Relevant bibliographies by topics / Appliance classification

Academic literature on the topic 'Appliance classification'

Author: Grafiati
Published: 10 March 2023

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles

Select a source type:

Contents

Consult the lists of relevant articles, books, theses, conference reports, and other scholarly sources on the topic 'Appliance classification.'

Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.

You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.

Journal articles on the topic "Appliance classification"

1

Faustine, Anthony, and Lucas Pereira. "Improved Appliance Classification in Non-Intrusive Load Monitoring Using Weighted Recurrence Graph and Convolutional Neural Networks." Energies 13, no. 13 (July 1, 2020): 3374. http://dx.doi.org/10.3390/en13133374.

Full text
Abstract:
Appliance recognition is one of the vital sub-tasks of NILM in which a machine learning classier is used to detect and recognize active appliances from power measurements. The performance of the appliance classifier highly depends on the signal features used to characterize the loads. Recently, different appliance features derived from the voltage–current (V–I) waveforms have been extensively used to describe appliances. However, the performance of V–I-based approaches is still unsatisfactory as it is still not distinctive enough to recognize devices that fall into the same category. Instead, we propose an appliance recognition method utilizing the recurrence graph (RG) technique and convolutional neural networks (CNNs). We introduce the weighted recurrent graph (WRG) generation that, given one-cycle current and voltage, produces an image-like representation with more values than the binary output created by RG. Experimental results on three different sub-metered datasets show that the proposed WRG-based image representation provides superior feature representation and, therefore, improves classification performance compared to V–I-based features.
APA, Harvard, Vancouver, ISO, and other styles
2

Jiang, Lei, Jiaming Li, Suhuai Luo, Sam West, and Glenn Platt. "Power Load Event Detection and Classification Based on Edge Symbol Analysis and Support Vector Machine." Applied Computational Intelligence and Soft Computing 2012 (2012): 1–10. http://dx.doi.org/10.1155/2012/742461.

Full text
Abstract:
Energy signature analysis of power appliance is the core of nonintrusive load monitoring (NILM) where the detailed data of the appliances used in houses are obtained by analyzing changes in the voltage and current. This paper focuses on developing an automatic power load event detection and appliance classification based on machine learning. In power load event detection, the paper presents a new transient detection algorithm. By turn-on and turn-off transient waveforms analysis, it can accurately detect the edge point when a device is switched on or switched off. The proposed load classification technique can identify different power appliances with improved recognition accuracy and computational speed. The load classification method is composed of two processes including frequency feature analysis and support vector machine. The experimental results indicated that the incorporation of the new edge detection and turn-on and turn-off transient signature analysis into NILM revealed more information than traditional NILM methods. The load classification method has achieved more than ninety percent recognition rate.
APA, Harvard, Vancouver, ISO, and other styles
3

Matindife, Liston, Yanxia Sun, and Zenghui Wang. "A Machine-Learning Based Nonintrusive Smart Home Appliance Status Recognition." Mathematical Problems in Engineering 2020 (September 18, 2020): 1–21. http://dx.doi.org/10.1155/2020/9356165.

Full text
Abstract:
In a smart home, the nonintrusive load monitoring recognition scheme normally achieves high appliance recognition performance in the case where the appliance signals have widely varying power levels and signature characteristics. However, it becomes more difficult to recognize appliances with equal or very close power specifications, often with almost identical signature characteristics. In literature, complex methods based on transient event detection and multiple classifiers that operate on different hand crafted features of the signal have been proposed to tackle this issue. In this paper, we propose a deep learning approach that dispenses with the complex transient event detection and hand crafting of signal features to provide high performance recognition of close tolerance appliances. The appliance classification is premised on the deep multilayer perceptron having three appliance signal parameters as input to increase the number of trainable samples and hence accuracy. In the case where we have limited data, we implement a transfer learning-based appliance classification strategy. With the view of obtaining an appropriate high performing disaggregation deep learning network for the said problem, we explore individually three deep learning disaggregation algorithms based on the multiple parallel structure convolutional neural networks, the recurrent neural network with parallel dense layers for a shared input, and the hybrid convolutional recurrent neural network. We disaggregate a total of three signal parameters per appliance in each case. To evaluate the performance of the proposed method, some simulations and comparisons have been carried out, and the results show that the proposed method can achieve promising performance.
APA, Harvard, Vancouver, ISO, and other styles
4

Kim, Hwan, and Sungsu Lim. "Temporal Patternization of Power Signatures for Appliance Classification in NILM." Energies 14, no. 10 (May 19, 2021): 2931. http://dx.doi.org/10.3390/en14102931.

