Relevant bibliographies by topics / NILM

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'NILM'

Author: Grafiati
Published: 4 June 2021
Last updated: 10 March 2023

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Journal articles on the topic "NILM"

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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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Dissertations / Theses on the topic "NILM"

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Donnal, John Sebastian. "Home NILM : a comprehensive energy monitoring toolkit." Thesis, Massachusetts Institute of Technology, 2013. http://hdl.handle.net/1721.1/82386.

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Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2013.
Cataloged from PDF version of thesis.
Includes bibliographical references (p. 193-196).
In this thesis we present two powerful new non-intrusive sensor designs, one for measuring equipment power consumption and one for measuring equipment vibration. We also discuss a unified data management framework for storing, processing, and viewing the large amounts of information collected from these sensors. Our electric power sensor can detect current and voltage with no ohmic contact to the wire. This enables power measurements from previously unavailable locations such as the front of the circuit breaker or the surface of a multi wire cable. This sensor is based off a Tunneling Magnetoresistive (TMR) element which is wrapped in a novel feedback architecture to provide a linear measurement of magnetic field strength over several orders of magnitude. The vibration sensor is part of a larger embedded energy harvesting project which aims to provide diagnostic feedback on motors during operation. The management framework is a powerful collection of software programs that allows data to be collected and stored locally but efficiently manipulated remotely by users anywhere in the world.
by John Sebastian Donnal.
S.M.
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Amirach, Nabil. "Détection d'évènements simples à partir de mesures sur courant alternatif." Thesis, Toulon, 2015. http://www.theses.fr/2015TOUL0006/document.

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La nécessité d’économiser de l'énergie est l’un des axes importants de ces dernières décennies, d’où le besoin de surveiller la consommation d'énergie des processus résidentiels et industriels. Le travail de recherche présenté dans ce manuscrit s’inscrit plus particulièrement dans le suivi de la consommation électrique afin de permettre l’économie d’énergie. Le but final étant d'avoir une connaissance précise et fiable d'un réseau électrique donné. Cela passe par la décomposition de la consommation électrique globale du réseau électrique étudié afin de fournir une analyse détaillée de l'énergie consommée par usage. L’objectif de cette thèse est la mise en place d’une approche non-intrusive permettant de réaliser les étapes de détection d’évènements et d’extraction de caractéristiques, qui précédent les étapes de classification et d’estimation de la consommation électrique par usage. L’algorithme résultant des travaux effectués durant cette thèse permet de détecter les évènements qui surviennent sur le courant et d’y associer un vecteur d’information contenant des paramètres caractérisant le régime permanent et le régime transitoire. Ce vecteur d’information permet ensuite de reconnaître tous les évènements liés à la même charge électrique
The need to save energy is an important focus of recent decades, hence the need to monitor the energy consumption of residential and industrial processes. The research works presented in this manuscript are within the monitoring power consumption area in order to enable energy saving. The final goal is to have a clear and reliable knowledge of a given grid. This involves the decomposition of the overall power consumption of the electrical network to provide a detailed analysis of the consumed energy. The objective of this thesis is to develop a non-intrusive approach to achieve the event detection and feature extraction steps, which precede the classification and the power consumption estimation steps. The algorithm resulting from the works performed in this thesis can detect events which occur on the current and associates to them an information vector containing the parameters characterizing the steady and transient states. Then this information vector is used to recognize all the events linked to the same electrical load
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Kane, Thomas John S. M. Massachusetts Institute of Technology. "The NILM Dashboard : watchstanding and real-time fault detection using Non-intrusive Load Monitoring." Thesis, Massachusetts Institute of Technology, 2019. https://hdl.handle.net/1721.1/122320.

