Relevant bibliographies by topics / Non-intrusive load management

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Academic literature on the topic 'Non-intrusive load management'

Author: Grafiati
Published: 5 June 2025
Last updated: 8 July 2025

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Journal articles on the topic "Non-intrusive load management"

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Wu, Sheng, and Kwok L. Lo. "Non-Intrusive Monitoring Algorithm for Resident Loads with Similar Electrical Characteristic." Processes 8, no. 11 (2020): 1385. http://dx.doi.org/10.3390/pr8111385.

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Non-intrusive load monitoring is a vital part of an overall load management scheme. One major disadvantage of existing non-intrusive load monitoring methods is the difficulty to accurately identify loads with similar electrical characteristics. To overcome the various switching probability of loads with similar characteristics in a specific time period, a new non-intrusive load monitoring method is proposed in this paper which will modify monitoring results based on load switching probability distribution curve. Firstly, according to the addition theorem of load working currents, the complex current is decomposed into the independently working current of each load. Secondly, based on the load working current, the initial identification of load is achieved with current frequency domain components, and then the load switching times in each hour is counted due to the initial identified results. Thirdly, a back propagation (BP) neural network is trained by the counted results, the switching probability distribution curve of an identified load is fitted with the BP neural network. Finally, the load operation pattern is profiled according to the switching probability distribution curve, the load operation pattern is used to modify identification result. The effectiveness of the method is verified by the measured data. This approach combines the operation pattern of load to modify the identification results, which improves the ability to identify loads with similar electrical characteristics.
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Okazawa, Kazuki, Naoya Kaneko, Dafang Zhao, et al. "Evaluation of Deep Learning-Based Non-Intrusive Thermal Load Monitoring." Energies 17, no. 9 (2024): 2012. http://dx.doi.org/10.3390/en17092012.

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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.
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Zhou, Mengran, Shuai Shao, Xu Wang, Ziwei Zhu, and Feng Hu. "Deep Learning-Based Non-Intrusive Commercial Load Monitoring." Sensors 22, no. 14 (2022): 5250. http://dx.doi.org/10.3390/s22145250.

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Commercial load is an essential demand-side resource. Monitoring commercial loads helps not only commercial customers understand their energy usage to improve energy efficiency but also helps electric utilities develop demand-side management strategies to ensure stable operation of the power system. However, existing non-intrusive methods cannot monitor multiple commercial loads simultaneously and do not consider the high correlation and severe imbalance among commercial loads. Therefore, this paper proposes a deep learning-based non-intrusive commercial load monitoring method to solve these problems. The method takes the total power signal of the commercial building as input and directly determines the state and power consumption of several specific appliances. The key elements of the method are a new neural network structure called TTRNet and a new loss function called MLFL. TTRNet is a multi-label classification model that can autonomously learn correlation information through its unique network structure. MLFL is a loss function specifically designed for multi-label classification tasks, which solves the imbalance problem and improves the monitoring accuracy for challenging loads. To validate the proposed method, experiments are performed separately in seen and unseen scenarios using a public dataset. In the seen scenario, the method achieves an average F1 score of 0.957, which is 7.77% better than existing multi-label classification methods. In the unseen scenario, the average F1 score is 0.904, which is 1.92% better than existing methods. The experimental results show that the method proposed in this paper is both effective and practical.
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He, Nian, Dengfeng Liu, Zhichen Zhang, Zhiquan Lin, Tiesong Zhao, and Yiwen Xu. "Learning-Based Non-Intrusive Electric Load Monitoring for Smart Energy Management." Sensors 24, no. 10 (2024): 3109. http://dx.doi.org/10.3390/s24103109.

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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.
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Massidda, Luca, and Marino Marrocu. "A Bayesian Approach to Unsupervised, Non-Intrusive Load Disaggregation." Sensors 22, no. 12 (2022): 4481. http://dx.doi.org/10.3390/s22124481.

