NILM techniques for intelligent home energy management and ambient assisted living: a review

The ongoing deployment of smart meters and different commercial devices has made electricity disaggregation feasible in buildings and households, based on a single measure of the current and, sometimes, of the voltage. Energy disaggregation is intended to separate the total power consumption into specific appliance loads, which can be achieved by applying Non-Intrusive Load Monitoring (NILM) techniques with a minimum invasion of privacy. NILM techniques are becoming more and more widespread in recent years, as a consequence of the interest companies and consumers have in efficient energy consumption and management. This work presents a detailed review of NILM methods, focusing particularly on recent proposals and their applications, particularly in the areas of Home Energy Management Systems (HEMS) and Ambient Assisted Living (AAL), where the ability to determine the on/off status of certain devices can provide key information for making further decisions. As well as complementing previous reviews on the NILM field and providing a discussion of the applications of NILM in HEMS and AAL, this paper provides guidelines for future research in these topics.Agência financiadora: Programa Operacional Portugal 2020 and Programa Operacional Regional do Algarve 01/SAICT/2018/39578 Fundação para a Ciência e Tecnologia through IDMEC, under LAETA: SFRH/BSAB/142998/2018 SFRH/BSAB/142997/2018 UID/EMS/50022/2019 Junta de Comunidades de Castilla-La-Mancha, Spain: SBPLY/17/180501/000392 Spanish Ministry of Economy, Industry and Competitiveness (SOC-PLC project): TEC2015-64835-C3-2-R MINECO/FEDERinfo:eu-repo/semantics/publishedVersio

Edge AI for Internet of Energy: Challenges and Perspectives

The digital landscape of the Internet of Energy (IoE) is on the brink of a revolutionary transformation with the integration of edge Artificial Intelligence (AI). This comprehensive review elucidates the promise and potential that edge AI holds for reshaping the IoE ecosystem. Commencing with a meticulously curated research methodology, the article delves into the myriad of edge AI techniques specifically tailored for IoE. The myriad benefits, spanning from reduced latency and real-time analytics to the pivotal aspects of information security, scalability, and cost-efficiency, underscore the indispensability of edge AI in modern IoE frameworks. As the narrative progresses, readers are acquainted with pragmatic applications and techniques, highlighting on-device computation, secure private inference methods, and the avant-garde paradigms of AI training on the edge. A critical analysis follows, offering a deep dive into the present challenges including security concerns, computational hurdles, and standardization issues. However, as the horizon of technology ever expands, the review culminates in a forward-looking perspective, envisaging the future symbiosis of 5G networks, federated edge AI, deep reinforcement learning, and more, painting a vibrant panorama of what the future beholds. For anyone vested in the domains of IoE and AI, this review offers both a foundation and a visionary lens, bridging the present realities with future possibilities