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Thesis: S.M., Massachusetts Institute of Technology, Department of Mechanical Engineering, 2019
Thesis: S.M. in Naval Architecture and Marine Engineering, Massachusetts Institute of Technology, Department of Mechanical Engineering, 2019
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 119-122).
Non-intrusive Load Monitoring (NILM) measures power at a central point in an electrical network and disaggregates individual load schedules from the overall power stream. This thesis presents the NILM Dashboard, a data-analysis and user interface tool that provides real-time machinery monitoring and fault diagnostics using NILM data. The Dashboard was developed and deployed for use onboard US Coast Guard Cutters to act as an automatic watchstander and condition-based maintenance aid. The effectiveness of the system is demonstrated on power data collected from electrical panels in the ship's engine room. Case studies are used to evaluate the Dashboard's ability to detect fault conditions in electromechanical systems.
by Thomas John Kane.
S.M.
S.M. in Naval Architecture and Marine Engineering
S.M. Massachusetts Institute of Technology, Department of Mechanical Engineering
S.M.inNavalArchitectureandMarineEngineering Massachusetts Institute of Technology, Department of Mechanical Engineering
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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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Bernard, Timo [Verfasser], and Norbert [Akademischer Betreuer] Fuhr. "Non-Intrusive Load Monitoring (NILM) : combining multiple distinct electrical features and unsupervised machine learning techniques / Timo Bernard ; Betreuer: Norbert Fuhr." Duisburg, 2018. http://d-nb.info/1163534102/34.

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Giri, Suman. "A Framework for Estimating Energy Consumed by Electric Loads Through Minimally Intrusive Approaches." Research Showcase @ CMU, 2015. http://repository.cmu.edu/dissertations/564.

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This dissertation explores the problem of energy estimation in supervised Non-Intrusive Load Monitoring (NILM). NILM refers to a set of techniques used to estimate the electricity consumed by individual loads in a building from measurements of the total electrical consumption. Most commonly, NILM works by first attributing any significant change in the total power consumption (also known as an event) to a specific load and subsequently using these attributions (i.e. the labels for the events) to estimate energy for each load. For this last step, most proposed solutions in the field impart simplifying assumptions to make the problem more tractable. This has severely limited the practicality of the proposed solutions. To address this knowledge gap, we present a framework for creating appliance models based on classification labels and aggregate power measurements that can help relax many of these assumptions. Within the framework, we model the problem of utilizing a sequence of event labels to generate energy estimates as a broader class of problems that has two major components (i) With the understanding that the labels arise from a process with distinct states and state transitions, we estimate the underlying Finite State Machine (FSM) model that most likely generated the observed sequence (ii) We allow for the observed sequence to have errors, and present an error correction algorithm to detect and correct them. We test the framework on data from 43 appliances collected from 19 houses and find that it improves errors in energy estimates when compared to the case with no correction in 19 appliances by a factor of 50, leaves 17 appliances unchanged, and negatively impacts 6 appliances by a factor of 1.4. This approach of utilizing event sequences to estimate energy has implications in virtual metering of appliances as well. In a case study, we utilize this framework in order to substitute the need of plug-level sensors with cheap and easily deployable contacless sensors, and find that on the 6 appliances virtually metered using magnetic field sensors, the inferred energy values have an average error of 10:9%.
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Olsson, Charlie, and David Hurtig. "An approach to evaluate machine learning algorithms for appliance classification." Thesis, Malmö universitet, Fakulteten för teknik och samhälle (TS), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:mau:diva-20217.

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A cheap and powerful solution to lower the electricity usage and making the residents more energy aware in a home is to simply make the residents aware of what appliances that are consuming electricity. Meaning the residents can then take decisions to turn them off in order to save energy. Non-intrusive load monitoring (NILM) is a cost-effective solution to identify different appliances based on their unique load signatures by only measuring the energy consumption at a single sensing point. In this thesis, a low-cost hardware platform is developed with the help of an Arduino to collect consumption signatures in real time, with the help of a single CT-sensor. Three different algorithms and one recurrent neural network are implemented with Python to find out which of them is the most suited for this kind of work. The tested algorithms are k-Nearest Neighbors, Random Forest and Decision Tree Classifier and the recurrent neural network is Long short-term memory.
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Huss, Anders. "Hybrid Model Approach to Appliance Load Disaggregation : Expressive appliance modelling by combining convolutional neural networks and hidden semi Markov models." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-179200.

