12 research outputs found

    Designing Artificial Neural Networks (ANNs) for Electrical Appliance Classification in Smart Energy Distribution Systems

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    En este proyecto se abordará el problema de la desagregación del consumo eléctrico a través del diseño de sistemas inteligentes, basados en redes neuronales profundas, que puedan formar parte de sistemas más amplios de gestión y distribución de energía. Durante la definición estará presente la búsqueda de una complejidad computacional adecuada que permita una implementación posterior de bajo costo. En concreto, estos sistemas realizarán el proceso de clasificación a partir de los cambios en la corriente eléctrica provocados por los distintos electrodomésticos. Para la evaluación y comparación de las diferentes propuestas se hará uso de la base de datos BLUED.This project will address the energy consumption disaggregation problem through the design of intelligent systems, based on deep artificial neural networks, which would be part of broader energy management and distribution systems. The search for adequate computational complexity that will allow a subsequent implementation of low cost will be present during algorithm definition. Specifically, these systems will carry out the classification process based on the changes caused by the different appliances in the electric current. For the evaluation and comparison of the different proposals, the BLUED database will be used.Máster Universitario en Ingeniería Industrial (M141

    Fault detection in low voltage networks with smart meters and machine learning techniques

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    25th International Conference on Electricity Distribution (CIRED 2019), junio, Madrid (Spain)Smart grid data analytics and artificial intelligence techniques are playing an increasingly critical role, becoming the focal point to understanding low voltage real-time grid performance. This new point of view, (advanced analytics in combination with electrical knowledge expertise), makes flexibility and efficiency in electrical grid management approach real. HDCE (Hidrocantábrico Distribución Eléctrica) is the Electrical Distribution System Operator for EdP (Electricity of Portugal) around Spain who supplies energy to 650.000 customers. Starting from 2012, this company has nowadays replaced 99% of traditional meters by smart meters. Based on the analysis of smart metering voltage alarms, recorded from EdP LV distribution network, an automatic learning system has been implemented that groups and orders these alarms helping the grid distribution operator to drive the network technicians to the right and more urgent places where a grid failure is happening, starts to happen or will happen

    Hardware and software platforms to deploy and evaluate non-intrusive load monitoring systems

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    The work in this PhD thesis addresses the practical implications of deploying and testing Non-Intrusive Load Monitoring (NILM) and eco-feedback solutions in real-world scenarios. The contributions to this topic are centered around the design and development of NILM frameworks that have been deployed in the wild, supporting long-term research in ecofeedback and also serving the purpose of producing real-world datasets and furthering the state of the art regarding the performance metrics used to evaluate NILM algorithms. This thesis consists of three main parts: i) the development of tools and datasets for NILM and eco-feedback research, ii) the design, implementation and deployment of NILM and eco-feedback technologies in real world scenarios, and iii) an experimental comparison of performance metrics for event detection and event classification algorithms. In the first part we describe the Energy Monitoring and Disaggregation Data Format (EMD-DF) and the SustData and SustDataED public datasets. In second part we discuss the development and deployment of two hardware and software platforms in real households, to support eco-feedback research. We then report on more than five years of experience in deploying and maintaining such platforms. Our findings suggest that the main practical issues can be divided in two categories, technological (e.g., system installation) and social (e.g., maintaining a steady sample throughout the whole study). In the final part of this thesis we analyze experimentally the behavior of a number of performance metrics for event detection and event classification, identifying clusters and relationships between the different measures. Our results evidence some considerable differences in the behavior of the performance metrics when applied to the different problems.O trabalho desenvolvido nesta tese de doutoramento aborda as implicações praticas da instalação e avaliação de soluções de monitorização não intrusiva de cargas elétricas (NILM) e eco-feedback em cenários reais. As contribuições para este tópico estão centradas em torno da concepção e desenvolvimento de plataformas NILM que foram instaladas em ambientes não controlados, suportando a pesquisa de longo termo em eco-feedback e servindo também o propósito de produzir conjuntos de dados científicos, bem como promover o avanço do estado da arte acerca das métricas de desempenho utilizadas para avaliar algoritmos NILM. Esta tese é constituída por três partes principais: i) o desenvolvimento de ferramentas e conjuntos de dados científicos para investigação em NILM e eco-feedback, ii) a concepção, desenho e instalação de tecnologias NILM e eco-feedback em cenários reais, e iii) uma comparação experimental de métricas de desempenho para algoritmos de detecção e de classificação de eventos. Na primeira parte descrevemos o Energy Monitoring and Disaggregation Data Format (EMD-DF) e os conjuntos de dados científicos SustData e SustDataED. Na segunda parte discutimos o desenvolvimento e instalação de duas plataformas de hardware e software em residências atuais com a finalidade de suportar a investigação em eco-feedback. Aqui, reportamos sobre mais de cinco anos de experiência na instalação e manutenção destes sistemas. Os nossos resultados sugerem que as principais implicações práticas podem ser divididas em duas categorias, físicas (e.g., instalação do sistema) e sociais (e.g., manter uma amostra constante ao longo de todo o estudo). Na terceira parte analisamos experimentalmente o comportamento de uma série de métricas de desempenho quando estas são utilizadas para avaliar algoritmos de detecção e de classificação de eventos. Calculamos as correlações lineares e não lineares entre os vários pares de métricas, e com base nesses valores procuramos agrupar as métricas que evidenciam um comportamento semelhante. Os nossos resultados sugerem a existência de diferenças evidentes no comportamento das métricas quando aplicadas a ambos dos problemas.Fundação para a Ciência e a Tecnologi