Design and Analysis of Smart Building Envelope Materials and Systems

As the largest consumer of electricity, the buildings sector accounts for about 76% of electricity use and 40% of all U.S. primary energy use and associated greenhouse gas (GHG) emissions. Research shows that a potential energy saving of 34.78% could be achieved by the smart buildings comparing to conventional buildings. Therefore, a smart management of building sectors becomes significantly important to achieve the optimal interior comfort with minimal energy expenditure. The ability of adaptation to the dynamic environments is considered the central aspect in smart building systems, which can be segmented into the passive adaptation and the active adaptation. The passive adaptation refers to the designs that do not change with the dynamic environment but improve the building overall performance by the integration of originally separated components, or by the application of advanced engineering materials. The active adaptation refers to the building management system (BMS) that actively responds and evolves with the changing environment, through the continuous monitoring of the surroundings via the sensor network, and the smart response through the controlling algorithms in the central controlling unit. This Ph.D. dissertation focuses on developing materials and systems for the smart building envelope, including a photovoltaic integrated roof with passive adaptation, and self-powered window systems with active responses environment. As the skin of a building, the building envelope provides the first level resistance towards air, water, heat, light and noise, which makes it the ideal target for the passive adaptation to the environments, as well as the perfect sensing location in the building management system for the active adaptation. This dissertation starts with a discussion of the building integrated photovoltaic thermal (BIPVT) roofing panel, including the fabrication, performance demonstration, and micromechanics-based theoretical modeling. The panel is structurally supported by a functionally graded material (FGM) panel made with high-density polyethylene as the matrix and aluminum particles as reinforcement. It prevents the heat from entering the building and directs the heat to the water tubes embedded inside the panel for the thermal energy harvesting, such that the overall energy efficiency is significantly improved. The design, fabrication and performance of the system is discussed, and an innovative non-destructive analysis method is developed to captures the authentic particle distribution of the FGM. As the main structural component, functionally graded material is comprehensively tested and modeled in elastic, thermoelastic, elastoplastic, and thermo-elastoplastic performance, based on the equivalent inclusion based method. An ensemble average approach was used to convert the particles’ interaction in the microscope to the averaged relation in the macroscope, such that both particle to matrix influence and particle to particle pair-wise interactions are characterized. The idea of the equivalent inclusion method extends to the plastic modeling of the FGM, by formulating an ensemble average form of the matrix stress norm in the macroscale that incorporate the local disturbance of particle reinforcement in the microscale. The accuracy of the proposed algorithm is verified and validated by comparing with another theory in homogeneous composite and experiments, respectively. To the best of the author’s knowledge, no prior theoretical algorithm has been proposed for the elastoplastic modeling of functionally graded materials. Therefore, the proposed algorithm can be used as a foundation and reference for further investigation and industry prediction of graded composites. Based on the theoretical modeling of the mechanical properties, a high order plate theory is also proposed in this dissertation to study for the thermo-mechanical performance of the FGM panel, to provide structural design guideline for the BIPVT panels. The shearing and bending behaviors are decomposed, solved independently, and combined to formulate the final solution. The shear strain components are assumed to follow a parabolic variation across the thickness, while the bending components follow the solution from classical plate theory. Closed-form solutions for the circular panel under different loadings are provided, with verification by comparing to other models and validation to experiments. Two smart window systems are proposed and demonstrated in this dissertation to actively monitor the building environment with active responses, and energy harvesting techniques are investigated to harvest energy from ambient environment the eternal power supply to the system. The thermoelectric powered wireless sensor network (TPWSN) platform is first demonstrated and discussed, where the energy is harvested from the temperature difference across the window frame. The TPWSN sits completely inside the window/façade frame with no compromise of the outlook and continuously monitors the building environment for the optimal control of the building energy consumption and indoor comfort. The energy harvesting technique grants eternal battery lifetime and significantly simplifies the installation and maintenance of the system with considerable saving of time and cost. In addition, the platform provides energy to various types of sensors for different kinds of sensing needs and store the data to the Google cloud for permanent storage and advanced analytics. The thermoelectric powered system works well for the sensors and microcontrollers but fails to provide enough power to the actuators. A novel sun-powered smart window blinds (SPSWB) system is designed, prototyped, and tested in this dissertation with solar energy harvesting on window blinds which provides enough power for the actuators. The thin-film photovoltaic cells are attached on one side of slats for energy harvesting and a PVdF-HFP coating is attached on the other side for the passive cooling. The voltage regulation and battery management systems are designed and tested, where a stable 55% energy efficiency from the PV into the battery has been achieved. The automatic control of the window blinds is accomplished with the help of sensors and a microcontroller. The energy equilibrium analysis is proposed and demonstrated with the local solar data to incorporate the influence of local weather conditions and solar zenith angle, from which we demonstrated that much more power than needed can be harvested. The abundant energy harvested validates the feasibility and the robustness of the system and proves its wide application potentials to various sensors and applications. In conclusion, both passive and active adaptations to the environment are investigated to build up the next generation of smart building envelope systems. The building integrated photovoltaic thermal roof is designed, fabricated, tested, and modeled in detail, which provides structural support to the external loads and improves the energy efficiency of buildings. The smart window/façade systems serve as a platform for various sensors and actuators via the energy harvesting from the ambient environment, and could significantly improve the energy expenditure with minimal impact of internal comfort

