Electrical and Electronics Commons^™

Local Electricity Market Operation In Presence Of Residential Energy Storage In Low Voltage Distribution Network: Role Of Retail Market Pricing, Aziz Saif, Shafi K. Khadem Shafi K. Khadem, Michael Conlon, Brian Norton

Articles

Local Electricity Market (LEM) appears as a promising consumer-centric market-based approach that extends the self-consumption method, widely implemented in residential households, to collective self-consumption in the local energy communities, enabled through peer-to-peer (P2P) transactions. To facilitate the integration of LEM in the wholesale electricity market (WEM), it is paramount to comprehend the synergy of retail electricity pricing on the LEM operation hosted in the low-voltage distribution network (LVDN). The paper presents a co-simulation framework consisting of a local electricity market model coupled with a three-phase distribution network simulator to perform a holistic case study for a smart energy community in …

Nipuna: A Novel Optimizer Activation Function For Deep Neural Networks, Golla Madhu, Sandeep Kautish, Khalid Abdulaziz Alnowibet, Hossam Zawbaa, Ali Wagdy Mohamed

Articles

In recent years, various deep neural networks with different learning paradigms have been widely employed in various applications, including medical diagnosis, image analysis, self-driving vehicles and others. The activation functions employed in deep neural networks have a huge impact on the training model and the reliability of the model. The Rectified Linear Unit (ReLU) has recently emerged as the most popular and extensively utilized activation function. ReLU has some flaws, such as the fact that it is only active when the units are positive during back-propagation and zero otherwise. This causes neurons to die (dying ReLU) and a shift in …

Interpreting Energy Utilisation With Shapley Additive Explanations By Defining A Synthetic Data Generator For Plausible Charging Sessions Of Electric Vehicles, Prasant Kumar Mohanty, Gayadhar Panda, Malabika Basu, Diptendu Sinha Roy

Articles

Electric vehicles (EVs) are an effective solution for reducing reliance on non-renewable energy sources. However, the lack of charging infrastructure and concerns over their range are some of the biggest hurdles to adopting EVs. Charging infrastructure for EVs is, however, on the rise. Proper planning of charging stations vis-`a-vis road networks and related points of interest such as transportation hubs, schools, shopping centres, etc., alongside such roads become vital to laying out a plan for such infrastructure, particularly for developing countries like India where EV adoption is relatively in a nascent stage. Synthetic datasets can help overcome these hurdles and …

Lightnet: A Novel Lightweight Convolutional Network For Brain Tumor Segmentation In Healthcare, Dongyuan Wu, Junyi Tao, Zhen Qin, Rao Asad Mumtaz, Jin Qin, Linfang Yu, Jane Courtney

Articles

Diagnosis, treatment planning, surveillance, and the monitoring of clinical trials for brain diseases all benefit greatly from neuroimaging-based tumor segmentation. Recently, Convolutional Neural Networks (CNNs) have demonstrated promising results in enhancing the efficiency of image-based brain tumor segmentation. Most current work on CNNs, however, is devoted to creating increasingly complicated convolution modules to improve performance, which in turn raises the computing cost of the model. This work proposes a simple and effective feed-forward CNN, LightNet (Light Network). Based on multi-path and multi-level, it replaces traditional convolutional methods with light operations, which reduces network parameters and redundant feature maps. In the …

H2020 Auto-Dan Project: Enhance The Participation Of The Community To Demand Response By Providing The State-Of-The-Art Technological And Policy Solution, Rene Peeren, Dharmesh Dabhi, John Dalton

Conference papers

The growing demand for electricity in Europe has increased the need for a more flexible and sustainable power system. In recent years, Demand Response (DR) has emerged as a promising solution to meet this need, by providing an opportunity for residential and smaller commercial consumers to actively participate in the electricity market. This research paper investigates the potential for DR among the residential community and small commercial electricity consumers in Europe and identifies the technological barriers and drivers that impact consumer engagement with DR programs in Europe. The different DR opportunities are identified and validated at the six different demo …

Quantum Classifiers For Video Quality Delivery, Tautvydas Lisas, Ruairí De Fréin

Conference papers

Classical classifiers such as the Support Vector Classifier (SVC) struggle to accurately classify video Quality of Delivery (QoD) time-series due to the challenge in constructing suitable decision boundaries using small amounts of training data. We develop a technique that takes advantage of a quantum-classical hybrid infrastructure called Quantum-Enhanced Codecs (QEC). We evaluate a (1) purely classical, (2) hybrid kernel, and (3) purely quantum classifier for video QoD congestion classification, where congestion is either low, medium or high, using QoD measurements from a real networking test-bed. Findings show that the SVC performs the classification task 4% better in the low congestion …

Optimising Electric Vehicle Charging Infrastructure In Dublin Using Geecharge, Alexander Mutua Mutiso, Ruairí De Fréin, Ali Malik, Eliel Kibanza, Marco Sahbane, Maxime Pantel