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Estimating household energy use patterns and user consumption habits is a fundamental requirement for management and control techniques of demand response programs, leading to a growing interest in non-intrusive load disaggregation methods. In this work we propose a new methodology for disaggregating the electrical load of a household from low-frequency electrical consumption measurements obtained from a smart meter and contextual environmental information. The method proposed allows, with an unsupervised and non-intrusive approach, to separate loads into two components related to environmental conditions and occupants’ habits. We use a Bayesian approach, in which disaggregation is achieved by exploiting actual electrical load information to update the a priori estimate of user consumption habits, to obtain a probabilistic forecast with hourly resolution of the two components. We obtain a remarkably good accuracy for a benchmark dataset, higher than that obtained with other unsupervised methods and comparable to the results of supervised algorithms based on deep learning. The proposed procedure is of great application interest in that, from the knowledge of the time series of electricity consumption alone, it enables the identification of households from which it is possible to extract flexibility in energy demand and to realize the prediction of the respective load components.
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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 (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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Zhang, Xu, Jun Zhou, Chunguang Lu, Lei Song, Fanyu Meng, and Xianbo Wang. "Non-Intrusive Load Monitoring Based on Dimensionality Reduction and Adapted Spatial Clustering." Energies 17, no. 17 (2024): 4303. http://dx.doi.org/10.3390/en17174303.

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Non-invasive load monitoring (NILM) deduces changes in energy consumption patterns and operational statuses of electrical equipment from power signals in the feed line. With the emergence of fine-grained power load distribution, the importance of utilizing this technology for implementing demand-side energy management in smart grid development has become increasingly prominent. To address the issue of low load identification accuracy stemming from complex and diverse load types, this paper introduces a NILM method based on uniform manifold approximation and projection (UMAP) reduction and enhanced density-based spatial clustering of applications with noise (DBSCAN). Firstly, this paper combines the characteristics of user load under transient and steady-state conditions and selects data with significant differences to construct a load-characteristic database. Additionally, UMAP is employed to reduce the dimensionality of high-dimensional load features and rebuild a load feature database. Subsequently, DBSCAN is utilized to categorize typical user loads, followed by a correlation analysis with the load-characteristic database to determine the types or classes of loads that involve switching actions. Finally, this paper simulates and analyzes the proposed method using the electricity consumption data of industrial users from the CER–Electricity–Data dataset. It identifies the electricity load data commonly utilized by users in a specific area of Zhejiang Province in China. The experimental results indicate that the accuracy of the proposed non-invasive load identification method reaches 95%. Compared to the wavelet transform, decision tree, and backpropagation network methods, the improvement is approximately 5%.
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Kuzmin, P. S. "NON-INTRUSIVE LOAD MONITORING: IMPLEMENTATION EFFECTS AND DISTRIBUTION PROSPECTS." Strategic decisions and risk management 10, no. 4 (2020): 306–19. http://dx.doi.org/10.17747/2618-947x-2019-4-306-319.

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The digital transition in the electric power industry is a promising goal for the development of the industry. In recent years, a wide range of technologies has been introduced into various types of activities of energy companies, including significant attention being paid to technologies that implement demand-side management, transferring consumers from the passive category to active consumers, and also opening up new opportunities in energy management. Non-intrusive load monitoring technology is of significant interest for both electricity suppliers and consumers in the USA and EEC countries, however, a Russian-language study is being carried out for the first time.The purpose of the study is to consider the concept of non-intrusive load monitoring, to formulate and systematize the effects for electric power industry entities and consumers of electricity from the introduction of technology.A review of literary sources is carried out, the most cited articles on this topic are analyzed. To calculate the propagation rate of non-intrusive load monitoring, the Bass innovation diffusion model was used. The model allows to perform an assessment based on data on similar products and has established itself as sufficiently effective for predicting the distribution of durable goods, the actual information for which has not yet been collected.For the first time, a classification of effects arising from the introduction of technology is proposed. The paper obtained a range of effects for households, energy companies, business and government. The calculation of the technology distribution rate showed that without the use of technological corridors and the systematic introduction by energy companies, the peak of adoption can be reached by 8 years from the start of implementation.Non-intrusive load monitoring allows you to get a wide range of data in order to further optimize energy consumption, increase the efficiency of enterprises, monitor the operation of equipment. There are great opportunities in the commercialization of collected data.
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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 (2021): 1814–24. https://doi.org/10.11591/ijeecs.v23.i3.pp1814-1824.