    Classifying Appliances Operation Modes Using Dynamic Time Warping (DTW) And K Nearest Neighbors (KNN)

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    In the Smart Grid environment, the advent of intelligent measuring devices facilitates monitoring appliance electricity consumption. This data can be used in applying Demand Response (DR) in residential houses through data analytics, and developing data mining techniques. In this research, we introduce a smart system approach that is applied to user\u27s disaggregated power consumption data. This system encourages the users to apply DR by changing their behaviour of using heavier operation modes to lighter modes, and by encouraging users to shift their usages to off-peak hours. First, we apply Cross Correlation to detect times of the occurrences when an appliance is being used. We then use two approaches to recognize the operation mode used: The Dynamic Time Warping (DTW), and Machine Learning using K-Means and K-Nearest Neighbors (KNN)

    Non-parametric modeling in non-intrusive load monitoring

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    Non-intrusive Load Monitoring (NILM) is an approach to the increasingly important task of residential energy analytics. Transparency of energy resources and consumption habits presents opportunities and benefits at all ends of the energy supply-chain, including the end-user. At present, there is no feasible infrastructure available to monitor individual appliances at a large scale. The goal of NILM is to provide appliance monitoring using only the available aggregate data, side-stepping the need for expensive and intrusive monitoring equipment. The present work showcases two self-contained, fully unsupervised NILM solutions: the first featuring non-parametric mixture models, and the second featuring non-parametric factorial Hidden Markov Models with explicit duration distributions. The present implementation makes use of traditional and novel constraints during inference, showing marked improvement in disaggregation accuracy with very little effect on computational cost, relative to the motivating work. To constitute a complete unsupervised solution, labels are applied to the inferred components using a Res-Net-based deep learning architecture. Although this preliminary approach to labelling proves less than satisfactory, it is well-founded and several opportunities for improvement are discussed. Both methods, along with the labelling network, make use of block-filtered data: a steady-state representation that removes transient behaviour and signal noise. A novel filter to achieve this steady-state representation that is both fast and reliable is developed and discussed at length. Finally, an approach to monitor the aggregate for novel events during deployment is developed under the framework of Bayesian surprise. The same non-parametric modelling can be leveraged to examine how the predictive and transitional distributions change given new windows of observations. This framework is also shown to have potential elsewhere, such as in regularizing models against over-fitting, which is an important problem in existing supervised NILM

    Sustainability in design: now! Challenges and opportunities for design research, education and practice in the XXI century

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    Copyright @ 2010 Greenleaf PublicationsLeNS project funded by the Asia Link Programme, EuropeAid, European Commission