Full text
Abstract:
Non-Intrusive Load Monitoring (NILM) techniques are effective for managing energy and for addressing imbalances between the energy demand and supply. Various studies based on deep learning have reported the classification of appliances from aggregated power signals. In this paper, we propose a novel approach called a temporal bar graph, which patternizes the operational status of the appliances and time in order to extract the inherent features from the aggregated power signals for efficient load identification. To verify the effectiveness of the proposed method, a temporal bar graph was applied to the total power and tested on three state-of-the-art deep learning techniques that previously exhibited superior performance in image classification tasks—namely, Extreme Inception (Xception), Very Deep One Dimensional CNN (VDOCNN), and Concatenate-DenseNet121. The UK Domestic Appliance-Level Electricity (UK-DALE) and Tracebase datasets were used for our experiments. The results of the five-appliance case demonstrated that the accuracy and F1-score increased by 19.55% and 21.43%, respectively, on VDOCNN, and by 33.22% and 35.71%, respectively, on Xception. A performance comparison with the state-of-the-art deep learning methods and image-based spectrogram approach was conducted.
APA, Harvard, Vancouver, ISO, and other styles
5

Kulkarni, Anand Sunil, Cindy K. Harnett, and Karla Conn Welch. "EMF Signature for Appliance Classification." IEEE Sensors Journal 15, no. 6 (June 2015): 3573–81. http://dx.doi.org/10.1109/jsen.2014.2379113.

Full text
APA, Harvard, Vancouver, ISO, and other styles
6

Baptista, Darío, Sheikh Mostafa, Lucas Pereira, Leonel Sousa, and Fernando Morgado-Dias. "Implementation Strategy of Convolution Neural Networks on Field Programmable Gate Arrays for Appliance Classification Using the Voltage and Current (V-I) Trajectory." Energies 11, no. 9 (September 17, 2018): 2460. http://dx.doi.org/10.3390/en11092460.

Full text
Abstract:
Specific information about types of appliances and their use in a specific time window could help determining in details the electrical energy consumption information. However, conventional main power meters fail to provide any specific information. One of the best ways to solve these problems is through non-intrusive load monitoring, which is cheaper and easier to implement than other methods. However, developing a classifier for deducing what kind of appliances are used at home is a difficult assignment, because the system should identify the appliance as fast as possible with a higher degree of certainty. To achieve all these requirements, a convolution neural network implemented on hardware was used to identify the appliance through the voltage and current (V-I) trajectory. For the implementation on hardware, a field programmable gate array (FPGA) was used to exploit processing parallelism in order to achieve optimal performance. To validate the design, a publicly available Plug Load Appliance Identification Dataset (PLAID), constituted by 11 different appliances, has been used. The overall average F-score achieved using this classifier is 78.16% for the PLAID 1 dataset. The convolution neural network implemented on hardware has a processing time of approximately 5.7 ms and a power consumption of 1.868 W.
APA, Harvard, Vancouver, ISO, and other styles
7

Faustine, Anthony, and Lucas Pereira. "Multi-Label Learning for Appliance Recognition in NILM Using Fryze-Current Decomposition and Convolutional Neural Network." Energies 13, no. 16 (August 11, 2020): 4154. http://dx.doi.org/10.3390/en13164154.

Full text
Abstract:
The advance in energy-sensing and smart-meter technologies have motivated the use of a Non-Intrusive Load Monitoring (NILM), a data-driven technique that recognizes active end-use appliances by analyzing the data streams coming from these devices. NILM offers an electricity consumption pattern of individual loads at consumer premises, which is crucial in the design of energy efficiency and energy demand management strategies in buildings. Appliance classification, also known as load identification is an essential sub-task for identifying the type and status of an unknown load from appliance features extracted from the aggregate power signal. Most of the existing work for appliance recognition in NILM uses a single-label learning strategy which, assumes only one appliance is active at a time. This assumption ignores the fact that multiple devices can be active simultaneously and requires a perfect event detector to recognize the appliance. In this paper proposes the Convolutional Neural Network (CNN)-based multi-label learning approach, which links multiple loads to an observed aggregate current signal. Our approach applies the Fryze power theory to decompose the current features into active and non-active components and use the Euclidean distance similarity function to transform the decomposed current into an image-like representation which, is used as input to the CNN. Experimental results suggest that the proposed approach is sufficient for recognizing multiple appliances from aggregated measurements.
APA, Harvard, Vancouver, ISO, and other styles
8

Matindife, L., Y. Sun, and Z. Wang. "Few-Shot Learning for Image-Based Nonintrusive Appliance Signal Recognition." Computational Intelligence and Neuroscience 2022 (August 23, 2022): 1–14. http://dx.doi.org/10.1155/2022/2142935.