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Demand-side load management (DSM) requires greater role-play by endusers. To lower the investment for this load management concept, nonintrusive 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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Liu, Siqi, Zhiyuan Xie, and Zhengwei Hu. "Research on Distributed Smart Home Energy Management Strategies Based on Non-Intrusive Load Monitoring (NILM)." Electronics 14, no. 9 (2025): 1719. https://doi.org/10.3390/electronics14091719.

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Home energy optimization management improves energy utilization efficiency and reduces electricity costs through intelligent load control, strategic utilization of time-of-use pricing, and optimized integration of energy storage and distributed energy systems. Simultaneously, it enhances energy autonomy, lowers carbon emissions, and promotes sustainable low-carbon lifestyles. By coordinating demand response programs with flexible load scheduling strategies, this approach effectively reduces peak loads and improves grid stability, thereby advancing smart grid development. This paper investigates the optimized scheduling problem in smart home energy management systems, focusing on achieving integrated optimization of multiple factors, including load balancing, cost control, carbon emission reduction, user comfort, and demand response. Considering the diverse load characteristics of residential energy systems, we propose a novel optimization framework incorporating dynamic pricing mechanisms and intelligent scheduling algorithms, which is rigorously validated through simulation experiments. Results demonstrate that the proposed scheduling strategy successfully balances economic efficiency, load management, and environmental sustainability while maintaining acceptable user comfort levels—providing a comprehensive solution for intelligent home energy management systems.
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Dissertations / Theses on the topic "Non-intrusive load management"

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Fuller, Ashley E. (Ashley Eliot). "Harmonic approaches to non-intrusive load diagnostics." Thesis, Massachusetts Institute of Technology, 2008. http://hdl.handle.net/1721.1/44847.

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Thesis (Nav. E.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering; and, (S.M.)--Massachusetts Institute of Technology, System Design and Management Program, 2008.<br>Includes bibliographical references (p. 88-90).<br>The Non-Intrusive Load Monitor (NILM) is a system that monitors, records and processes voltage and current measurements to establish the operating characteristics of individual loads on a load center from a single aggregate measurement. The NILM can also be used to actively monitor degradation or diagnose specific system failures. Current NILM research conducted at the Massachusetts Institute of Technology's Laboratory for Electromagnetic and Electronic Systems (LEES) is exploring the application and expansion of NILM technology for the use of monitoring a myriad of electromechanical loads. This thesis presents a fundamental guide to understanding NILM operation using laboratory bench testing and demonstrates its potential to detect an array of electric machine failures before they become catastrophic. The NILM's ability to the monitor the current spectrum of electric machines can be used to immediately diagnose multiple common system casualties and detect unusual system operation. Clean current spectrum regions can be exploited by selecting induction machine design characteristics that result in eccentric modulation frequencies occurring in areas free of supply frequency harmonics. Current spectrum analysis was used to demonstrate the NILM's potential to monitor multiple machines from an aggregate source and discuss intersystem impedances. It can be shown that multiple machines with slightly varied physical characteristics, such as induction motor rotor slots, coupled with using clean current spectral regions support automated diagnostic system development. Measurements and experimentation were conducted in the LEES laboratory and the Industrial Support Center electric shop, Boston.<br>by Ashley E. Fuller.<br>S.M.<br>Nav.E.
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Jones, Richard A. (Richard Alan) Nav E. Massachusetts Institute of Technology. "Improving shipboard applications of non-intrusive load monitoring." Thesis, Massachusetts Institute of Technology, 2008. http://hdl.handle.net/1721.1/44843.