    Disaggregation von Haushaltsenergiemessdaten mit tiefen neuronalen Netzen

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    Die aktuell besten Ansätze zur Disaggregation von Haushaltsenergiemessdaten, die von handelsüblichen Smart Meter erfasst werden, basieren auf künstlichen neuronalen Netzen, die mit einer Deep-Learning-Methodik erstellt sind. Die Leistungsfähigkeit dieser Ansätze objektiv zu vergleichen ist allerdings schwer, da die Ansätze oft auf unterschiedlichen Datensätzen evaluiert werden, Trainingsverfahren nicht ausführlich beschrieben sind und keine einheitlichen Testmetriken verwendet werden. Erst durch die Evaluation bekannter Ansätze basierend auf einem einheitlichen Aufbau für Disaggregationsexperimente wird in dieser Arbeit deutlich, dass die Praxistauglichkeit aller Ansätze insbesondere durch die geringe Anzahl unterschiedlicher Gerätemodelle im Trainingsdatensatz beschränkt ist. Um für einen festgelegten Trainingsdatensatz den Fehler bei der Gerätelastgangsschätzung dennoch zu verringern, fokussiert sich die vorliegende Arbeit auf das Problem, dass Ansätze oftmals eindeutig falsche und unplausible Gerätelastgänge ausgeben, die von realen Geräten nicht reproduziert werden können. Dazu werden zwei verschiedene neue Ansätze untersucht, die die Plausibilität der geschätzten Lastgänge sicherstellen sollen. Zur Erzeugung von plausiblen Gerätelastgängen werden unterschiedliche Teile eines Generative Adversarial Networks (GAN) verwendet. Ein dritter Ansatz entwirft ein bestehendes Netzmodell neu und kombiniert dieses mit der U-Net-Architektur durch das Hinzufügen von Querverbindungen zwischen Netzschichten. Dies soll helfen, Detailinformationen in den Lastgängen besser zu reproduzieren. Bei der Evaluation der eigenen Ansätze mit dem gleichen Experimentenaufbau werden bei dem zweiten Ansatz häufiger realisierbare Lastgänge ausgegeben. Dabei bleibt die Disaggregationsgenauigkeit auf dem gleichen Niveau. Durch einen weiteren Austausch der beim Modelltraining verwendeten Verlustfunktion wird erreicht, dass sich alle betrachteten Bewertungsmetriken im Mittel über alle Geräte verbessern. Zudem kann bei bestimmten Geräteklassen mit der im dritten Ansatz evaluierten U-Net-Architektur eine weitere Verbesserung der Bewertungsmetriken erzielt werden

    Personality Identification from Social Media Using Deep Learning: A Review

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    Social media helps in sharing of ideas and information among people scattered around the world and thus helps in creating communities, groups, and virtual networks. Identification of personality is significant in many types of applications such as in detecting the mental state or character of a person, predicting job satisfaction, professional and personal relationship success, in recommendation systems. Personality is also an important factor to determine individual variation in thoughts, feelings, and conduct systems. According to the survey of Global social media research in 2018, approximately 3.196 billion social media users are in worldwide. The numbers are estimated to grow rapidly further with the use of mobile smart devices and advancement in technology. Support vector machine (SVM), Naive Bayes (NB), Multilayer perceptron neural network, and convolutional neural network (CNN) are some of the machine learning techniques used for personality identification in the literature review. This paper presents various studies conducted in identifying the personality of social media users with the help of machine learning approaches and the recent studies that targeted to predict the personality of online social media (OSM) users are reviewed

    INNODOCT/17. International conference on innovation,documentation and education

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    INNODOCT/17 que tiene como objetivo proporcionar un foro para académicos y profesionales donde compartir sus investigaciones, discutir ideas, proyectos actuales, resultados y retos La conferencia tiene como objetivo proporcionar un foro para académicos y profesionales que permita compartir sus investigaciones, discutir ideas, proyectos actuales, resultados y retos relacionados con las Nuevas Tecnologías de Información y Comunicación, innovaciones y metodologías aplicadas a la Educación y la Investigación, en áreas como Ciencias, Ingenierías, Ciencias Sociales, Economía, Gestión, Marketing, y también Turismo y HosteleríaGarrigós Simón, FJ.; Estelles Miguel, S.; Lengua Lengua, I.; Onofre Montesa, J.; Dema Pérez, CM.; Oltra Gutiérrez, JV.; Yeamduan Narangajavana... (2018). INNODOCT/17. International conference on innovation,documentation and education. Editorial Universitat Politècnica de València. http://hdl.handle.net/10251/107064EDITORIA
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