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

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

Cloud Energy Micro-Moment Data Classification: A Platform Study

Energy efficiency is a crucial factor in the well-being of our planet. In parallel, Machine Learning (ML) plays an instrumental role in automating our lives and creating convenient workflows for enhancing behavior. So, analyzing energy behavior can help understand weak points and lay the path towards better interventions. Moving towards higher performance, cloud platforms can assist researchers in conducting classification trials that need high computational power. Under the larger umbrella of the Consumer Engagement Towards Energy Saving Behavior by means of Exploiting Micro Moments and Mobile Recommendation Systems (EM)3 framework, we aim to influence consumers behavioral change via improving their power consumption consciousness. In this paper, common cloud artificial intelligence platforms are benchmarked and compared for micro-moment classification. The Amazon Web Services, Google Cloud Platform, Google Colab, and Microsoft Azure Machine Learning are employed on simulated and real energy consumption datasets. The KNN, DNN, and SVM classifiers have been employed. Superb performance has been observed in the selected cloud platforms, showing relatively close performance. Yet, the nature of some algorithms limits the training performance.Comment: This paper has been accepted in IEEE RTDPCC 2020: International Symposium on Real-time Data Processing for Cloud Computin

Machine learning for smart building applications: Review and taxonomy

© 2019 Association for Computing Machinery. The use of machine learning (ML) in smart building applications is reviewed in this article. We split existing solutions into two main classes: occupant-centric versus energy/devices-centric. The first class groups solutions that use ML for aspects related to the occupants, including (1) occupancy estimation and identification, (2) activity recognition, and (3) estimating preferences and behavior. The second class groups solutions that use ML to estimate aspects related either to energy or devices. They are divided into three categories: (1) energy profiling and demand estimation, (2) appliances profiling and fault detection, and (3) inference on sensors. Solutions in each category are presented, discussed, and compared; open perspectives and research trends are discussed as well. Compared to related state-of-the-art survey papers, the contribution herein is to provide a comprehensive and holistic review from the ML perspectives rather than architectural and technical aspects of existing building management systems. This is by considering all types of ML tools, buildings, and several categories of applications, and by structuring the taxonomy accordingly. The article ends with a summary discussion of the presented works, with focus on lessons learned, challenges, open and future directions of research in this field

Fall Detection Using Neural Networks

Falls inside of the home is a major concern facing the aging population. Monitoring the home environment to detect a fall can prevent profound consequences due to delayed emergency response. One option to monitor a home environment is to use a camera-based fall detection system. Conceptual designs vary from 3D positional monitoring (multi-camera monitoring) to body position and limb speed classification. Research shows varying degree of success with such concepts when designed with multi-camera setup. However, camera-based systems are inherently intrusive and costly to implement. In this research, we use a sound-based system to detect fall events. Acoustic sensors are used to monitor various sound events and feed a trained machine learning model that makes predictions of a fall events. Audio samples from the sensors are converted to frequency domain images using Mel-Frequency Cepstral Coefficients method. These images are used by a trained convolution neural network to predict a fall. A publicly available dataset of household sounds is used to train the model. Varying the model\u27s complexity, we found an optimal architecture that achieves high performance while being computationally less extensive compared to the other models with similar performance. We deployed this model in a NVIDIA Jetson Nano Developer Kit

Energy Data Analytics for Smart Meter Data

The principal advantage of smart electricity meters is their ability to transfer digitized electricity consumption data to remote processing systems. The data collected by these devices make the realization of many novel use cases possible, providing benefits to electricity providers and customers alike. This book includes 14 research articles that explore and exploit the information content of smart meter data, and provides insights into the realization of new digital solutions and services that support the transition towards a sustainable energy system. This volume has been edited by Andreas Reinhardt, head of the Energy Informatics research group at Technische Universität Clausthal, Germany, and Lucas Pereira, research fellow at Técnico Lisboa, Portugal

Employing multi-modal sensors for personalised smart home health monitoring.

