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CONV1D-GRU: A Hybrid Model for Short-Term Electrical Load Forecasting

CONV1D-GRU: A Hybrid Model for Short-Term Electrical Load Fo...

引用

2023 International Telecommunications Conference, ITC-Egypt 2023

作者： Farid, Karim S. Ali, A.A. Salem, Sameh A. Mohamed, Amr E. Egyptian Electricity Holding Company Dept. Information Systems & Communications Cairo Egypt Helwan University - Faculty of Engineering Dept. Electrical Power Cairo Egypt Helwan University - Faculty of Engineering Dept. Computer & Systems Engineering Cairo Egypt

ISBN: (纸本)9798350326062

With the development of plans to replace conventional meters with smart meters to enhance the functionality and performance of power system, forecasting power consumption is still a challenge due to nonlinearity patterns, different types of noise in the datasets, and sometimes lack of data. Artificial Intelligence plays an important role in utilizing the data sent from smart meters, particularly in the area of short-term load forecasting, which is required to meet the needs of consumers. Based on such insight, this study suggests a hybrid model based on a one-dimension convolutional neural network and gated recurrent units (CONV1D-GRU), to forecast hourly power consumption. The outcome demonstrates that the suggested model is capable of encompassing the patterns in a historical data pertaining the hourly power use. The suggested model has outperformed the competing strategies in the mean absolute percentage error (MAPE%), and the coefficient of determination (R^2) metrics. © 2023 IEEE.

关键词： Smart meters

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TCN-MLP: A Hybrid Model for Short-Term Electrical Load Forecasting 1

TCN-MLP: A Hybrid Model for Short-Term Electrical Load Forec...

引用

1st International Conference of Intelligent Methods, systems and Applications, IMSA 2023

ISBN: (纸本)9798350335569

Using artificial intelligence (AI) to anticipate electrical consumption is one of the used applications of this technology. The analysis and training of AI models are done using data related to electricity consumption, including consumption itself, temperatures, working hours, the number of inhabitants, etc. AI models can help to reduce electricity expenses and consumption, which helps in identifying peak use periods and creating more efficient energy plans. Another use of artificial intelligence is to advise users on how to use less energy in a more efficient manner. Based on such insight, this study suggests a hybrid model based on a temporal convolutional network and multilayer perceptron (TCN-MLP), to forecast hourly power consumption. The results show that the recommended model can account for the patterns in the historical data relating to hourly power usage. TCN-MLP hybrid model has proved to be better than competing approaches in the coefficient of determination (R2) and the mean absolute percentage error (MAPE%) metrics. © 2023 IEEE.

关键词： Electric power utilization

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Synergia: Device-Edge Server Association for ISAC-assisted Mobile Edge Computing systems

Synergia: Device-Edge Server Association for ISAC-assisted M...

引用

2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Charatsaris, Panagiotis Penafiel, Arianna Santamaria Diamanti, Maria Tsiropoulou, Eirini Eleni Papavassiliou, Symeon National Technical University of Athens Institute of Communication and Computer Systems School of Electrical and Computer Engineering Athens Greece University of New Mexico Dept. of Electrical and Computer Engineering AlbuquerqueNM United States

ISBN: (纸本)9798350351255

The efficient operation of the unified Integrated Sensing and Communication (ISAC) - Mobile Edge Computing (MEC) systems is important for enhancing data sensing, communication, and computation processes in next-generation wireless systems. Despite prior research focusing on these systems, little attention has been given to optimizing the device-edge server associations. This paper addresses this gap by introducing the novel two-stage device-edge server association Synergia framework. Firstly, representative utility functions capture the characteristics of the devices and MEC servers by jointly considering their sensing, communication, and computation characteristics. Secondly, the Estimated Synergia framework leverages the Matching Theory to rapidly determine an initial device-server matching by disregarding the devices' externalities, i.e., the matching decisions of other devices. Thirdly, the Accurate Synergia model refines and improves this matching by using the coalition formation games, while considering the devices' externalities in optimizing the utilities of both the devices and the MEC servers. Extensive numerical evaluations demonstrate the Synergia's operational efficiency and scalability, outperforming reinforcement learningbased approaches. Also, a real-world application involving car accident detection validates its applicability. © 2024 IEEE.

关键词： Mobile edge computing

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TOPMG: Trust-Based Crowdsourcing through Multilateral Bargaining Game Theory

TOPMG: Trust-Based Crowdsourcing through Multilateral Bargai...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Charatsaris, Panagiotis Adesokan, Adedamola Rahman, Aisha B Tsiropoulou, Eirini Eleni Papavassiliou, Symeon National Technical University of Athens Institute of Communication and Computer Systems School of Electrical and Computer Engineering Athens Greece University of New Mexico Dept. of Electrical and Computer Engineering AlbuquerqueNM United States

ISBN: (纸本)9798350351255

Crowdsourcing plays a critical role in modern information gathering and task execution, yet it faces challenges regarding the task selection and equitable monetary incentives distribution. In this paper, we introduce the TOPMG framework, which addresses these challenges by enabling the workers to select tasks based on their historically experienced monetary incentives and the platforms' trustworthiness. Specifically, the TOPMG framework utilizes a reinforcement learning approach based on the principles of Optimistic Q-learning with Upper Confidence Bound (OQ-UCB) algorithm, guiding the platform selection process by considering the workers' monetary incentives, profit, and the platforms' trustworthiness. Also, the proposed framework introduces a multilateral bargaining game to allocate the platforms' monetary incentives to the workers by prioritizing their information contribution, fairness, and the platforms' reputation. Simulation results demonstrate TOPMG's operational dynamics, scalability, and efficacy, as well as its superiority over existing methodologies. © 2024 IEEE.