Full text
Abstract:
In this article, we present the recognition of nonintrusive disaggregated appliance signals through a reduced dataset computer vision deep learning approach. Deep learning data requirements are costly in terms of acquisition time, storage memory requirements, computation time, and dynamic memory usage. We develop our recognition strategy on Siamese and prototypical reduced data few-shot classification algorithms. Siamese networks address the 1-shot recognition well. Appliance activation periods vary considerably, and this can result in imbalance in the number of appliance-specific generated signal images. Prototypical networks address the problem of data imbalance in training. By first carrying out a similarity test on the entire dataset, we establish the quality of our data before input into the deep learning algorithms. The results give acceptable performance and show the promise of few-shot learning in recognizing appliances in the nonintrusive load-monitoring scheme for very limited data samples.
APA, Harvard, Vancouver, ISO, and other styles
9

Azizi, Elnaz, Mohammad T. H. Beheshti, and Sadegh Bolouki. "Event Matching Classification Method for Non-Intrusive Load Monitoring." Sustainability 13, no. 2 (January 12, 2021): 693. http://dx.doi.org/10.3390/su13020693.

Full text
Abstract:
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.
APA, Harvard, Vancouver, ISO, and other styles
10

Massidda, Luca, Marino Marrocu, and Simone Manca. "Non-Intrusive Load Disaggregation by Convolutional Neural Network and Multilabel Classification." Applied Sciences 10, no. 4 (February 21, 2020): 1454. http://dx.doi.org/10.3390/app10041454.

Full text
Abstract:
Non-intrusive load monitoring (NILM) is the main method used to monitor the energy footprint of a residential building and disaggregate total electrical usage into appliance-related signals. The most common disaggregation algorithms are based on the Hidden Markov Model, while solutions based on deep neural networks have recently caught the attention of researchers. In this work we address the problem through the recognition of the state of activation of the appliances using a fully convolutional deep neural network, borrowing some techniques used in the semantic segmentation of images and multilabel classification. This approach has allowed obtaining high performances not only in the recognition of the activation state of the domestic appliances but also in the estimation of their consumptions, improving the state of the art for a reference dataset.
APA, Harvard, Vancouver, ISO, and other styles
More sources

Dissertations / Theses on the topic "Appliance classification"

1

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.

Full text
Abstract:
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.
APA, Harvard, Vancouver, ISO, and other styles
2

Basu, Kaustav. "Techniques avancées de classification pour l'identification et la prédiction non intrusive de l'état des charges dans le bâtiment." Thesis, Grenoble, 2014. http://www.theses.fr/2014GRENT089/document.

Full text
Abstract:
Nous abordons dans ces travaux l’identification non intrusive des charges des bâtiments résidentiels ainsi que la prédiction de leur état futur. L'originalité de ces travaux réside dans la méthode utilisée pour obtenir les résultats voulus, à savoir l'analyse statistique des données(algorithmes de classification). Celle-ci se base sur des hypothèses réalistes et restrictives sans pour autant avoir de limitation sur les modèles comportementaux des charges (variations de charges ou modèles) ni besoin de la connaissance des changements d'état des charges. Ainsi, nous sommes en mesure d’identifier et/ou de prédire l'état des charges consommatrices d'énergie (et potentiellement contrôlables) en se basant uniquement sur une phase d'entrainement réduite et des mesures de puissance active agrégée sur un pas de mesure de dix minutes, préservant donc la vie privée des habitants.Dans cette communication, après avoir décrit la méthodologie développée pour classifier les charges et leurs états, ainsi que les connaissances métier fournies aux algorithmes, nous comparons les résultats d’identification pour cinq algorithmes tirés de l'état de l'art et les utilisons comme support d'application à la prédiction. Les algorithmes utilisés se différencient par leur capacité à traiter des problèmes plus ou moins complexe (notamment la prise en compte de relations entre les charges) et se ne révèlent pas tous appropriés à tout type de charge dans le bâtiment résidentiel
Smart metering is one of the fundamental units of a smart grid, as many further applicationsdepend on the availability of fine-grained information of energy consumption and production.Demand response techniques can be substantially improved by processing smart meter data to extractrelevant knowledge of appliances within a residence. The thesis aims at finding generic solutions for thenon-intrusive load monitoring and future usage prediction of residential loads at a low sampling rate.Load monitoring refers to the dis-aggregation of individual loads from the total consumption at thesmart meter. Future usage prediction of appliances are important from the energy management point ofview. In this work, state of the art multi-label temporal classification techniques are implemented usingnovel set of features. Moreover, multi-label classifiers are able to take inter-appliance correlation intoaccount. The methods are validated using a dataset of residential loads in 100 houses monitored over aduration of 1-year
APA, Harvard, Vancouver, ISO, and other styles
3

Nguyen, Thien-Minh. "Contribution to the analysis and understanting of electrical-grid signals with signal processing and machine learning techniques." Thesis, Mulhouse, 2017. http://www.theses.fr/2017MULH9234/document.