Smart home systems are employed worldwide for a variety of automated monitoring tasks. FITsense is a system that performs personalised smart home health monitoring using sensor data. In this thesis, we expand upon this system by identifying the limits of health monitoring using simple IoT sensors, and establishing deployable solutions for new rich sensing technologies. The FITsense system collects data from FitHomes and generates behavioural insights for health monitoring. To allow the system to expand to arbitrary home layouts, sensing applications must be delivered while relying on sparse "ground truth" data. An enhanced data representation was tested for improving activity recognition performance by encoding observed temporal dependencies. Experiments showed an improvement in activity recognition accuracy over baseline data representations with standard classifiers. Channel State Information (CSI) was chosen as our rich sensing technology for its ambient nature and potential deployability. We developed a novel Python toolkit, called CSIKit, to handle various CSI software implementations, including automatic detection for off-the-shelf CSI formats. Previous researchers proposed a method to address AGC effects on COTS CSI hardware, which we tested and found to improve correlation with a baseline without AGC. This implementation was included in the public release of CSIKit. Two sensing applications were delivered using CSIKit to demonstrate its functionality. Our statistical approach to motion detection with CSI data showed a 32% increase in accuracy over an infrared sensor-based solution using data from 2 unique environments. We also demonstrated the first CSI activity recognition application on a Raspberry Pi 4, which achieved an accuracy of 92% with 11 activity classes. An application was then trained to support movement detection using data from all COTS CSI hardware. This was combined with our signal divider implementation to compare CSI wireless and sensing performance characteristics. The IWL5300 exhibited the most consistent wireless performance, while the ESP32 was found to produce viable CSI data for sensing applications. This establishes the ESP32 as a low-cost high-value hardware solution for CSI sensing. To complete this work, an in-home study was performed using real-world sensor data. An ESP32-based CSI sensor was developed to be integrated into our IoT network. This sensor was tested in a FitHome environment to identify how the data from our existing simple sensors could aid sensor development. We performed an experiment to demonstrate that annotations for CSI data could be gathered with infrared motion sensors. Results showed that our new CSI sensor collected real-world data of similar utility to that collected manually in a controlled environment

Management And Security Of Multi-Cloud Applications

Single cloud management platform technology has reached maturity and is quite successful in information technology applications. Enterprises and application service providers are increasingly adopting a multi-cloud strategy to reduce the risk of cloud service provider lock-in and cloud blackouts and, at the same time, get the benefits like competitive pricing, the flexibility of resource provisioning and better points of presence. Another class of applications that are getting cloud service providers increasingly interested in is the carriers\u27 virtualized network services. However, virtualized carrier services require high levels of availability and performance and impose stringent requirements on cloud services. They necessitate the use of multi-cloud management and innovative techniques for placement and performance management. We consider two classes of distributed applications – the virtual network services and the next generation of healthcare – that would benefit immensely from deployment over multiple clouds. This thesis deals with the design and development of new processes and algorithms to enable these classes of applications. We have evolved a method for optimization of multi-cloud platforms that will pave the way for obtaining optimized placement for both classes of services. The approach that we have followed for placement itself is predictive cost optimized latency controlled virtual resource placement for both types of applications. To improve the availability of virtual network services, we have made innovative use of the machine and deep learning for developing a framework for fault detection and localization. Finally, to secure patient data flowing through the wide expanse of sensors, cloud hierarchy, virtualized network, and visualization domain, we have evolved hierarchical autoencoder models for data in motion between the IoT domain and the multi-cloud domain and within the multi-cloud hierarchy

Plateforme informatique pour l'assistance à l'autonomie à domicile de personnes âgées