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Thesis (Nav. E.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering; and, (S.M.)--Massachusetts Institute of Technology, System Design and Management Program, 2008.<br>Includes bibliographical references (p. 73-76).<br>The Non-Intrusive Load Monitor (NILM) measures equipment performance by measuring and analyzing the source power to the equipment at a single point in the electrical system. Previous studies have proven the usefulness of the NILM system in characterizing the state of mechanical systems onboard U.S. Coast Guard vessels and at the U.S. Navys Land Based Engineering Site (LBES) in Philadelphia, Pennsylvania. This thesis seeks to augment the NILM system by exploring a more user friendly Graphical User Interface (GUI) to allow shipboard crews to utilize the NILM while in operation. Previous applications of NILM required post-event data analysis in the laboratory. An additional monitor was installed on the Low Pressure Air Compressor (LPAC) #1 at the LBES facility to investigate abnormalities detected in the operation of LPAC #2 by previous research. The ability of the NILM to function at the highest levels of the electrical distribution system was also explored at the LBES facility with the installation of two additional NILM systems on the main switchboards supplying power to the auxiliary system loads. Finally, a brief overview of the analysis software of the Multi-Function Monitor (MFM), a key component in modern ships Zonal Electrical Distribution Systems (ZEDS), is presented to explore the possibility of the NILM and MFM systems operating in conjunction to improve the operation of future ZEDS.<br>by Richard A. Jones.<br>S.M.<br>Nav.E.
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Branch, Perry L. (Perry Lamar). "Development of real time non-intrusive load monitor for shipboard fluid systems." Thesis, Massachusetts Institute of Technology, 2008. http://hdl.handle.net/1721.1/44846.

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Thesis (Nav. E.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering; and, (S.M.)--Massachusetts Institute of Technology, System Design and Management Program, 2008.<br>Includes bibliographical references (p. 84-85).<br>Since the year 2000, the United States Navy has spent an average of half a billion dollars over the congressionally approved budget for shipbuilding. Additionally, most experts project that in order to meet the Chief of Naval Operation's goal of a 313 ship Navy, the annual ship building budget will have to increase by about two thirds. Exacerbating this problem is the rising cost of maintaining the current inventory of ships. The U.S. Navy has long used a requirements driven maintenance program to reduce the number of total system failures by conducting routine maintenance and inspections whether they are needed or not. In order to combat this problem the Navy will inevitably have to turn to a condition based maintenance system. The Non-Intrusive Load Monitor (NILM) is a system that can greatly enhance the ability to monitor the health of engineering systems while incurring a low acquisition cost and low technology risk. This research focuses on the development of a real time user interface for the current NILM architecture in order to provide useful system information to an operator. Additionally, this research has shown that the NILM can be used effectively and reliably, to monitor equipment health, recognize and indicate abnormal operating conditions and casualties and provide invaluable information for training operators, diagnosing problems and troubleshooting. The NILM is an inexpensive and promising platform for monitoring equipment and reducing maintenance costs.<br>by Perry L. Branch.<br>S.M.<br>Nav.E.
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Aladesanmi, Ereola Johnson. "Non intrusive load monitoring & identification for energy management system using computational intelligence approach." Master's thesis, University of Cape Town, 2015. http://hdl.handle.net/11427/13561.

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Includes bibliography.<br>Electrical energy is the life line to every nation’s or continent development and economic progress. Referable to the recent growth in the demand for electricity and shortage in production, it is indispensable to develop strategies for effective energy management and system delivery. Load monitoring such as intrusive load monitoring, non-intrusive load monitoring, and identification of domestic electrical appliances is proposed especially at the residential level since it is the major energy consumer. The intrusive load monitoring provides accurate results and would allow each individual appliance's energy consumption to be transmitted to a central hub. Nevertheless, there are many practical disadvantages to this method that have motivated the introduction of non-intrusive load monitoring system. The fiscal cost of manufacturing and installing enough monitoring devices to match the number of domestic appliances is considered to be a disadvantage. In addition, the installation of one meter per household appliances would lead to congestion in the house and thus cause inconvenience to the occupants of the house, therefore, non-intrusive load monitoring technique was developed to alleviate the aforementioned challenges of intrusive load monitoring. Non-intrusive load monitoring (NILM) is the process of disaggregating a household’s total energy consumption into its contributing appliances. The total household load is monitored via a single monitoring device such as smart meter (SM). NILM provides cost effective and convenient means of load monitoring and identification. Several nonintrusive load monitoring and identification techniques are reviewed. However, the literature lacks a comprehensive system that can identify appliances with small energy consumption, appliances with overlapping energy consumption and a group of appliance ranges at once. This has been the major setback to most of the adopted techniques. In this dissertation, we propose techniques that overcome these setbacks by combining artificial neural networks (ANN) with a developed algorithm to identify appliances ranges that contribute to the energy consumption within a given period of time usually an hour interval.
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He, Dawei. "An advanced non-intrusive load monitoring technique and its application in smart grid building energy management systems." Diss., Georgia Institute of Technology, 2016. http://hdl.handle.net/1853/54951.