Full text
Abstract:
Ce travail de thèse propose des approches d’identification et de reconnaissance des harmoniques de courant qui sont basées sur des stratégies d’apprentissage automatique. Les approches proposées s’appliquent directement dans les dispositifs d’amélioration de la qualité de l’énergie électrique.Des structures neuronales complètes, dotées de capacités d’apprentissage automatique, ont été développées pour identifier les composantes harmoniques d’un signal sinusoïdal au sens large et plus spécifiquement d’un courant alternatif perturbé par des charges non linéaires. L’identification des harmoniques a été réalisée avec des réseaux de neurones de type Multi–Layer Perceptron (MLP). Plusieurs schémas d’identification ont été développés, ils sont basés sur un réseau MLP composé de neurones linéaire ou sur plusieurs réseaux MLP avec des apprentissages spécifiques. Les harmoniques d’un signal perturbé sont identifiées avec leur amplitude et leur phase, elles peuvent servir à générer des courants de compensation pour améliorer la forme du courant électrique. D’autres approches neuronales a été développées pour reconnaître les charges. Elles consistent en des réseaux MLP ou SVM (Support Vector Machine) et fonctionnent en tant que classificateurs. Leur apprentissage permet à partir des harmoniques de courant de reconnaître le type de charge non linéaire qui génère des perturbations dans le réseau électrique. Toutes les approches d’identification et de reconnaissance des harmoniques ont été validées par des tests de simulation à l’aide des données expérimentales. Des comparaisons avec d’autres méthodes ont démontré des performances supérieures et une meilleure robustesse
This thesis proposes identifying approaches and recognition of current harmonics that are based on machine learning strategies. The approaches are applied directly in the quality improvement devices of electric energy and in energy management solutions. Complete neural structures, equipped with automatic learning capabilities have been developed to identify the harmonic components of a sinusoidal signal at large and more specifically an AC disturbed by non–linear loads. The harmonic identification is performed with multilayer perceptron neural networks (MLP). Several identification schemes have been developed. They are based on a MLP neural network composed of linear or multiple MLP networks with specific learning. Harmonics of a disturbed signal are identified with their amplitude and phases. They can be used to generate compensation currents fed back into the network to improve the waveform of the electric current. Neural approaches were developed to distinguish and to recognize the types of harmonics and is nonlinear load types that are at the origin. They consist of MLP or SVM (Support Vector Machine) acting as classifier that learns the harmonic profile of several types of predetermined signals and representative of non–linear loads. They entry are the parameters of current harmonics of the current wave. Learning can recognize the type of nonlinear load that generates disturbances in the power network. All harmonics identification and recognition approaches have been validated by simulation tests or using experimental data. The comparisons with other methods have demonstrated superior characteristics in terms of performance and robustness
APA, Harvard, Vancouver, ISO, and other styles
4

Сетун, М. А. "Товарознавча оцінка асортименту електроприладів та удосконалення організації торгівлі ними на прикладі магазину «Електроніка» м. Чернігів." Thesis, Чернігів, 2020. http://ir.stu.cn.ua/123456789/21667.

Full text
Abstract:
Сетун, М. А. Товарознавча оцінка асортименту електроприладів та удосконалення організації торгівлі ними на прикладі магазину «Електроніка» м. Чернігів : магістерська робота : 076 Підприємництво, торгівля та біржова діяльність / М. А. Сетун ; керівник роботи Денисенко Т. М. ; Національний університет «Чернігівська політехніка», кафедра підприємництва і торгівлі. – Чернігів, 2020. – 64 с.
Об’єкт дослідження - електроприлади та магазин «Електроніка», м.Чернігів. Предмет дослідження - асортимент електроприладів та організація торгівлі. Метою випускної кваліфікаційної роботи є визначення поняття, видів та оцінка формування товарного асортименту і якості електротоварів в магазині в сучасних економічних умовах в Україні. У першому розділі представлено детальний аналіз ринку електроприладів. Наведено динаміку імпорту та експорту електроприладів в Україні в 2018-2019 роках, зокрема, взято питання експорту та імпорту у м. Чернігів. Визначені основні країни експортери та імпортери електроприладів в Україні. Проаналізовано класифікацію асортименту електроприладів, вимоги до якості та маркування. У другому розділі подано товарознавчу характеристику асортименту електроприладівв магазині «Електроніка» за показниками асортименту. Обґрунтовано фактори,щомають вирішальний вплив на покупку. Проаналізовано організаційну структуру магазину, основні економічні показники господарської діяльності та організацію продажу електроприладів. Третій розділприсвячений оцінці ефективності діяльності магазину та шляхам покращення організації торгівлі при використанні засобів мерчандайзингу.
The object of research - electrical appliances and the store "Electronics", Chernihiv. The subject of research - the range of electrical appliances and trade organization. The purpose of the final qualifying work is to define the concept, types and assessment of the formation of the product range and quality of electrical goods in the store in modern economic conditions in Ukraine. The first part presents a detailed analysis of the electrical appliance market. The dynamics of import and export of electrical appliances in Ukraine in 2018-2019 are described. In particular, the issue of export and import in Chernihiv is taken for description. The main countries exporting and importers of electrical appliances in Ukraine have been identified. The classification of the range of electrical appliances, quality requirements and labeling have been analyzed. The second part presents the commodity characteristics of the range of electrical appliances in the store "Electronics" in terms of range. The factors that have a decisive influence on the purchase were substantiated. The organizational structure of the store, the main economic indicators of economic activity and the organization of sales of electrical appliances were analyzed. The third part is devoted to assessing the effectiveness of the store and how to improve the organization of trade with the help of merchandising.
APA, Harvard, Vancouver, ISO, and other styles
5

