation of mrxA does not play a detectable role in X. nematophila-host interactions. CpxR positively regulates expression of nilA, nilB, and nilC genes necessary for nematode colonization. Here we show that the nematode colonization defect of the ΔcpxR1 mutant is rescued by elevating nil gene expression through mutation of nilR, a negative regulator of nilA, nilB, and nilC. These data suggest that the nematode colonization defect previously observed in ΔcpxR1 is caused, at least in part, by altered regulation of nilA, nilB, and nilC.
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Azizi, Elnaz, Mohammad T. H. Beheshti, and Sadegh Bolouki. "Event Matching Classification Method for Non-Intrusive Load Monitoring." Sustainability 13, no. 2 (January 12, 2021): 693. http://dx.doi.org/10.3390/su13020693.
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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.
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Kong, Xiangyu, Shijian Zhu, Xianxu Huo, Shupeng Li, Ye Li, and Siqiong Zhang. "A Household Energy Efficiency Index Assessment Method Based on Non-Intrusive Load Monitoring Data." Applied Sciences 10, no. 11 (May 30, 2020): 3820. http://dx.doi.org/10.3390/app10113820.
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Various countries in the world are vigorously developing energy-saving industries and attaching importance to the improvement of household energy efficiency, but it is difficult to evaluate user power consumption characteristics due to insufficient information and large data granularity. It is, however, possible to evaluate the energy efficiency of household users via non-intrusive load monitoring (NILM). This paper explores the energy efficiency assessment of residential users and proposes a household energy efficiency assessment method based on NILM data. An energy efficiency assessment index of residents is provided by analyzing factors that affect residents’ energy efficiency. This index is clear, operable, and easy to obtain and quantify. Based on NILM information, clustering, and comprehensive evaluation, as well as combining the entropy weight method with the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS), a user’s energy efficiency can be evaluated and analyzed. Some case studies are provided to verify the validity of the proposed method based on non-intrusive information, to analyze the characteristics and deficiencies of the user’s energy consumption, and to give corresponding energy recommendations.
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Lazzaretti, André Eugenio, Douglas Paulo Bertrand Renaux, Carlos Raimundo Erig Lima, Bruna Machado Mulinari, Hellen Cristina Ancelmo, Elder Oroski, Fabiana Pöttker, et al. "A Multi-Agent NILM Architecture for Event Detection and Load Classification." Energies 13, no. 17 (August 26, 2020): 4396. http://dx.doi.org/10.3390/en13174396.
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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%.
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Virtsionis Gkalinikis, Nikolaos, Christoforos Nalmpantis, and Dimitris Vrakas. "Torch-NILM: An Effective Deep Learning Toolkit for Non-Intrusive Load Monitoring in Pytorch." Energies 15, no. 7 (April 4, 2022): 2647. http://dx.doi.org/10.3390/en15072647.
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Non-intrusive load monitoring is a blind source separation task that has been attracting significant interest from researchers working in the field of energy informatics. However, despite the considerable progress, there are a very limited number of tools and libraries dedicated to the problem of energy disaggregation. Herein, we report the development of a novel open-source framework named Torch-NILM in order to help researchers and engineers take advantage of the benefits of Pytorch. The aim of this research is to tackle the comparability and reproducibility issues often reported in NILM research by standardising the experimental setup, while providing solid baseline models by writing only a few lines of code. Torch-NILM offers a suite of tools particularly useful for training deep neural networks in the task of energy disaggregation. The basic features include: (i) easy-to-use APIs for running new experiments, (ii) a benchmark framework for evaluation, (iii) the implementation of popular architectures, (iv) custom data loaders for efficient training and (v) automated generation of reports.
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Calamaro, Netzah, Moshe Donko, and Doron Shmilovitz. "A Highly Accurate NILM: With an Electro-Spectral Space That Best Fits Algorithm’s National Deployment Requirements." Energies 14, no. 21 (November 7, 2021): 7410. http://dx.doi.org/10.3390/en14217410.