RÉSUMÉ : Ambient Assisted Living (AAL) en général et Activity Recognition (AR) en particulier sont des domaines de recherche actifs qui visent à aider les personnes dans leurs activités de la vie quotidienne (AVQ). Au cours des dernières années, nous avons constaté un intérêt accru pour leur applicabilité aux personnes âgées vivant en milieu rural qui perdent lentement leur autonomie en raison du vieillissement et aux maladies chroniques. Une avenue de recherche importante consiste à agréger et à rechercher des corrélations entre les données physiologiques qui servent à surveiller la santé des personnes âgées, leurs AVQ, leurs mouvements et toute autre donnée pouvant être recueillis sur leur environnement immédiat. Dans ce travail, nous abordons la possibilité de développer une plateforme non intrusive et abordable en raison de l'absence d'une telle plateforme. Elle est basée sur des capteurs de santé, de mouvement, d'activité et de localisation. En outre, nous discutons des principaux concepts derrière la création d'une architecture en couches, flexible et hautement modulaire qui se concentre sur la façon dont l'intégration de données de capteurs combinés peut être réalisée. À l'aide d'un prototype d'application de téléphonie mobile, nos travaux ont montré que nous pouvons intégrer de nombreuses technologies non invasives qui ne sont pas nécessairement les plus récentes, mais les plus abordables, évolutives et prêtes à être déployées dans des environnements réels. Un autre domaine de recherche découlant de ces avancées est de savoir comment la technologie et l'analyse pourraient bénéficier à la prévention et au traitement des maladies chroniques chez le nombre croissant de personnes âgées ayant des problèmes de santé. De nombreuses architectures sont proposées dans la littérature, mais elles manquent de modularité et de flexibilité pour différents types de capteurs. À cette fin, nous proposons une architecture à quatre couches et hautement modulaire pour l'analyse de la santé des personnes âgées. Finalement, nous évaluons l'approche en implémentant une partie de l'architecture sur des nœuds de brouillard et le cloud. De plus, nous déployons ces capteurs abordables, de qualité, et accessibles au grand public dans un appartement afin d'avancer vers l'utilisation du système proposé. Des données recueillies sont utilisées comme un test préliminaire pour évaluer les capacités de la plate-forme. En utilisant les données collectées lors de l'étape de validation, nous effectuons des prévisions d'une semaine dans le futur pour des séries univariées en utilisant des méthodes classiques populaires et les méthodes d'apprentissage en profondeur les plus récentes. Une comparaison de précision est présentée. -- Mot(s) clé(s) en français : IoT, suivi à distance des personnes âgées, santé intelligente et connectée, analyse, assistance à la vie ambiante, capteurs, intelligence artificielle. -- ABSTRACT : Ambient Assisted Living (AAL) in general and Activity Recognition (AR) in particular are active fields of research that aim at assisting people in their Activities of Daily Living (ADL). In recent years, we have seen an increased interest in their applicability to the rural seniors who are slowly losing their autonomy due to aging and chronic diseases. One research venue is to aggregate and seek for correlations between the physiological data that serves to monitor the health of the elderly, their ADLs, their movements and any other data that may be collected about their immediate environment. In this work, we are tackling the possibility of developing a non-intrusive and affordable platform due to the lack of such a platform. It is based on embedded health, movement, activity and location sensors. Furthermore, we discuss the main concepts behind the creation of a layered, flexible and highly modular architecture that focuses on how the integration of newly combined sensor data can be achieved. Using a mobile phone application prototype, our work has shown that we can integrate many non-invasive technologies that are not necessarily the newest, but the most affordable, scalable and ready to be deployed in real life settings. Another researched venue deriving from these advances is how the technology and analytics could benefit the prevention and treatment of chronic diseases in the escalating number of elderly people experiencing health issues. Many architectures are proposed in the literature, but they lack modularity and flexibility for different types of sensors. To that end, we propose a four layered and highly modular architecture for health analytics of elderly people. In the final analysis, we evaluate the approach by implementing part of the architecture on fog nodes and the cloud. Moreover, we deploy these affordable consumer grade sensors in an apartment in order to move toward the use of the system proposed. The data collected from this experiment is used as a preliminary test of the capabilities of the platform. We perform univariate series forecasting using a popular classical methods and the more recent deep learning methods by using the data collected in the validation stage. An accuracy comparison is presented. -- Mot(s) clé(s) en anglais : IoT, remote elderly monitoring, smart and connected Health, analytics, ambient assisted living, sensors

Data-driven design of intelligent wireless networks: an overview and tutorial

Data science or "data-driven research" is a research approach that uses real-life data to gain insight about the behavior of systems. It enables the analysis of small, simple as well as large and more complex systems in order to assess whether they function according to the intended design and as seen in simulation. Data science approaches have been successfully applied to analyze networked interactions in several research areas such as large-scale social networks, advanced business and healthcare processes. Wireless networks can exhibit unpredictable interactions between algorithms from multiple protocol layers, interactions between multiple devices, and hardware specific influences. These interactions can lead to a difference between real-world functioning and design time functioning. Data science methods can help to detect the actual behavior and possibly help to correct it. Data science is increasingly used in wireless research. To support data-driven research in wireless networks, this paper illustrates the step-by-step methodology that has to be applied to extract knowledge from raw data traces. To this end, the paper (i) clarifies when, why and how to use data science in wireless network research; (ii) provides a generic framework for applying data science in wireless networks; (iii) gives an overview of existing research papers that utilized data science approaches in wireless networks; (iv) illustrates the overall knowledge discovery process through an extensive example in which device types are identified based on their traffic patterns; (v) provides the reader the necessary datasets and scripts to go through the tutorial steps themselves