Klisch, Nico. "Die Therapie schlafbezogener Atmungsstörungen mit Hilfe eines den Unterkiefer protrudierenden Schienensystems." Doctoral thesis, 2014. https://ul.qucosa.de/id/qucosa%3A13307.

Full text
Abstract:
50 Patienten wurden mit einem herausnehmbaren unterkiefervorverlagernden Schienensystem im Zeitraum der Jahre 2000 bis 2002 behandelt. Die schlafmedizinisch-diagnostischen Untersuchungen vor Therapiebeginn erfolgten bei 25 Patienten ambulant und bei den anderen 25 Patienten stationär. Es wurden schlafmedizinischen Parameter zur Diagnosestellung herangezogen. Hälftig bestanden bei diesen Patienten die schlafmedizinische Diagnosen einer Rhonchopathie (reine Schnarcher, ohne internistische Besonderheiten) und einer leicht bis mittelgradigen obstruktiven Schlafapnoe. Bei 23 Patienten mit einer Rhonchopathie wurde das Funktionsprinzip der Unterkiefervorverlagerung durch den Wilcoxon-Test bei der Veränderung der schlafmedizinischen Parameter Entsättigungsindex, Schnarchindex, Anzahl der Entsättigungen und niederste Entsättigung bestätigt. Bei zwei (8%) der 25 Patienten veränderten sich die Parameter nicht positiv, so dass die Schienentherapie abgebrochen wurde. Bei 20 Patienten mit leicht- bis mittelgradiger Schlafapnoe bestätigte der Wilcoxon-Test die Signifikanz der Veränderungen bei den polysomnographischen Werten AHI , REM und Schlafeffizienz. Die Signifkanzschranke wurde bei dem somnologischem Wert Tiefschlaf nicht erreicht. Obgleich bei 13 Patienten die Schienentherapie aus unterschiedlichen Gründen innerhalb der zwei Untersuchungsjahre abgebrochen werden musste, wurde die Behandlung bei den 37 verbliebenen Patienten (74%) als erfolgreich und zufriedenstellend eingeschätzt.
APA, Harvard, Vancouver, ISO, and other styles
6

PARADISO, FRANCESCA. "Smart Home: energy monitoring and exploitation of network virtualization." Doctoral thesis, 2017. http://hdl.handle.net/2158/1080014.

Full text
Abstract:
The Smart Home concept refers to a domestic network environment able to provide connectivity, adaptivity and automation through different kinds of smart device to improve inhabitants’ awareness, safety and comfort. This thesis is concerned with the evolution of the Smart Home Environment towards a new virtual version by mainly dealing with two topics. First, on the application perspective, focus is made on the crucial issue of improving user awareness on power consumptions for achieving energy savings. Second, on the technological infrastructure perspective, it investigates possible impact and related issues of the adoption of network virtualization and programmability technologies in the Smart Home networking by providing a contribution as regards VNF Placement problem. The rational use of energy has recently become one of the most pressing research topic because of the constantly growing consumptions in contrast with the scarcity of resources. In a Smart Home scenario the recent progress of technology, along with lower costs, has made it possible to perform energy monitoring and management actions through the distribution of smart meters and environmental sensors capable of providing information to a Home Energy Management System (HEMS). Recent studies have shown that informing users about the actual appliances consumption as well as device-usage habits, can help to obtain energy consumption reduction in private households. In order to achieve this goal a supervised classification algorithm for detecting and identifying consuming appliances has been implemented. Then a Non-Intrusive Load Monitoring (NILM) approach has been investigated to reduce the cost of attaching a single meter (i.e. smart plug) to each device; the proposed algorithm aims at recognizing the power consumption of a specific device from the whole-house consumption profile and from the input of context information (i.e. the user presence in the house and the hourly utilization of appliances). As regards the technological perspective, the Network Function Virtualization (NFV) technology, together with the complementary Software Defined Networking (SDN) paradigm, let envisage a revolution in the traditional concept of network service delivery and availability. This evolution promises to enable on-demand and flexible services provision as it allows to separate the network functions from the hardware they run on by leveraging the virtualization abstraction. This work addresses the issue of optimal VNF placement in a multi-stakeholder network infrastructure by considering the framework of a NFV Management and Orchestration architecture that leverages the Software Defined Networking paradigm. Given a set of service requests and considering a set of constraints (e.g., maximum end-to-end delay, monetary cost, allowed server utilization level), a mathematical model has been formulated to maximize the profit that can be obtained by both tenants (i.e. Infrastructure providers, Cloud providers) and renters (i.e. service providers/users). In order to favour generalization and to ease the treatment of aspects that in literature have not accounted (e.g. multiple users), the choice of the actual forwarding path of incoming traffic flows is deferred to a later step (optimal routing), to be performed by the SDN Controller. Moreover, the work provides a detailed formalization of service requests and Data Centers and considers two types of users with different privileges (i.e. Premium and Best Effort). The energy efficiency and sustainability goals have been also taken into account.
APA, Harvard, Vancouver, ISO, and other styles
7