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The objective of the proposed research is to develop an intelligent load modeling, identification, and prediction technology to provide granular load energy consumption and performance details and drive building energy reduction, demand reduction, and proactive equipment maintenance. Electricity consumption in commercial and residential sectors accounts for about 70% of the total electricity generation in United States. Buildings are the most important consumers, and contribute to over 80% of the consumptions in these two sectors. To reduce electrical energy spending and carbon emission, several studies from Pacific Northwest National Lab (PNNL) and National Renewable Energy Lab (NREL) prove that if equipped with the proper technologies, a commercial or a residential building can potentially improve energy savings of buildings by up to about 10% to 30% of their usage. However, the market acceptance of these new technologies today is still not sufficient, and the reason is generally acknowledged to be the lack of solution to quantify the contributions of these new technologies to the energy savings, and the invisibility of the loads in buildings. A non-intrusive load monitoring (NILM) system is proposed in this dissertation, which can identify every individual load in buildings and record the energy consumption, time-of-day variations and other relevant statistics of the identified load, with no access to the individual component. The challenge of such a non-intrusive load monitoring is to find features that are unique for a particular load and then to match a measured feature of an unknown load against a database or library of known. Many problems exist in this procedure and the proposed research is going to focus on three directions to overcome the bottlenecks. They are respectively fundamental load studies for a model-driven feature extraction, adaptive identification algorithms for load space extendibility, and the practical simplifications for the real industrial applications. The simulation results show the great potentials of this new technology in building energy monitoring and management.
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Hare, Jeremy (Jeremy B. ). "Disaggregation of residential home energy via non-intrusive load monitoring for energy savings and targeted demand response." Thesis, Massachusetts Institute of Technology, 2018. http://hdl.handle.net/1721.1/117983.

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Thesis: M.B.A., Massachusetts Institute of Technology, Sloan School of Management, in conjunction with the Leaders for Global Operations Program at MIT, 2018.<br>Thesis: S.M., Massachusetts Institute of Technology, Department of Civil and Environmental Engineering, in conjunction with the Leaders for Global Operations Program at MIT, 2018.<br>"June 2018." Cataloged from PDF version of thesis.<br>Includes bibliographical references (pages 59-61).<br>Residential energy disaggregation is a process by which the power usage of a home is broken down into the consumption of individual appliances. There are a number of different methods to perform energy disaggregation, from simulation models to installing "smart-plugs" at every outlet where an appliance is connected to the wall. Non-Intrusive Load Monitoring (NILM) is one such disaggregation option. NILM is widely recognized as one of the most cost-effective methods for gathering disaggregated energy data while maintaining a high level of accuracy. Although the technology has existed for many years, the adoption rate of NILM, and other devices that disaggregate energy, has been minimal. This thesis provides details on the potential benefits, both for the customer and utility provider, associated with furthering the adoption of NILM devices and obtaining the disaggregated appliance level energy-use. A broad overview of potential benefits is presented; however, the primary goal of this thesis will be to investigate two benefits of NILM in detail: overall household energy reduction and targeted demand response. First, installation of a NILM device can provide electricity customers information that allows them to become more aware of their energy consumption, and thereby, more energy efficient. A study was conducted that looked at the electricity consumption of 174 homes that were using a passive NILM device in their home. This NILM device provided immediate feedback on the power consumption for a portion of the home's appliances via smart-phone application. The homes reduced their monthly energy consumption by an average of 2.6 - 3.1% after the NILM installation. This was validated by a number of analysis methods returning similar results. Aligned with this benefit comes a recommendation for an incentive structure that can reduce the price paid by the consumer and develop a higher adoption rate of NILM devices. Second, the wide-spread adoption of NILM devices can provide electric utilities information to reduce carbon intensity via targeted demand response. There is a significant opportunity for utilities to engage their customers based on the time of use of detailed appliances. Multiple metrics are presented in this thesis to quantify the deferrable load opportunity of specific appliances and individual households. Utility operational cost savings and greater customer incentives can be linked to the use of these metrics.<br>by Jeremy Hare.<br>M.B.A.<br>S.M.
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Lin, Yu-Hsiu, and 林郁修. "An Advanced Home Energy Management System Facilitated by Non-intrusive Load Monitoring with Meta-heuristic-based Multi-objective In-home Load Scheduling." Thesis, 2014. http://ndltd.ncl.edu.tw/handle/n8b9f7.