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The central problems of some of the existing Non-Intrusive Load Monitoring (NILM) algorithms are indicated as: (1) higher required electrical device identification accuracy; (2) the fact that they enable training over a larger device count; and (3) their ability to be trained faster, limiting them from usage in industrial premises and external grids due to their sensitivity to various device types found in residential premises. The algorithm accuracy is higher compared to previous work and is capable of training over at least thirteen electrical devices collaboratively, a number that could be much higher if such a dataset is generated. The algorithm trains the data around 1.8×108 faster due to a higher sampling rate. These improvements potentially enable the algorithm to be suitable for future “grids and industrial premises load identification” systems. The algorithm builds on new principles: an electro-spectral features preprocessor, a faster waveform sampling sensor, a shorter required duration for the recorded data set, and the use of current waveforms vs. energy load profile, as was the case in previous NILM algorithms. Since the algorithm is intended for operation in any industrial premises or grid location, fast training is required. Known classification algorithms are comparatively trained using the proposed preprocessor over residential datasets, and in addition, the algorithm is compared to five known low-sampling NILM rate algorithms. The proposed spectral algorithm achieved 98% accuracy in terms of device identification over two international datasets, which is higher than the usual success of NILM algorithms.
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Fortuna, Luigi, and Arturo Buscarino. "Non-Intrusive Load Monitoring." Sensors 22, no. 17 (September 3, 2022): 6675. http://dx.doi.org/10.3390/s22176675.
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Makonin, Stephen, and Fred Popowich. "Nonintrusive load monitoring (NILM) performance evaluation." Energy Efficiency 8, no. 4 (October 31, 2014): 809–14. http://dx.doi.org/10.1007/s12053-014-9306-2.
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Sykiotis, Stavros, Maria Kaselimi, Anastasios Doulamis, and Nikolaos Doulamis. "ELECTRIcity: An Efficient Transformer for Non-Intrusive Load Monitoring." Sensors 22, no. 8 (April 11, 2022): 2926. http://dx.doi.org/10.3390/s22082926.
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Non-Intrusive Load Monitoring (NILM) describes the process of inferring the consumption pattern of appliances by only having access to the aggregated household signal. Sequence-to-sequence deep learning models have been firmly established as state-of-the-art approaches for NILM, in an attempt to identify the pattern of the appliance power consumption signal into the aggregated power signal. Exceeding the limitations of recurrent models that have been widely used in sequential modeling, this paper proposes a transformer-based architecture for NILM. Our approach, called ELECTRIcity, utilizes transformer layers to accurately estimate the power signal of domestic appliances by relying entirely on attention mechanisms to extract global dependencies between the aggregate and the domestic appliance signals. Another additive value of the proposed model is that ELECTRIcity works with minimal dataset pre-processing and without requiring data balancing. Furthermore, ELECTRIcity introduces an efficient training routine compared to other traditional transformer-based architectures. According to this routine, ELECTRIcity splits model training into unsupervised pre-training and downstream task fine-tuning, which yields performance increases in both predictive accuracy and training time decrease. Experimental results indicate ELECTRIcity’s superiority compared to several state-of-the-art methods.
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Le, Thi-Thu-Huong, and Howon Kim. "Non-Intrusive Load Monitoring Based on Novel Transient Signal in Household Appliances with Low Sampling Rate." Energies 11, no. 12 (December 5, 2018): 3409. http://dx.doi.org/10.3390/en11123409.
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Nowadays climate change problems have been more and more concerns and urgent in the real world. Especially, the energy power consumption monitoring is a considerate trend having positive effects in decreasing affecting climate change. Non-Intrusive Load Monitoring (NILM) is the best economic solution to solve the electrical consumption monitoring issue. NILM captures the electrical signals from the aggregate energy consumption, feature extraction from these signals and then learning and predicting the switch ON/OFF of appliances used these feature extracted. This paper proposed a NILM framework including data acquisition, data feature extraction, and classification model. The main contribution is to develop a new transient signal in a different aspect. The proposed transient signal is extracted from the active power signal in the low-frequency sampling rate. This transient signal is used to detect the event of household appliances. In household appliances event detection, we applied to Decision Tree and Long Short-Time Memory (LSTM) models. The average accuracies of these models achieved 92.64% and 96.85%, respectively. The computational and result experiments present the solution effectiveness for the accurate transient signal extraction in the electrical input signals.
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Stoler, Mark H., Thomas C. Wright, Valentin Parvu, Karen Yanson, Karen Eckert, Salma Kodsi, and Charles Cooper. "HPV Testing With 16, 18, and 45 Genotyping Stratifies Cancer Risk for Women With Normal Cytology." American Journal of Clinical Pathology 151, no. 4 (January 14, 2019): 433–42. http://dx.doi.org/10.1093/ajcp/aqy169.