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The increasing energy consumption is one of the greatest environmental challenges of our time. Residential buildings account for a considerable part of the total electricity consumption and is further a sector that is shown to have large savings potential. Non Intrusive Load Monitoring (NILM), i.e. the deduction of the electricity consumption of individual home appliances from the total electricity consumption of a household, is a compelling approach to deliver appliance specific consumption feedback to consumers. This enables informed choices and can promote sustainable and cost saving actions. To achieve this, accurate and reliable appliance load disaggregation algorithms must be developed. This Master's thesis proposes a novel approach to tackle the disaggregation problem inspired by state of the art algorithms in the field of speech recognition. Previous approaches, for sampling frequencies 1 Hz, have primarily focused on different types of hidden Markov models (HMMs) and occasionally the use of artificial neural networks (ANNs). HMMs are a natural representation of electric appliances, however with a purely generative approach to disaggregation, basically all appliances have to be modelled simultaneously. Due to the large number of possible appliances and variations between households, this is a major challenge. It imposes strong restrictions on the complexity, and thus the expressiveness, of the respective appliance model to make inference algorithms feasible. In this thesis, disaggregation is treated as a factorisation problem where the respective appliance signal has to be extracted from its background. A hybrid model is proposed, where a convolutional neural network (CNN) extracts features that correlate with the state of a single appliance and the features are used as observations for a hidden semi Markov model (HSMM) of the appliance. Since this allows for modelling of a single appliance, it becomes computationally feasible to use a more expressive Markov model. As proof of concept, the hybrid model is evaluated on 238 days of 1 Hz power data, collected from six households, to predict the power usage of the households' washing machine. The hybrid model is shown to perform considerably better than a CNN alone and it is further demonstrated how a significant increase in performance is achieved by including transitional features in the HSMM.
Den ökande energikonsumtionen är en stor utmaning för en hållbar utveckling. Bostäder står för en stor del av vår totala elförbrukning och är en sektor där det påvisats stor potential för besparingar. Non Intrusive Load Monitoring (NILM), dvs. härledning av hushållsapparaters individuella elförbrukning utifrån ett hushålls totala elförbrukning, är en tilltalande metod för att fortlöpande ge detaljerad information om elförbrukningen till hushåll. Detta utgör ett underlag för medvetna beslut och kan bidraga med incitament för hushåll att minska sin miljöpåverakan och sina elkostnader. För att åstadkomma detta måste precisa och tillförlitliga algoritmer för el-disaggregering utvecklas. Denna masteruppsats föreslår ett nytt angreppssätt till el-disaggregeringsproblemet, inspirerat av ledande metoder inom taligenkänning. Tidigare angreppsätt inom NILM (i frekvensområdet 1 Hz) har huvudsakligen fokuserat på olika typer av Markovmodeller (HMM) och enstaka förekomster av artificiella neurala nätverk. En HMM är en naturlig representation av en elapparat, men med uteslutande generativ modellering måste alla apparater modelleras samtidigt. Det stora antalet möjliga apparater och den stora variationen i sammansättningen av dessa mellan olika hushåll utgör en stor utmaning för sådana metoder. Det medför en stark begränsning av komplexiteten och detaljnivån i modellen av respektive apparat, för att de algoritmer som används vid prediktion ska vara beräkningsmässigt möjliga. I denna uppsats behandlas el-disaggregering som ett faktoriseringsproblem, där respektive apparat ska separeras från bakgrunden av andra apparater. För att göra detta föreslås en hybridmodell där ett neuralt nätverk extraherar information som korrelerar med sannolikheten för att den avsedda apparaten är i olika tillstånd. Denna information används som obervationssekvens för en semi-Markovmodell (HSMM). Då detta utförs för en enskild apparat blir det beräkningsmässigt möjligt att använda en mer detaljerad modell av apparaten. Den föreslagna Hybridmodellen utvärderas för uppgiften att avgöra när tvättmaskinen används för totalt 238 dagar av elförbrukningsmätningar från sex olika hushåll. Hybridmodellen presterar betydligt bättre än enbart ett neuralt nätverk, vidare påvisas att prestandan förbättras ytterligare genom att introducera tillstånds-övergång-observationer i HSMM:en.
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SEVERINI, Marco. "Energy and resources management in Micro Grid environments." Doctoral thesis, Università Politecnica delle Marche, 2017. http://hdl.handle.net/11566/245444.