Dickert, Jörg. "Synthese von Zeitreihen elektrischer Lasten basierend auf technischen und sozialen Kennzahlen: Grundlage für Planung, Betrieb und Simulation von aktiven Verteilungsnetzen." Doctoral thesis, 2015. https://tud.qucosa.de/id/qucosa%3A29601.

Full text
Abstract:
Kenntnisse über das prinzipielle Verhalten der Lasten und deren Benutzung durch die Endabnehmer sind im Wesentlichen vorhanden. Viele der aktuell notwendigen Untersuchungen benötigen jedoch Zeitreihen elektrischer Lasten, sogenannte Lastgänge. Mit der Synthese von Zeitreihen elektrischer Lasten können unter Berücksichtigung verschiedenster Anforderungen Lastgänge aufgebaut werden, wobei in dieser Arbeit der Fokus auf Haushaltsabnehmer liegt. Wichtige Eingangsdaten für die Lastgangsynthese sind die technischen Kenngrößen der elektrischen Geräte und die sozialen Kennzahlen zur Benutzung der Geräte durch die Endabnehmer. Anhand dieser Eingangsdaten wird die Lastgangsynthese durchgeführt und werden Anwendungsbeispiele dargestellt. Die Entwicklung von klassischen Versorgungsnetzen hin zu aktiven Verteilungsnetzen ist bedingt durch neue Verbraucher, wie Wärmepumpen, Elektroautos, sowie vielen dezentralen Erzeugungsanlagen. Speziell die fluktuierende Einspeisung durch Photovoltaik-Anlagen ist Anlass zur Forderung nach einem Verbrauchs- und Lastmanagement. Mit dem Verbrauchsmanagement wird die Last an die Einspeisung angepasst und das Lastmanagement berücksichtigt zusätzlich die Versorgungssituation des Netzes. Für die Lastgangsynthese werden die Haushaltsgeräte in fünf Geräteklassen unterteilt, für die spezifische Kennzahlen aus technischer und sozialer Sicht angegeben werden. Diese Kennzahlen sind Leistung pro Gerät oder Energieverbrauch pro Nutzung sowie Ausstattungsgrade, Benutzungshäufigkeiten und Zeiten für das Ein- und Ausschalten der Geräte. Damit wird ein neuer Ansatz gewählt, welcher nicht mehr auf die detaillierte Beschreibung des Bewohnerverhaltens beruht, da die Datenbereitstellung dafür äußerst schwierig war und ist. Vorzugsweise in Niederspannungsnetzen sind mit synthetischen Zeitreihen umfangreiche und umfassende Untersuchungen realisierbar. Es gibt verschiedenste Möglichkeiten, die Zeitreihen zusammenzustellen. Mit Lastgängen je Außenleiter können beispielsweise unsymmetrische Zustände der Netze analysiert werden. Zudem können auch Lastgänge für Geräte bzw. Gerätegruppen erstellt werden, welche für Potenzialanalysen des Verbrauchsmanagement essenziell sind. Der wesentliche Unterschied besteht darin, dass viele Berechnungen nicht mehr auf deterministische Extremwerte beruhen, sondern die stochastischen Eigenschaften der Endabnehmer mit den resultierenden Lastgängen berücksichtigt werden.
Distributed generation and novel loads such as electric vehicles and heat pumps require the development towards active distribution networks. Load curves are needed for the appropriate design process. This thesis presents a feasible and expandable synthesis of load curves, which is performed exemplary on residential customers with a period under review of 1 year and time steps of as little as 30 s. The data is collected for up-to-date appliances and current statics examining the way of life. The main focus lies on the input data for the synthesis and distinguishes between technical and social factors. Some thirty home appliances have been analyzed and are classified into five appliance classes by incorporating switching operations and power consumptions. The active power is the key figure for the technical perspective and the data is derived from manufacturer information. For the social perspective six different customer types are defined. They differ in sizes of household and housekeeping. The social key figures are appliance penetration rate and depending on the appliance class the turn-on time, turn-off time, operating duration or cycle duration. The elaborated two-stage synthesis is efficiently implemented in Matlab®. First, artificial load curves are created for each appliance of the households under consideration of the appliance class. In the second step, the individual load curves of the appliances are combined to load curves per line conductor. The algorithms have been validated in the implementation process by retracing the input data in the load curves. Also, the feasibility of the results is shown by comparing the key figures maximum load and power consumption to data in literature. The generated load curves allow for unsymmetrical calculations of distribution systems and can be used for probabilistic investigations of the charging of electric vehicles, the sizing of thermal storage combined with heat pumps or the integration of battery storage systems. A main advantage is the possibility to estimate the likelihood of operating conditions. The enhancement to further appliances and the changeability of the input data allows for versatile further possible investigations.
APA, Harvard, Vancouver, ISO, and other styles