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ABSTRACT Objectives To determine the BD Onclarity human papillomavirus (HPV) assay performance and risk values for cervical intraepithelial neoplasia grade 2 (CIN2) or higher and cervical intraepithelial neoplasia grade 3 (CIN3) or higher during Papanicolaou/HPV cotesting in a negative for intraepithelial lesions or malignancies (NILM) population. Methods In total, 22,383 of the 33,858 enrolled women were 30 years or older with NILM cytology. HPV+ and a subset of HPV– patients (3,219/33,858 combined; 9.5%) were referred to colposcopy/biopsy. Results Overall, 7.9% of women were Onclarity positive; HPV 16 had the highest prevalence (1.5%). Verification bias-adjusted (VBA) CIN2 or higher and CIN3 or higher prevalences were 0.9% and 0.3%, respectively. Onclarity had VBA CIN2 or higher (44.1%) and CIN3 or higher (69.5%) sensitivities, as well as CIN2 or higher (92.4%) and CIN3 or higher (92.3%) specificities—all similar to Hybrid Capture 2. HPV 16, 18, 45, and the other 11 genotypes had CIN3 or higher risks of 6.9%, 2.6%, 1.1%, and 2.2%, respectively. Conclusions Onclarity is clinically validated for cotesting in NILM women. Genotyping actionably stratifies women at greater CIN3 or higher risk.
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Zhang, Luhao, and Hongwei Zhu. "A Simple Method of Non-Intrusive Load Monitoring Based on BP Neural Network." Journal of Physics: Conference Series 2237, no. 1 (March 1, 2022): 012010. http://dx.doi.org/10.1088/1742-6596/2237/1/012010.
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Abstract Non-Intrusive Load Monitoring (NILM) is a method to breakdown the total power consumption into individual application, and it has great value for improving energy utilization. Deep-learning methods on high frequency data usually have a complex structure. Here, we use BP network with single hidden layer to achieve the task of NILM and get great results. The methods in this paper include event detection, feature extraction to get event-based differential current, data augmentation and application classification. BLUED dataset is used for this experiment. Our method has a good performance in BLUED dataset, with above 95% accuracy on phase A and B for application classification.
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Hoyo-Montaño, José Antonio, Jesús Naim Leon-Ortega, Guillermo Valencia-Palomo, Rafael Armando Galaz-Bustamante, Daniel Fernando Espejel-Blanco, and Martín Gustavo Vázquez Palma. "Non-Intrusive Electric Load identification using Wavelet Transform." Ingeniería e Investigación 38, no. 2 (May 1, 2018): 42–51. http://dx.doi.org/10.15446/ing.investig.v38n2.70550.
Full textAbstract:
This paper shows the development of a decision tree for the classification of loads in a non-intrusive load monitoring (NILM) system implemented in a simple board computer (Raspberry Pi 3). The decision tree uses the total energy value of the power signal of an equipment, which is generated using a discrete wavelet transform and Parseval’s theorem. The power consumption data of different types of equipment were obtained from a public access database for NILM applications. The best split point for the design of the decision tree was determined using the weighted average Gini index. The tree was validated using loads available in the same public access database.
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Ahajjam, Mohamed Aymane, Daniel Bonilla Licea, Mounir Ghogho, and Abdellatif Kobbane. "IMPEC: An Integrated System for Monitoring and Processing Electricity Consumption in Buildings." Sensors 20, no. 4 (February 14, 2020): 1048. http://dx.doi.org/10.3390/s20041048.
Full textAbstract:
Non-intrusive Load Monitoring (NILM) systems aim at identifying and monitoring the power consumption of individual appliances using the aggregate electricity consumption. Many issues hinder their development. For example, due to the complexity of data acquisition and labeling, datasets are scarce; labeled datasets are essential for developing disaggregation and load prediction algorithms. In this paper, we introduce a new NILM system, called Integrated Monitoring and Processing Electricity Consumption (IMPEC). The main characteristics of the proposed system are flexibility, compactness, modularity, and advanced on-board processing capabilities. Both hardware and software parts of the system are described, along with several validation tests performed at residential and industrial settings.