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Nonostante le tecnologie Micro Grid siano ancora in fase sperimentale, il potenziale miglioramento di efficienza robustezza e flessibilità è significativo. Lo spreco di energia e le fluttuazioni del carico possono essere notevolmente ridotte, ciononostante un sistema automatico che gestisca correttamente le risorse risulta necessario per sviluppare completamente il potenziale delle risorse disponibili. Al riguardo, un approccio alla gestione dell’energia, basato su tecniche Mixed Integer Linear Programming è stato esaminato, implementato e proposto. La dissertazione copre gli aspetti teorici del problema, quali le tecniche di gestione MILP, il modello di Micro Grid per due degli scenari più comuni, e gli algoritmi a supporto del sistema di gestione. Le sperimentazioni hanno evidenziato l’efficacia del metodo in termini di efficienza e robustezza. Per migliorare la gestione, si è ritenuto necessario modellare il comportamento di un impianto fotovoltaico reale. Prendendo in considerazione l’effetto dell’ombreggiamento parziale, le performance dell’impianto possono essere valutate, e l’accuratezza nella predizione della produzione di energia solare migliorata. Inoltre, per fornire al gestore lo stato del sistema, un algoritmo capace di monitorare l’attività di ciascun carico a partire dall’analisi del consumo aggregato di energia è stato esaminato. A supporto dell’attività di gestione, inoltre, è stato implementato un algoritmo di schedulazione per dispositivi a consumo ridotto, per lo sviluppo di dispositivi sensore alimentati da fonti rinnovabili impiegabili nei sistemi di lettura automatica dei contatori, così da fornire al manager le informazioni relative al consumo di acqua e gas. A complemento, un algoritmo per l’identificazione delle perdite, per distinguere il consumo effettivo dallo spreco di risorse, è stato investigato.
Althought Micro Grid technologies are still in the experimental phase, the potential improvement of efficiency robustness and flexibility is significant. The energy waste and the load swing can be greatly reduced, nonetheless an automated system that properly manages the resources is required to fully develop the potential of the available resources. On purpose, an energy management system approach, based on Mixed Integer Linear Programming technique has been investigated, implemented and proposed. The dissertation covers the theoretical aspects of the problem, such as the MILP management approach, the model of a Micro Grid for two of the most common scenarios, and the algorithms that support the management system. The experimentations have shown the effectiveness of the approach in terms of management efficiency and robustness. To improve the management, the modelling of the behaviour of a real life photovoltaic power plant has been deemed necessary . By taking into account the effect of partial shading, the actual performance of a plant can be evaluated and thus the accuracy of the forecast of solar energy production can be improved. Additionally, to feed the state of the system back to the manager, an algorithm that monitors the activity of each appliance within the system through the analysis of the aggregated energy consumption has been investigated. To support the management activity, also, a scheduling algorithm aimed at ultra low power devices has been proposed and implemented, as a mean to develop sensor devices powered by renewable energy supply. This type of sensor can be effectively used in automated meter reading systems to provide the manager with the information relating water and gas consumption. Furthermore, a leakage detection algorithm has been developed and investigated to differentiate actual consumption from resource waste.
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Bonfigli, Roberto. "Machine Learning approaches for Non-Intrusive Load Monitoring." Doctoral thesis, Università Politecnica delle Marche, 2018. http://hdl.handle.net/11566/253110.

