Tesis de Postgrado Acceso Abierto
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Browsing Tesis de Postgrado Acceso Abierto by Author "Abdelhamid, Mohamed"
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Thesis Design and Implementation of IoEV Platform for Grid Integration of Electric Vehicles(2024-08) Guerrero Flores, Leonardo Andrés; Silva Jiménez, César Armando; Abdelhamid, MohamedNowadays, the internet of electric vehicles (IoEV) has opened many new opportunities for various applications such as traffic safety, congestion avoidance, charging station selection, and user-comfort services. IoEV communication will support different applications such as vehicle to infrastructure (V2I), vehicle to vehicle (V2V), and vehicle to pedestrian (V2P). Different requirements for data rate, reliability, latency, security, and privacy are needed in order to support reliable IoEV communications. Extensive research has been conducted on the suitability of various short-range and long-range wired/wireless technologies for IoEV communications. These challenges include, for example, transmission range, latency, throughput, and power consumption. Different wired/wireless communication technologies are believed to play an essential role in supporting different IoEV applications. This work aims to investigate the performance of different communication architectures for supporting various IoEV applications such as Grid-to-Vehicle (G2V), Vehicle-to-Grid (V2G), and V2V. The developed architectures are evaluated considering real scenarios for parking vehicles and vehicles on-the-move. Furthermore, the developed communication network is evaluated using off-the-shelf prototypes (sensors and MCU units) under practical scenarios on the UTFSM campus. The performance of the communication network has been evaluated with respect to latency and network reliability.Thesis Energyauction: design and implementation of peer-to-peer energy trading platform in a microgrid(2023-06-01) Condon Silva, Felipe Esteban; Martínez Verdugo, José Manuel; Departamento de Electrónica; Abdelhamid, MohamedEn la actualidad, muchos hogares están adoptando recursos energéticos distribuidos (DER), como sistemas fotovoltaicos solares (PV) y sistemas de almacenamiento de energía con baterías (BESS) que permiten a cada hogar generar y consumir energía. Los avances en el Internet de las cosas (IoT) y la computación en la nube abrieron nuevas oportunidades para el desarrollo de diversas aplicaciones y servicios de redes eléctricas inteligentes. La creciente adopción de dispositivos IoT ha permitido el desarrollo de aplicaciones y soluciones para gestionar el consumo de energía de manera eficiente. El comercio de energía local es una nueva forma de gestión del comercio de energía entre productores y consumidores en el sistema de distribución de energía, lo que permite la venta de energía excedente de forma local. Este trabajo presenta una plataforma de comercio de energía P2P que se compone de un sistema de gestión de energía doméstica (HEMS) IoT-Cloud para recopilar y almacenar datos de consumo de energía y de una arquitectura blockchain que permite el comercio de energía local entre hogares inteligentes en una microred. El enfoque principal es la interpretación entre redes distribuidas P2P, como blockchain y las redes oráculo. La implementación de la arquitectura propuesta IoT-blockchain consiste en una red blockchain Ethereum privada y una red oráculo de Chainlink. Los contratos inteligentes permiten a los productores y consumidores comerciar energía en una subasta abierta mientras solicitan datos de energía externos de una API a través de la red oráculo para el proceso de liquidación. El aporte principal de este trabajo es la integración de estas tecnologías en una implementación real, lo que permite la validación de la posibilidad de ofertar o demandar energía en una microred. Además, se presenta una descripción detallada de la implementación y los resultados obtenidos. Se espera que este trabajo sirva como base para futuras investigaciones en el campo de la gestión de energía distribuida y el comercio de energía local.Thesis Iot based approach for load monitoring and activity recognition in smart homes(Universidad Técnica Federico Santa María, 2021-06) Franco Troya, Patricia; Beghelli, Alejandra; Abdelhamid, MohamedAppliance load monitoring in smart homes has been gaining importance due to its significant advantages in achieving an energy e cient smart grid. The methods to manage such processes can be classified into hardware-based methods, including intrusive load monitoring (ILM) and software-based methods referring to non-intrusive load monitoring (NILM). ILM is based on low-end meter devices attached to home appliances in opposition to NILM techniques, in which only a single point of sensing is needed. Although ILM solutions are relatively expensive, they provide higher e ciency and reliability rather than NILMs do. Moreover, future solutions are expected to be hybrid, combining the benefits of NILM along with individual power measurement by smart plugs and smart appliances. This thesis proposes a novel ILM approach for load monitoring that aims to develop an activity recognition system based on an IoT architecture. The proposed IoT architecture consists of an appliances layer, a perception layer, a communication network layer, a middleware layer, and an application layer. The application layer consists of an appliance recognition module and activities of daily living (ADL) classification algorithm. The main function of the appliance recognition module is to label sensor data and to allow the implementation of di erent home applications. Three di erent classifier models are tested using real data from the UK-DALE dataset: feed-forward neural network (FFNN), long short-term memory (LSTM), and support vector machine (SVM). The developed ADL algorithm maps each ADL to a set of criteria depending on the appliance used. The features are extracted according to the consumption in Watt-hours and the times where appliances are switched on. In the FFNN and the LSTM networks, the accuracy is above 0.9 while being around 0.8 for the SVM network. Other experiments are performed to evaluate the classifier model using a test set. A sensitivity analysis is also carried out to study the impact of the group size on the classifier accuracy. Once results were obtained, the proposed ADL classification system was enhanced in two frameworks: a training framework and an inference framework. This is to allow a practical implementation of the system. In this regard, several modifications were made in the appliance recognition module, including the use of new data, and therefore new appliances: an electric vehicle, an oven and a microwave, from the Dataport dataset. The frameworks include graphical interfaces that significantly facilitate its use. The dataset configuration, pre-processing and classification parameters can be easily selected and modified. In the feature extraction, inside a sliding window, statistical features of the power samples are computed. In this way, the same pre-processing can be applied in the two di erent datasets. A feature importance analysis can also be rerformed to analyze the contribution of the selected features in the models predictions. With this implementation, the real-time operation is directly related with the size of the window used.
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