Conference papers

Range anxiety poses a hurdle to the adoption of Electric Vehicles (EVs), as drivers worry about running out of charge without timely access to a Charging Point (CP). We present novel methods for optimising the distribution of CPs, namely, EV portacharge and GEECharge. These solutions distribute CPs in Dublin, in this paper, by considering the population density and Points Of Interest (POIs) or road traffic. The object of this paper is to (1) develop and evaluate methods to distribute CPs in Dublin city; (2) optimise CP allocation; (3) visualise paths in the graph network to show the most used roads …

Architecting Future Multi-Modal Networks Coexistence, Generalization & Testbeds, Maqsood Ahamed Abdul Careem

Legacy Theses & Dissertations (2009 - 2024)

Next Generation (xG) wireless networks are poised to revolutionize the way people, devices, data and processes sense, communicate, interact, and collectively enable a wide range of emerging applications, ranging from smart cities, connected healthcare, and advanced vehicular communication to extended reality. To facilitate this seamless interoperability, these networks need to evolve to accommodate and integrate multiple modalities in communication/ sensing technologies and spectrum, heterogeneous networks, trends in signal-processing (statistical, AI-driven, and distributed systems), centralized and distributed architectures, and device/ network hardware resources. However, to cater to the high-target metrics and wide-range of applications, these multi-modal networks must efficiently address multi-faceted …

Optimal Battery Sizing For An Industrial Factory: A Hybrid Approach Using Particle Swarm Optimization And Load Estimation, Muhammad Hammad Hassan

Chulalongkorn University Theses and Dissertations (Chula ETD)

Solar photovoltaic (PV) systems are crucial in addressing global energy demands with clean and renewable electricity. However, the intermittent nature of solar energy requires integrating batteries to ensure a reliable power supply. This study focuses on optimizing battery size for solar PV systems to balance energy storage during high irradiance periods and discharge during low sunlight, thereby enhancing system efficiency and cost-effectiveness. Accurate load forecasting and load estimation, essential for effective energy storage management, is achieved using long short-term memory (LSTM) networks, which excel in handling time series data. Particle Swarm Optimization (PSO) is then employed to determine the optimal …

Blockchain Framework For Secured On-Demand Patient Health Records Sharing, Meryem Abouali

Dissertations and Theses

As the healthcare industry continues to digitize and share patient data for a better understanding of patient health history, cybersecurity must remain a top priority. However, patient health record (PHR) data is extremely sensitive and faces significant challenges due to its distributed nature across various healthcare facilities and providers, which creates a lack of interoperability among healthcare systems. Most patient health record systems adopt a centralized management structure and deploy PHRs to the cloud, which raises privacy concerns when sharing patient information over a network. Thus, there is a need for a framework that considers patient privacy and data security …

Single Image Super Resolution Using Deep Attention Network, Jagrati Talreja

Chulalongkorn University Theses and Dissertations (Chula ETD)

This thesis introduces two innovative frameworks for Single Image Super Resolution (SISR) that enhances image quality while optimizing computational efficiency. The motivation for this research is driven by the need to overcome the limitations of current SISR techniques like capturing long-range dependencies, enhancing multi-scale feature learning, mitigating over-smoothing, and managing computational demands. The choice to incorporate attention mechanisms in both frameworks is motivated by their proven ability to allow the model to focus on the most relevant parts of the image, thereby improving the reconstruction of high-resolution details from low-resolution inputs. By integrating attention mechanisms with inception blocks, the proposed …

A Review Of Piezoelectric Footwear Energy Harvesters: Principles, Methods, And Applications, Bingqi Zhao, Feng Qian, Alexander Hatfield, Lei Zuo, Tian-Bing Xu

Mechanical & Aerospace Engineering Faculty Publications

Over the last couple of decades, numerous piezoelectric footwear energy harvesters (PFEHs) have been reported in the literature. This paper reviews the principles, methods, and applications of PFEH technologies. First, the popular piezoelectric materials used and their properties for PEEHs are summarized. Then, the force interaction with the ground and dynamic energy distribution on the footprint as well as accelerations are analyzed and summarized to provide the baseline, constraints, potential, and limitations for PFEH design. Furthermore, the energy flow from human walking to the usable energy by the PFEHs and the methods to improve the energy conversion efficiency are presented. …

Unmanned Aircraft Systems For Precision Meteorology: An Analysis Of Gnss Position Measurement Error And Embedded Sensor Development, Karla S. Ladino

Theses and Dissertations--Biosystems and Agricultural Engineering

The overarching objective of this research was to enhance our comprehension of the three-dimensional precision of meteorological measurements obtained using small unmanned aircraft systems (UAS). Two complimentary experiments were conducted to achieve this objective.