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La ricerca sulle Smart Grids si è concentrata sulla questione del monitoraggio energetico, in cui il Non-Intrusive Load Monitoring (NILM) rappresenta uno degli argomenti di maggiore interesse: si riferisce alla scomposizione dei dati aggregati di consumo acquisiti in un singolo punto di misurazione nei profili degli elettrodomestici. Questo lavoro riporta uno stato dell'arte aggiornato dei metodi più performanti, con una panoramica dei dataset pubblici disponibili. Tra tutti i metodi proposti, quelli basati su Hidden Markov model (HMM) e su Deep Neural Network (DNN) risultano tra i più performanti. Tra gli approcci basati su HMM, l'algoritmo Additive Factorial Approximate MAP (AFAMAP) è considerato come un modello di riferimento. L'algoritmo AFAMAP è stato esteso, per mezzo di un modello differenziale in avanti. In una seconda fase, viene presentata una formulazione alternativa dello stesso algoritmo, al fine di trattare con HMM bidimensionali, i cui simboli emessi sono i segnali di potenza reattivi attivi congiunti. Gli esperimenti sono condotti sul dataset AMPds, in condizioni di assenza e presenza di rumore. Inoltre, una procedura agevolata di estrazione dell'impronta è presentato in uno scenario reale. Tra gli approcci basati su DNN, il Denoising Autoencoder (dAE) rappresenta uno degli approcci più performanti. In questo lavoro, questo metodo è esteso e migliorato conducendo uno studio dettagliato sulla topologia della rete. Gli esperimenti sono stati condotti su AMPds, UK-DALE e REDD in scenari seen ed unseen in presenza e in assenza di rumore. Inoltre, lo stesso metodo è esplorato quando la dimensione dell'ingresso viene aumentata, includendo la componente di potenza reattiva di consumo di energia. Infine, tecniche simili di intelligenza computazionale sono applicate in altri campi, ossia nella Smart Grid per la distribuzione idrica e gas e in applicazioni audio.
Research on Smart Grids has recently focused on the energy monitoring issue, in which one of the hottest topic is represented by Non-Intrusive Load Monitoring (NILM): it refers to ecomposing the consumption aggregated data acquired at a single point of measurement into the consumption profiles of appliances. This work reports an up-to-date state of the art of most promising NILM methods, with an overview of the public available dataset used. Within all the proposed methods, the Hidden Markov Model (HMM) based and the Deep Neural Network (DNN) based ones have been detected as the most performing. In the HMM based approaches, the Additive Factorial Approximate MAP (AFAMAP) algorithm is nowadays regarded as a reference model. The AFAMAP algorithm has been extended, by means of a differential forward model. In a second step, an alternative formulation of the same algorithm is presented, in order to deal with bivariate HMM, whose emitted symbols are the joint active-reactive power signals. The experiments are conducted on the AMPds dataset, in noised and denoised conditions. Additionally, a user-aided footprint extraction procedure is presented in real scenario. In the DNN based approaches, the Denoising Autoencoder (dAE) represents one of the most performing approaches. In this work, this method is extended and improved by conducting a detailed study on the topology of the network. The experiments have been conducted on the AMPds, UK-DALE, and REDD datasets in seen and unseen scenarios both in presence and in absence of noise. Furthermore, the same method is explored when the input size is increased, including the reactive power component near the active power consumption. Finally, similar computational intelligence techniques are applied in other field, i.e. the smart water and gas grid, and audio application.
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Books on the topic "NILM"

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author, Vrtal Vlastimil, Honzl Jiří author, Jungová Gabriela author, Janák Jiří 1976 author, and Národní muzeum v. Praze, eds. Na březích Nilu: On the banks of the Nile. Praha: Národní muzeum, 2019.