Available Technologies and Commercial Devices to Harvest Energy by Human Trampling in Smart Flooring Systems: a Review

Technological innovation has increased the global demand for electrical power and energy. Accordingly, energy harvesting has become a research area of primary interest for the scientific community and companies because it constitutes a sustainable way to collect energy from various sources. In particular, kinetic energy generated from human walking or vehicle movements on smart energy floors represents a promising research topic. This paper aims to analyze the state-of-art of smart energy harvesting floors to determine the best solution to feed a lighting system and charging columns. In particular, the fundamentals of the main harvesting mechanisms applicable in this field (i.e., piezoelectric, electromagnetic, triboelectric, and relative hybrids) are discussed. Moreover, an overview of scientific works related to energy harvesting floors is presented, focusing on the architectures of the developed tiles, the transduction mechanism, and the output performances. Finally, a survey of the commercial energy harvesting floors proposed by companies and startups is reported. From the carried-out analysis, we concluded that the piezoelectric transduction mechanism represents the optimal solution for designing smart energy floors, given their compactness, high efficiency, and absence of moving parts

Carbon Nano Tubes (CNTS) for the development of high-performance and smart composites.

Los nanotubos de carbono han atraído una enorme atención en los últimos años debido a sus propiedades multifuncionales sobresalientes. Un número cada vez mayor de trabajos de investigación de primera línea centran su interés en la búsqueda de aplicaciones prácticas que den uso de las notables propiedades de los nanotubos de carbono, incluyendo una elevada resistencia mecánica, propiedades piezorestivas, alta conductividad eléctrica, ligereza, excelente estabilidad química y térmica. En concreto, los estudios más recientes plantean dos grandes ramas de aplicación: fabricación de estructuras aligeradas de alta resistencia, y desarrollo de estructuras inteligentes. Con respecto a la primera línea de aplicación, el desarrollo de materiales compuestos ligeros de alta resistencia conecta con la creciente tendencia de la ingeniería estructural a incorporar materiales compuestos innovadores. Ejemplos recientes como el avión comercial Boeing 787, en el que la mitad del peso fue diseñado con materiales compuestos, predicen un futuro auspicioso para los nanotubos de carbono en la ingeniería aeronáutica. Sin embargo, aún resulta más interesante el comportamiento piezorresistivo de los compuestos reforzados con nanotubos de carbono, ya que posibilita la creación de estructuras que no sólo presentan altas capacidades portantes y reducido peso específico, sino que también ofrecen capacidades de auto-detección de deformaciones. Cuando el material se ve sometido a una deformación externa, en virtud de dicha propiedad piezoresistiva, la conductividad eléctrica varía de modo que es posible correlacionar su respuesta eléctrica con el campo deformacional aplicado. Estas propiedades multifuncionales entroncan con el nuevo paradigma de la Vigilancia de la Salud Estructural el cual aboga por el uso de materiales/estructuras inteligentes para resolver el problema de escalabilidad. En este contexto, la estructura o parte de ella presenta capacidades de auto-detección de tal manera que el mantenimiento basado en la condición puede llevarse a cabo sin necesidad de incluir sensores externos. En ambas líneas, la mayoría de las investigaciones han centrado el estudio en la experimentación, siendo mucho menor el número de trabajos que plantean modelos teóricos capaces de simular las propiedades mecánicas, eléctricas y electromecánicas de estos compuestos. Desde un punto de vista mecánico, existen estudios experimentales que informan acerca de los efectos perjudiciales sobre la respuesta macroscópica de aspectos micromecánicos tales como la tendencia a formar aglomerados, así como la curvatura de los nanotubos de carbono. Es por ello esencial desarrollar modelos teóricos que incorporen estos efectos y asistan al diseño de elementos estructurales reforzados con nanotubos de carbono. Respecto al estudio de las propiedades de conductividad y piezoresistividad, es esencial desarrollar formulaciones teóricas capaces de abordar la optimización de las propiedades de autodetección de deformaciones. Asimismo, es crucial comprender los diferentes mecanismos físicos que rigen la conductividad eléctrica de estos compuestos, de modo que sea posible incorporar su efecto diferencial dentro de un marco teórico. Por último, también es fundamental avanzar hacia el dominio del tiempo con el fin de desarrollar aplicaciones de vigilancia de la salud estructural basada en vibraciones. Con todo ello, los esfuerzos de esta tesis se han centrado en el modelado