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博士<br>國立臺北科技大學<br>機電科技研究所<br>102<br>Electricity is one of the most popular forms of energy used in the modern society. Nowadays, electricity energy demands requested from down-stream sectors of a Smart Grid constantly increase. One way to improve the electricity energy efficiency such that those demands are met can be achieved by utilizing the Home or Building Energy Management Systems (HEMS/BEMS). In a smart house connected to a smart grid via Advanced Metering Infrastructure, a traditional HEMS is used to monitor major or power-intensive household appliances by installing many smart power meters, and manage them in response to Demand Response (DR) schemes. The construction of such an HEMS deploying smart plugs in a house environment incurs extra costs including hardware installation and annual maintenance costs as well as installation complexity. A rules- or tasks-based and priorities-driven DR strategy of the HEMS managing the household appliances to react to DR schemes with user intervention was developed. In this dissertation, an Advanced HEMS (AHEMS) as data fusion facilitated by a Non-intrusive Load Monitoring (NILM) technique with a Meta-heuristic-based Multi-objective in-home Load Scheduling mechanism is proposed. By analyzing aggregated voltage and current signals picked-up by only one set of plug-panel voltage and current sensors, the NILM acted as a load disaggregation process is able to ascertain how many major or power-intensive household appliances monitored are being operated and is capable of estimating how much power goes into each of the major household appliances non-intrusively. Information identified by the NILM is very useful for DR implementation. In this dissertation, a Non-dominated Sorting Genetic Algorithm-II (NSGA-II)-based Multi-objective in-home Load Scheduling mechanism that meta-heuristically schedules major or power-intensive household appliances, while taking residents’ comfort preferences and lifestyles into account without user intervention is proposed. By effectively operating the household appliances enrolled for participation in DR and scheduled in exchange for a discount on electricity prices, residents can save their electricity bills. The AHEMS proposed in this dissertation is conducted and evaluated in a residential house in Taiwan. As demonstrated in this dissertation, the proposed system is able to identify how much electricity energy each of household appliances monitored in the household uses, and is capable of providing advices on how to make more efficient uses of the electricity energy to the residents without user intervention. From the field validation and the performance evaluation in this dissertation, the proposed AHEMS is workable and feasible.
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Book chapters on the topic "Non-intrusive load management"

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Sastry, L. N., and Sri Phani Krishna Karri. "Smart Home Energy Management Using Non-intrusive Load Monitoring." In Sustainable Energy Solutions with Artificial Intelligence, Blockchain Technology, and Internet of Things. CRC Press, 2023. http://dx.doi.org/10.1201/9781003356639-4.

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Kizonde, Bihindi K., Tebello N. D. Mathaba, and Hendrick M. Langa. "A Non-intrusive Load Monitoring Technique for Real-Time Energy Management." In Lecture Notes in Networks and Systems. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-88653-9_45.