Books on the topic "Appliance classification"

1

Harni, Pekka. Object categories: Typology of tools. Helsinki]: Aalto University, School of Art and Design, 2010.

Find full text
APA, Harvard, Vancouver, ISO, and other styles

Book chapters on the topic "Appliance classification"

1

Völker, Benjamin, Philipp M. Scholl, and Bernd Becker. "A Feature and Classifier Study for Appliance Event Classification." In Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, 99–116. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-97027-7_7.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

Tanoni, Giulia, Emanuele Principi, Luigi Mandolini, and Stefano Squartini. "Weakly Supervised Transfer Learning for Multi-label Appliance Classification." In Applied Intelligence and Informatics, 360–75. Cham: Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-24801-6_26.

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

Liu, Qi, Hao Wu, Xiaodong Liu, and Nigel Linge. "Single Appliance Recognition Using Statistical Features Based k-NN Classification." In Cloud Computing and Security, 631–40. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-68542-7_54.

Full text
APA, Harvard, Vancouver, ISO, and other styles
4

Ye, Junnan, Jianxin Cheng, Chaoxiang Yang, Zhang Zhang, Xinyu Yang, and Lingyun Yao. "Research on the Construction of the Hierarchical Classification Model of the Urban Intelligent Lighting Appliance (UILA) Based on User Needs." In Intelligent Human Systems Integration, 315–20. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-73888-8_49.

Full text
APA, Harvard, Vancouver, ISO, and other styles
5

Succetti, Federico, Antonello Rosato, and Massimo Panella. "Nonexclusive Classification of Household Appliances by Fuzzy Deep Neural Networks." In Applied Intelligence and Informatics, 404–18. Cham: Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-24801-6_29.

Full text
APA, Harvard, Vancouver, ISO, and other styles
6

Panda, Bighnaraj, Madhusmita Mohanty, and Bidyadhar Rout. "Classification of Electrical Home Appliances Based on Harmonic Analysis Using ANN." In Advances in Intelligent Systems and Computing, 273–80. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-0224-4_25.

Full text
APA, Harvard, Vancouver, ISO, and other styles
7

Firat, Asuman, and Gulgun Kayakutlu. "AI Classification in Collaboration for Innovation of Electric Motors of Household Appliances." In IFIP Advances in Information and Communication Technology, 107–23. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-52903-1_9.

Full text
APA, Harvard, Vancouver, ISO, and other styles
8

Izzuddin, Tarmizi Ahmad, Norlaili Mat Safri, Ong Sze Munn, Zamani Md Sani, and Mohamad Na’im Mohd Nasir. "Classification of Domestic Electrical Appliances Based on Starting Transient Using Artificial Intelligence Methods." In Lecture Notes in Electrical Engineering, 455–66. Singapore: Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-8690-0_41.

Full text
APA, Harvard, Vancouver, ISO, and other styles
9

Murata, Hiroshi, and Takashi Onoda. "Applying Kernel Based Subspace Classification to a Non-intrusive Monitoring for Household Electric Appliances." In Artificial Neural Networks — ICANN 2001, 692–98. Berlin, Heidelberg: Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/3-540-44668-0_96.

Full text
APA, Harvard, Vancouver, ISO, and other styles
10

Bharati, Subrato, Mohammad Atikur Rahman, Rajib Mondal, Prajoy Podder, Anas Abdullah Alvi, and Atiq Mahmood. "Prediction of Energy Consumed by Home Appliances with the Visualization of Plot Analysis Applying Different Classification Algorithm." In Frontiers in Intelligent Computing: Theory and Applications, 246–57. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-9920-6_25.

Full text
APA, Harvard, Vancouver, ISO, and other styles

Conference papers on the topic "Appliance classification"

1

Su, Man, Jianting Ji, Yulin Che, Ting Liu, Siyun Chen, and Zhanbo Xu. "An appliance classification method for residential appliance scheduling." In the 2015 ACM International Joint Conference. New York, New York, USA: ACM Press, 2015. http://dx.doi.org/10.1145/2800835.2801641.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

Zufferey, Damien, Christophe Gisler, Omar Abou Khaled, and Jean Hennebert. "Machine learning approaches for electric appliance classification." In 2012 11th International Conference on Information Sciences, Signal Processing and their Applications (ISSPA). IEEE, 2012. http://dx.doi.org/10.1109/isspa.2012.6310651.