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Daraja juu ya mto Nile: Bridge across the Nile = pont sur le Nil. Dar es Salaam, Tanzania: E&D Vision Publishing Limited, 2015.

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Objevování země na Nilu: Discovering the land on the Nile. Praha: Národní muzeum, 2008.

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Allard-Huard, Léone. Nil-Sahara, dialogues rupestres =: Nile-Sahara, dialogues of the rocks. Divajeu: L. Allard-Huard, 1993.

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Reinhard, Düchting, and Körkel Boris, eds. Wunder des Nil =: Admiranda Nili : 1623 : Faksimile-Ausgabe mit Dokumenten und Nachwort. Heidelberg: Winter, 2000.

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Kolmanič, Karolina. Nila. Ljubljana: Karantanija, 2010.

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Jessica, Agnes. Nila. Jakarta: Pustaka Hermon, 2011.

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Lidd, T. Nile. Bloomington, IN: AuthorHouse, 2008.

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Nilam. Tiruvaṇṇāmalai: Vamci Puks, 2014.

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Jessica, Agnes. Nila. Jakarta: Pustaka Hermon, 2011.

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Book chapters on the topic "NILM"

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Kyrkou, Lamprini, Christoforos Nalmpantis, and Dimitris Vrakas. "Imaging Time-Series for NILM." In Engineering Applications of Neural Networks, 188–96. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-20257-6_16.

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Ellert, Bradley, Stephen Makonin, and Fred Popowich. "Appliance Water Disaggregation via Non-intrusive Load Monitoring (NILM)." In Smart City 360°, 455–67. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-33681-7_38.

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Revuelta Herrero, Jorge, Álvaro Lozano Murciego, Alberto López Barriuso, Daniel Hernández de la Iglesia, Gabriel Villarrubia González, Juan Manuel Corchado Rodríguez, and Rita Carreira. "Non Intrusive Load Monitoring (NILM): A State of the Art." In Advances in Intelligent Systems and Computing, 125–38. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-61578-3_12.

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Chavan, Deepika R., and Dagadu S. More. "A Systematic Review on Low-Resolution NILM: Datasets, Algorithms, and Challenges." In Lecture Notes in Electrical Engineering, 101–20. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-9488-2_9.

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Alami C., Mohamed, Jérémie Decock, Rim kaddah, and Jesse Read. "Conv-NILM-Net, a Causal and Multi-appliance Model for Energy Source Separation." In Communications in Computer and Information Science, 207–22. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-23633-4_15.

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Figueiredo, Marisa B., Ana de Almeida, and Bernardete Ribeiro. "An Experimental Study on Electrical Signature Identification of Non-Intrusive Load Monitoring (NILM) Systems." In Adaptive and Natural Computing Algorithms, 31–40. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-20267-4_4.

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Kamoto, Kondwani M., and Qi Liu. "Monitoring Home Energy Usage Using an Unsupervised NILM Algorithm Based on Entropy Index Constraints Competitive Agglomeration (EICCA)." In Cloud Computing and Security, 478–90. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-00018-9_42.

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Rühling, Lutz. "Ferlin, Nils." In Kindlers Literatur Lexikon (KLL), 1. Stuttgart: J.B. Metzler, 2020. http://dx.doi.org/10.1007/978-3-476-05728-0_9309-1.

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Stigand, C. H., and Reginald Wingate. "The Nile." In Equatoria, 117–30. London: Routledge, 2021. http://dx.doi.org/10.4324/9781315094403-11.

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Malik, Yashpal Singh, Arockiasamy Arun Prince Milton, Sandeep Ghatak, and Souvik Ghosh. "West Nile." In Livestock Diseases and Management, 39–55. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-4554-9_4.

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Conference papers on the topic "NILM"

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Faustine, Anthony, Lucas Pereira, Hafsa Bousbiat, and Shridhar Kulkarni. "UNet-NILM." In BuildSys '20: The 7th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3427771.3427859.