de las propiedades mecánicas, conductivas y electromecánicas de los compuestos reforzados con nanotubos de carbono para el desarrollo de estructuras inteligentes y de alta resistencia. Estas dos aplicaciones, a saber, compuestos de alta resistencia e inteligentes, han sido enmarcadas en el ámbito de los materiales poliméricos y de cemento, respectivamente. La razón de esta distinción se debe a la presunción de que los compuestos poliméricos pueden encontrar aplicaciones directas como paneles de fuselaje para estructuras de aeronaves, así como refuerzos mecánicos sobre estructuras pre-existentes. En cuanto al uso de nanotubos de carbono como inclusiones multifuncionales para compuestos inteligentes, tanto los materiales poliméricos como los de base cemento ofrecen una amplia gama de aplicaciones potenciales. Sin embargo, la similitud entre los compuestos de base cemento y el hormigón estructural convencional sugiere la idea de desarrollar sensores embebidos que ofrezcan una monitorización continua integrada sin comprometer a priori la durabilidad de la estructura huésped. Tanto las propiedades mecánicas como las conductivas han sido estudiadas mediante métodos de homogeneización de campo medio. Aspectos micromecánicos tales como la relación de aspecto, el contenido, la distribución de la orientación, la ondulación o la aglomeración de los nanotubos se han estudiado en detalle e incorporado al análisis de diferentes elementos estructurales. De manera similar, se han estudiado las propiedades de conductividad eléctrica y auto-detección de deformaciones bajo cargas cuasi-estáticas mediante modelos mixtos de homogenización micromecánica de Mori-Tanaka. Los principales mecanismos que gobiernan las propiedades de transporte eléctrico de estos compuestos, a saber, los efectos de túnel cuántico y la formación de canales conductores, se han incorporado por separado en las simulaciones a través de la teoría de percolación de fibras conductoras. Los resultados teóricos han sido validados con éxito mediante experimentos en condiciones de laboratorio. Finalmente, se ha desarrollado un nuevo circuito equivalente piezorresistivo/piezoeléctrico para el modelado electromecánico de materiales de base cemento reforzado con nanotubos de carbono en el dominio del tiempo. Con los experimentos como base de validación, se ha demostrado que el enfoque propuesto proporciona resultados precisos y ofrece un marco teórico apto para aplicaciones de procesamiento de señales y monitorización de la salud estructural. Se espera que el trabajo desarrollado en esta tesis pueda proporcionar herramientas valiosas que permitan profundizar en la comprensión de los principales aspectos físicos que controlan las propiedades mecánicas, eléctricas y electromecánicas de los compuestos reforzados con nanotubos de carbono. Además, se espera que los resultados presentados en esta tesis impulsen el desarrollo de materiales compuestos auto-sensibles embebidos para aplicaciones de vigilancia de la salud estructural.Carbon nanotubes have drawn enormous attention in recent years due to their outstanding multifunctional properties. A constantly growing number of works at the front line of research pursue potential applications of their remarkable physical properties, including elevated load-bearing capacity, piezoresistive properties, high electrical conductivity, lightness, and excellent chemical and thermal stability. In particular, most recent works contemplate two different application branches: manufacture of light-weight high-strength structures, and development of smart structures. With regard to the first line of application, the development of high-strength lightweight composites connects with the growing tendency of structural engineering to incorporate advanced composite materials. Recent noticeable examples such as the commercial aircraft Boeing 787, in which half of the total weight was designed with composite materials, predict an auspicious future for carbon nanotubes in aircraft structures. Nonetheless, what is even more interesting is the piezoresistive behavior of carbon nanotube-reinforced composites, which allows us to create structures that are not only high-strength and lightweight but also strain-sensitive. When the composites are subjected to external strain fields, in virtue of such piezoresistive properties, the overall electrical conductivity varies in such a way that it is possible to correlate the electrical response with the deformational state of the material. These multifunctional properties are in line with the new paradigm of Structural Health Monitoring which advocates the use of smart materials/structures to solve the scalability issue. In this context, the structure or part of it presents self-sensing capabilities in such a way that the condition-based maintenance can be conducted without necessitating external off-the-shelf sensors. In both lines, most investigations have focused on experimentation. Conversely, the