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

Tembey, P., A. Bhatt, D. Rao, A. Gavrilovska, and K. Schwan. "Flexible Classification on Heterogenous Multicore Appliance Platforms." In 17th International Conference on Computer Communications and Networks 2008. IEEE, 2008. http://dx.doi.org/10.1109/icccn.2008.ecp.27.

Full text
APA, Harvard, Vancouver, ISO, and other styles
4

Davies, Peter, Jon Dennis, Jack Hansom, William Martin, Aistis Stankevicius, and Lionel Ward. "Deep Neural Networks for Appliance Transient Classification." In ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2019. http://dx.doi.org/10.1109/icassp.2019.8682658.

Full text
APA, Harvard, Vancouver, ISO, and other styles
5

Po-An Chou, Chi-Cheng Chuang, and Ray-I Chang. "Automatic appliance classification for non-intrusive load monitoring." In 2012 IEEE International Conference on Power System Technology (POWERCON 2012). IEEE, 2012. http://dx.doi.org/10.1109/powercon.2012.6401409.

Full text
APA, Harvard, Vancouver, ISO, and other styles
6

Kahl, Matthias, Thomas Kriechbaumer, Daniel Jorde, Anwar Ul Haq, and Hans-Arno Jacobsen. "Appliance Event Detection - A Multivariate, Supervised Classification Approach." In e-Energy '19: The Tenth ACM International Conference on Future Energy Systems. New York, NY, USA: ACM, 2019. http://dx.doi.org/10.1145/3307772.3330155.

Full text
APA, Harvard, Vancouver, ISO, and other styles
7

Bhattacharjee, Sourodeep, Anirudh Kumar, and Joydeb RoyChowdhury. "Appliance classification using energy disaggregation in smart homes." In 2014 International Conference On Computation of Power , Energy, Information and Communication (ICCPEIC). IEEE, 2014. http://dx.doi.org/10.1109/iccpeic.2014.6915330.

Full text
APA, Harvard, Vancouver, ISO, and other styles
8

Kahl, Matthias, Thomas Kriechbaumer, Anwar Ul Haq, and Hans-Arno Jacobsen. "Appliance classification across multiple high frequency energy datasets." In 2017 IEEE International Conference on Smart Grid Communications (SmartGridComm). IEEE, 2017. http://dx.doi.org/10.1109/smartgridcomm.2017.8340664.

Full text
APA, Harvard, Vancouver, ISO, and other styles
9

Salihagic, Emir, Jasmin Kevric, and Nejdet Dogru. "Classification of ON-OFF states of appliance consumption signatures." In 2016 XI International Symposium on Telecommunications – BIHTEL. IEEE, 2016. http://dx.doi.org/10.1109/bihtel.2016.7775722.

Full text
APA, Harvard, Vancouver, ISO, and other styles
10

Berg'es, Mario, and Anthony Rowe. "Appliance classification and energy management using multi-modal sensing." In the Third ACM Workshop. New York, New York, USA: ACM Press, 2011. http://dx.doi.org/10.1145/2434020.2434037.

Full text
APA, Harvard, Vancouver, ISO, and other styles

Reports on the topic "Appliance classification"

1

Osadcha, Kateryna, Viacheslav Osadchyi, Serhiy Semerikov, Hanna Chemerys, and Alona Chorna. The Review of the Adaptive Learning Systems for the Formation of Individual Educational Trajectory. [б. в.], November 2020. http://dx.doi.org/10.31812/123456789/4130.

Full text
Abstract:
The article is devoted to the review of the adaptive learning systems. We considered the modern state and relevance of usage of the adaptive learning systems to be a useful tool of the formation of individual educational trajectory for achieving the highest level of intellectual development according to the natural abilities and inclination with the help of formation of individual trajectory of education, the usage of adaptive tests for monitoring of the quality of acquired knowledge, the formation of complicated model of the knowledge assessment, building of the complicated model of the subject of education, in particular considering the social-emotional characteristics. The existing classification of the adaptive learning systems was researched. We provide the comparative analysis of relevant adaptive learning systems according to the sphere of usage, the type of adaptive learning, the functional purpose, the integration with the existing Learning Management Systems, the appliance of modern technologies of generation and discernment of natural language and courseware features, ratings are based on CWiC Framework for Digital Learning. We conducted the research of the geography of usage of the systems by the institutions of higher education. We describe the perspectives of effective usage of adaptive systems of learning for the implementation and support of new strategies of learning and teaching and improvement of results of studies.
APA, Harvard, Vancouver, ISO, and other styles
You might also be interested in the extended bibliographies on the topic 'Appliance classification' for particular source types:
Journal articles
We offer discounts on all premium plans for authors whose works are included in thematic literature selections. Contact us to get a unique promo code!

To the bibliography