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Kelly, Jack, and William Knottenbelt. "Neural NILM." In BuildSys '15: The 2nd ACM International Conference on Embedded Systems for Energy-Efficient Built Environments. New York, NY, USA: ACM, 2015. http://dx.doi.org/10.1145/2821650.2821672.

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Ahmed, Shamim, and Marc Bons. "Edge computed NILM." In BuildSys '20: The 7th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3427771.3427852.

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Murray, David, Lina Stankovic, and Vladimir Stankovic. "Explainable NILM Networks." In BuildSys '20: The 7th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3427771.3427855.

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Diou, Christos, and Georgios Andreou. "eeRIS-NILM: An Open Source, Unsupervised Baseline for Real-Time Feedback Through NILM." In 2020 55th International Universities Power Engineering Conference (UPEC). IEEE, 2020. http://dx.doi.org/10.1109/upec49904.2020.9209866.

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Jacobs, Gilles, and Pierre Henneaux. "Unsupervised learning procedure for NILM applications." In 2020 IEEE 20th Mediterranean Electrotechnical Conference ( MELECON). IEEE, 2020. http://dx.doi.org/10.1109/melecon48756.2020.9140477.

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Hussein, Neveen M., Ahmed M. Hesham, and Mohsen A. Rashawn. "States and Power Consumption Estimation for NILM." In 2019 14th International Conference on Computer Engineering and Systems (ICCES). IEEE, 2019. http://dx.doi.org/10.1109/icces48960.2019.9068152.

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Papageorgiou, Petros G., Paschalis A. Gkaidatzis, Georgios C. Christoforidis, and Aggelos S. Bouhouras. "Unsupervised NILM Implementation Using Odd Harmonic Currents." In 2021 56th International Universities Power Engineering Conference (UPEC). IEEE, 2021. http://dx.doi.org/10.1109/upec50034.2021.9548250.

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Jacobs, Gilles, and Pierre Henneaux. "Specific and generic unsupervised algorithms for NILM applications." In 2020 International Conference on Smart Energy Systems and Technologies (SEST). IEEE, 2020. http://dx.doi.org/10.1109/sest48500.2020.9203555.

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Osathanunkul, Kitisak, and Khukrit Osathanunkul. "Different Sampling Rates on Neural NILM Energy Disaggregation." In 2019 Joint International Conference on Digital Arts, Media and Technology with ECTI Northern Section Conference on Electrical, Electronics, Computer and Telecommunications Engineering (ECTI DAMT-NCON). IEEE, 2019. http://dx.doi.org/10.1109/ecti-ncon.2019.8692281.

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Reports on the topic "NILM"

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Christensen, Dane, Lieko Earle, and Bethany Sparn. NILM Applications for the Energy-Efficient Home. Office of Scientific and Technical Information (OSTI), November 2012. http://dx.doi.org/10.2172/1056133.

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Kukushkina, Nataliya. The Nile. Basin of the river. Edited by Nikolay Komedchikov. Entsiklopediya, January 2012. http://dx.doi.org/10.15356/dm2015-12-10-4.

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Costrell, Louis, Frank R. Lenkszus, Stanley J. Rudnick, Eric Davey, John Gould, Seymour Rankowitz, William P. Sims, et al. Standard NIM Instrumentation System. Office of Scientific and Technical Information (OSTI), May 1990. http://dx.doi.org/10.2172/7120327.

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Ubbelohde, Kurt F. Freshwater Scarcity in the Nile River Basin. Fort Belvoir, VA: Defense Technical Information Center, April 2000. http://dx.doi.org/10.21236/ada378148.

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Pope, G. F., and R. J. McDonald. Computerized CAMAC and NIM module library. Office of Scientific and Technical Information (OSTI), August 1990. http://dx.doi.org/10.2172/6225767.