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Dang, Hoang-Anh, and Van-Dung Dao. "Electric Load Disaggregation Using Non-intrusive Load Monitoring Algorithm for Home Energy Management: Case-Study for an Apartment in Hanoi." In The AUN/SEED-Net Joint Regional Conference in Transportation, Energy, and Mechanical Manufacturing Engineering. Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-1968-8_53.

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Conference papers on the topic "Non-intrusive load management"

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Hamedifar, Sima, Shichao Liu, and George Xiao. "Non-Intrusive Load Monitoring-based Fuzzy Actor-Critic Reinforcement Learning Home Energy Management." In 2024 IEEE 7th International Conference on Industrial Cyber-Physical Systems (ICPS). IEEE, 2024. http://dx.doi.org/10.1109/icps59941.2024.10639994.

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Rajkumar, N., Anitha Govindaram, R. Geetha, Subhamathi A S F, Nithya S., and Jose Anand A. "Non-Intrusive Load Monitoring Techniques for Intelligent Energy Management: A Comparative Study of FHMM and LSTM Approach." In 2025 International Conference on Visual Analytics and Data Visualization (ICVADV). IEEE, 2025. https://doi.org/10.1109/icvadv63329.2025.10961750.

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Chelli, G., L. Ciabattoni, J. M. Flores-Arias, G. Foresi, A. Monteriu, and D. Proietti Pagnotta. "Non intrusive load identification for smart energy management systems." In 2018 IEEE International Conference on Consumer Electronics (ICCE). IEEE, 2018. http://dx.doi.org/10.1109/icce.2018.8326318.

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Chou, Manith, Kosorl Thourn, and Rothvichea Chea. "Multi-Scale Electrical Appliance Load Signature for Non-Intrusive Load Monitoring Classification." In 2022 14th International Conference on Software, Knowledge, Information Management and Applications (SKIMA). IEEE, 2022. http://dx.doi.org/10.1109/skima57145.2022.10029485.

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Naghibi, Bahman, and Sara Deilami. "Non-intrusive load monitoring and supplementary techniques for home energy management." In 2014 Australasian Universities Power Engineering Conference (AUPEC). IEEE, 2014. http://dx.doi.org/10.1109/aupec.2014.6966647.

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Li, Wenjin Jason, Xiaoqi Tan, and Danny H. K. Tsang. "Smart home energy management systems based on non-intrusive load monitoring." In 2015 IEEE International Conference on Smart Grid Communications (SmartGridComm). IEEE, 2015. http://dx.doi.org/10.1109/smartgridcomm.2015.7436413.

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Li, Hongbo, Lingxia Lu, and Miao Yu. "Home Energy Management System(HEMS) Based on Non-Intrusive Load Monitoring." In 2023 IEEE 7th Conference on Energy Internet and Energy System Integration (EI2). IEEE, 2023. http://dx.doi.org/10.1109/ei259745.2023.10512886.

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Pingping, Liu, Zhu Leiji, and Xiong Yong. "Dataset Construction and Verification Based on Non-Intrusive Load Monitoring." In 2022 IEEE 5th Advanced Information Management, Communicates, Electronic and Automation Control Conference (IMCEC). IEEE, 2022. http://dx.doi.org/10.1109/imcec55388.2022.10019803.

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Jawad, Muhammad, Noman Shabbir, Roya Ahmadiahangar, Argo Rosin, Taha Khaleel, and Wisha Tahir. "Non-Intrusive Load Monitoring Based Smart Energy Management System For Smart Buildings." In 2024 7th International Conference on Information and Computer Technologies (ICICT). IEEE, 2024. http://dx.doi.org/10.1109/icict62343.2024.00014.

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Alahmad, Mahmoud, Hosen Hasna, and Evans Sordiashie. "Non-intrusive electrical load monitoring and profiling methods for applications in energy management systems." In 2011 IEEE Long Island Systems, Applications and Technology Conference (LISAT). IEEE, 2011. http://dx.doi.org/10.1109/lisat.2011.5784233.

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