The first experiment entailed the development and implementation of a system to determine the global navigation satellite system (GNSS) position accuracy on a UAS platform. This system was utilized to assess the static and dynamic accuracy of L1 and L1/L2 GNSS receivers in real-time kinematic (RTK) and non-RTK fix modes. Adjusted two-sample t-tests revealed significant differences in horizontal and vertical error between RTK and non-RTK receivers …

Running Shoe Pedometer, Benjamin Kasper

Williams Honors College, Honors Research Projects

Running shoe pedometer aims to solve the issue of worn out running shoes. It can be difficult to know just how many miles you have run in your shoes and when a new pair is needed. Running in old shoes and worn out shoes is heavily linked to injury. My proposed project is a device that is powered by the compressive forces on the shoes soles that counts the number of steps the wearer takes using a microcontroller. Then, when the shoe reaches milestone that indicate it has been used 75% 90% and 100% of its expected life, it will …

Lidar Remote Sensing Of Aerosol And Ozone Profiles And Application To Air Quality Studies In New York City Area, Dingdong Li

Dissertations and Theses

Ozone (O₃) and PM2.5 (Particulate Matter with a diameter less than 2.5 μm) are the main pollutants in New York City (NYC) due to high levels of anthropogenic emissions, complex urban meteorology, and regional transport. However, vertical distribution observations are still lacking. This thesis utilizes lidar remote sensing techniques to retrieve the vertical distribution of O₃ and aerosols and study air pollution episodes in the NYC metropolitan area.

The thesis includes three parts. The first part analyzes continuous ceilometer observations of aerosols and atmospheric mixed-layer height (ML). We investigate the vertical, diurnal, and seasonal characteristics of the …

Long-Range Aceo Phenomena In Microfluidic Channel, Diganta Dutta, Keifer Smith, Xavier Palmer

Electrical & Computer Engineering Faculty Publications

Microfluidic devices are increasingly utilized in numerous industries, including that of medicine, for their abilities to pump and mix fluid at a microscale. Within these devices, microchannels paired with microelectrodes enable the mixing and transportation of ionized fluid. The ionization process charges the microchannel and manipulates the fluid with an electric field. Although complex in operation at the microscale, microchannels within microfluidic devices are easy to produce and economical. This paper uses simulations to convey helpful insights into the analysis of electrokinetic microfluidic device phenomena. The simulations in this paper use the Navier–Stokes and Poisson Nernst–Planck equations solved using COMSOL …

Wifi Sensing At The Edge Towards Scalable On-Device Wireless Sensing Systems, Steven M. Hernandez

Theses and Dissertations

WiFi sensing offers a powerful method for tracking physical activities using the radio-frequency signals already found throughout our homes and offices. This novel sensing modality offers continuous and non-intrusive activity tracking since sensing can be performed (i) without requiring wearable sensors, (ii) outside the line-of-sight, and even (iii) through the wall. Furthermore, WiFi has become a ubiquitous technology in our computers, our smartphones, and even in low-cost Internet of Things devices. In this work, we consider how the ubiquity of these low-cost WiFi devices offer an unparalleled opportunity for improving the scalability of wireless sensing systems. Thus far, WiFi sensing …

Comprehensive Fault Diagnostics And Performance Forecasting Of Wind Turbines Through Condition Monitoring Solutions, Shuo Zhang

Doctoral

The operation and maintenance (O&M) issues of wind turbines (WTs) are challenging because unplanned maintenance, caused by sudden component failures, can bring about durable downtimes and significant revenue losses. It is important to carry out effective fault diagnostics and prognostics schemes under the rapid development of wind power generation. Hence, Condition Monitoring (CM) solutions focus on measurements and detections of high-risk WT components, which could result in high failure rates and long downtimes. Machine Learning (ML) models have been commonly applied in CM to allocate imminent indications of failure or degradation for curtailing the O&M costs of WTs. By way …

Ai-Driven Security Constrained Unit Commitment Using Predictive Modeling And Eigen Decomposition, Talha Iqbal

Graduate Theses, Dissertations, and Problem Reports (ETD)

Security Constrained Unit Commitment (SC-UC) is a complex large scale mix integer constrained optimization problem solved by Independent System Operators (ISOs) in the daily planning of the electricity markets. After receiving offers and bids, ISOs have only few hours to clear the day-ahead electricity market. It requires a lot of computational effort and a reasonable time to solve a large-scale SC-UC problem. However, exploiting the fact that a UC problem is solved several times a day with only minor changes in the system data, the computational effort can be reduced by learning from the historical data and identifying the patterns …

High Energy Blue Light Induces Oxidative Stress And Retinal Cell Apoptosis, Jessica Malinsky

Capstone Showcase

Blue light (BL) is a high energy, short wavelength spanning 400 to 500 nm. Found in technological and environmental forms, BL has been shown to induce photochemical damage of the retina by reactive oxygen species (ROS) production. Excess ROS leads to oxidative stress, which disrupts retinal mitochondrial structure and function. As mitochondria amply occupy photoreceptors, they also contribute to oxidative stress due to their selectively significant absorption of BL at 400 to 500 nm. ROS generation that induces oxidative stress subsequently promotes retinal mitochondrial apoptosis. BL filtering and preventative mechanisms have been suggested to improve or repair BL-induced retinal damage, …