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Mancuso, Marina. Climate and infection-age on West Nile Virus transmission. Office of Scientific and Technical Information (OSTI), October 2022. http://dx.doi.org/10.2172/1894805.

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Trujillo, Estevan R. National Instrument List Mode Data Acquisition (NILA) System User Manual. Office of Scientific and Technical Information (OSTI), August 2013. http://dx.doi.org/10.2172/1091866.

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Migongo-Bake, Catacutan, and Namirembe. Assessment of the headwaters of the Blue Nile in Ethiopia. World Agroforestry Centre (ICRAF), 2012. http://dx.doi.org/10.5716/wp12160.pdf.

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Crepeau, Paul J., and John C. McCanless. Coding and Synchronization Analysis of the NILE UHF Fixed-Frequency Waveform. Fort Belvoir, VA: Defense Technical Information Center, September 1995. http://dx.doi.org/10.21236/ada298808.

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Smith, Margaret, Nurit Katzir, Susan McCouch, and Yaakov Tadmor. Discovery and Transfer of Genes from Wild Zea Germplasm to Improve Grain Oil and Protein Composition of Temperate Maize. United States Department of Agriculture, 1998. http://dx.doi.org/10.32747/1998.7580683.bard.

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Project Objectives 1. Develop and amplify two interspecific populations (annual and perennial teosintes x elite maize inbred) as the basis for genetic analysis of grain quality. 2. Identify quantitative trait loci (QTLs) from teosinte that improve oil, protein, and essential amino acid composition of maize grain. 3. Develop near isogenic lines (NILs) to quantify QTL contributions to grain quality and as a resource for future breeding and gene cloning efforts. 4. Analyze the contribution of these QTLs to hybrid performance in both the US and Israel. 5. Measure the yield potential of improved grain quality hybrids. (NOTE: Yield potential could not be evaluated due to environmentally-caused failure of the breeding nursery where seed was produced for this evaluation.) Background: Maize is a significant agricultural commodity worldwide. As an open pollinated crop, variation within the species is large and, in most cases, sufficient to supply the demand for modem varieties and for new environments. In recent years there is a growing demand for maize varieties with special quality attributes. While domesticated sources of genetic variation for high oil and protein content are limited, useful alleles for these traits may remain in maize's wild relative, teosinte. We utilized advanced backcross (AB) analysis to search for QTLs contributing to oil and protein content from two teosinte accessions: Zea mays ssp. mexicana Race Chalco, an annual teosinte (referred to as Chalco), and Z diploperennis Race San Miguel, a perennial teosinte (referred to as Diplo). Major Conclusions and Achievements Two NILs targeting a Diplo introgression in bin 1.04 showed a significant increase in oil content in homozygous sib-pollinated seed when compared to sibbed seed of their counterpart non-introgressed controls. These BC4S2 NILs, referred to as D-RD29 and D-RD30, carry the Diplo allele in bin 1.04 and the introgression extends partially into bins 1.03 and 1.05. These NILs remain heterozygous in bins 4.01 and 8.02, but otherwise are homozygous for the recurrent parent (RD6502) alleles. NILs were developed also for the Chalco introgression in bin 1.04 but these do not show any improvement in oil content, suggesting that the Chalco alleles differ from the Diplo alleles in this region. Testcross Fl seed and sibbed grain from these Fl plants did not show any effect on oil content from this introgression, suggesting that it would need to be present in both parents of a maize hybrid to have an effect on oil content. Implications, both Scientific and Agricultural The Diplo region identified increases oil content by 12.5% (from 4.8% to 5.4% oil in the seed). Although this absolute difference is not large in agronomic terms, this locus could provide additive increases to oil content in combination with other maize-derived loci for high oil. To our knowledge, this is the first confirmed report of a QTL from teosinte for improved grain oil content in maize. It suggests that further research on grain quality alleles from maize wild relatives would be of both scientific and agricultural interest.
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