number of theoretical models capable of simulating the mechanical, electrical, and electromechanical properties of these composites is still scarce. From a mechanical point of view, experiments have reported about the detrimental effects of micromechanical aspects such as agglomeration of fillers and curviness on the macroscopic properties. Hence, it is essential to develop theoretical models that allow us to include these effects and assist the design of composite structural elements. With regard to the study of the conductivity and piezoresistivity of carbon nanotube-reinforced composites, it is essential to develop theoretical formulations capable of tackling the optimization of their strain sensitivity. In addition, it is crucial to understand the different physical mechanisms that govern the electrical conductivity of these composites and include them separately in the theoretical framework. Finally, it is also fundamental to move towards the time domain in order to develop applications for vibration-based structural health monitoring. Overall, all the efforts of this thesis have been put into the modeling of the mechanical, conductive and electromechanical properties of carbon nanotube-reinforced composites for the development of high-strength and smart structures. These two applications, namely high-strength and smart composites, have been framed in the realm of polymeric and cement-based materials, respectively. The reason for this distinction is the idea that polymer composites with high load-bearing capacity can find direct applications as fuselage panels for aircraft structures, as well as mechanical reinforcements attached to pre-existing structures. With regard to the use of carbon nanotubes as fillers for smart composites, both polymer and cement-based materials offer an enormous range of potential applications. Nonetheless, the similarity between cement-based composites and regular structural concrete suggests the idea of developing continuous embedded monitoring systems without compromising the durability of the hosting structure a priori. Both mechanical and conductive properties have been studied by means of mean-field homogenization methods. Micromechanical aspects such as filler aspect ratio, content, orientation distribution, waviness or agglomeration have been studied in detail and incorporated to the analysis of different structural elements. Similarly, the electrical conductivity and strain-sensing properties of these composites under quasi-static loadings have been studied by means of mixed Mori-Tanaka micromechanics models. The main mechanisms that underlie the electrical conduction of these composites, namely quantum tunneling effects and conductive networks, have been distinguished by a percolative-type behavior. The theoretical results have been successfully validated by means of experiments under laboratory conditions. Finally, a novel piezoresistive/piezoelectric equivalent lumped circuit has been developed for the electromechanical modeling of carbon nanotube-reinforced cement-based materials in the time domain. With experiments as validating basis, the proposed approach has been shown to provide accurate results and offers a theoretical framework readily applicable to signal processing applications and structural health monitoring. The work developed in this thesis is envisaged to provide valuable tools to further the understanding of the main physical aspects that control the mechanical, electrical and electromechanical properties of composites doped with carbon nanotubes. Furthermore, it is expected to boost the development of embedded self-sensing carbon nanotube-reinforced composites for structural health monitoring applications.Premio Extraordinario de Doctorado U

Machine Learning for Microcontroller-Class Hardware -- A Review

The advancements in machine learning opened a new opportunity to bring intelligence to the low-end Internet-of-Things nodes such as microcontrollers. Conventional machine learning deployment has high memory and compute footprint hindering their direct deployment on ultra resource-constrained microcontrollers. This paper highlights the unique requirements of enabling onboard machine learning for microcontroller class devices. Researchers use a specialized model development workflow for resource-limited applications to ensure the compute and latency budget is within the device limits while still maintaining the desired performance. We characterize a closed-loop widely applicable workflow of machine learning model development for microcontroller class devices and show that several classes of applications adopt a specific instance of it. We present both qualitative and numerical insights into different stages of model development by showcasing several use cases. Finally, we identify the open research challenges and unsolved questions demanding careful considerations moving forward.Comment: Accepted for publication at IEEE Sensors Journa