关键词： Reinforcement learning

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Compact Complementary Split Ring Resonator Based Sensor for Non-Invasive Glucose monitoring

Compact Complementary Split Ring Resonator Based Sensor for ...

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2024 International Telecommunications Conference, ITC-Egypt 2024

作者： Zahran, Adnan Aly, Youssif Ayman Eldamak, Angie R. Elsheakh, Dalia N. Ain Shams University Electronics and Communication Systems Engineering Program Faculty of engineering Cairo Egypt Ain Shams University Faculty of engineering Electronics and Communication Systems Engineering Dept. Cairo Egypt Badr University in Cairo Faculty of Engineering and Technology Electrical Dept. Badr City Cairo11829 Egypt

ISBN: (纸本)9798350351408

This paper presents a novel approach for noninvasive glucose monitoring using a Compact Complementary Split Ring Resonator (CCSRR) based sensor. The sensor design is initially simulated using High-Frequency Structure Simulator (HFSS) software, and then experimental validation is done. We carried out simulations with and without a finger phantom model to closely resemble conditions in real situations. The sensor exhibits high sensitivity and accuracy in detecting glucose concentrations within blood samples. Vector Network Analyzer was used along with the sensor to validate the simulation results. The results demonstrate close agreement between simulation and experimental data, affirming the effectiveness of the proposed sensor design for non-invasive glucose monitoring. © 2024 IEEE.

关键词： Glucose sensors

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TRUSTCACHE: Trust-based Content Caching in Information-Centric Networks

TRUSTCACHE: Trust-based Content Caching in Information-Centr...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Tsikteris, Sean Rahman, Aisha B. Siraj, Md Sadman Charatsaris, Panagiotis Tsiropoulou, Eirini Eleni Papavassiliou, Symeon National Technical University of Athens Institute of Communication and Computer Systems School of Electrical and Computer Engineering Athens Greece University of New Mexico Dept. of Electrical and Computer Engineering AlbuquerqueNM United States

ISBN: (纸本)9798350351255

The Information-Centric Networking (ICN) paradigm has reshaped the modern network architectures and promises efficient content delivery to the end-users. This paper introduces TRUSTCACHE, a novel framework enabling the content caching within ICNs and focusing on the trust-based ICN selection and optimal cache memory allocation to the Content Providers (CPs). The TRUSTCACHE framework incorporates the Optimistic Q-learning with Upper Confidence Bound reinforcement learning algorithm that enables the CPs to autonomously select ICNs based on their cache memory availability and trust levels. Also, TRUSTCACHE enables the CPs to jointly consider the reliability of the ICNs and their cache memory availability by integrating a novel trust model. Furthermore, TRUSTCACHE leverages the multilateral bargaining principles in order to ensure the optimal cache memory allocation among the CPs, in terms of aligning with their profit margin characteristics. Simulation-based experiments validate TRUSTCACHE's operational efficiency across diverse CP profit margin profiles and highlight its superiority over alternative models lacking trust-based ICN selection or employing proportional fairness strategies for cache memory allocation. © 2024 IEEE.

关键词： Reinforcement learning

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Intelligent Accident Detection and Severity Analysis System for Enhanced Emergency Response 1

Intelligent Accident Detection and Severity Analysis System ...

引用

1st IEEE International Conference on Computing and Data Science, ICCDS 2024

作者： Harini, P. Chidambarathanu, K. Ramachandran, Lalitha R.M.K Engineering College Dept. Computer Science and Business Systems Kavarapettai India R.M.K Engineering College Dept. Science and Humanities Kavarapettai India

ISBN: (纸本)9798350365337

Road accidents cause serious injuries and deaths worldwide. The previous year's Ministry of Road Transport and Highways Annual Report on Road Accidents in India showed that many road accidents were fatal. A large percentage of Indian deaths occur without rapid medical intervention or first assistance. Delays in reporting incidents to emergency services increased fatalities. A sophisticated method using deep learning and analytics to locate CCTV accidents, classify their severity, and speed up emergency response. Accident detection and analysis are efficient with cutting-edge technologies. The accident detection phase uses YOLOv7, a deep-learning object detection model, to evaluate CCTV footage in real time. A stratified K-fold deep neural network classifies accidents as major or minor based on collision severity. Real-time GPS tracking alerts ambulance drivers within three km of the accident using the mobile app. This expedites emergency response, lowering mortality by reducing the time between the accident and medical assistance. © 2024 IEEE.

关键词： Object detection

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Comparative Reliability Analysis of a Microgrid versus Zonal Nanogrids

Comparative Reliability Analysis of a Microgrid versus Zonal...

引用

2024 IEEE Energy Conversion Congress and Exposition, ECCE 2024

作者： Smith, Gabrielle Van Bossuyt, Douglas L. Oriti, Giovanna Vygoder, Mark Alves, Richard Naval Postgraduate School Systems Engineering Dept. MontereyCA United States Naval Postgraduate School Electrical and Computer Engineering Dept. MontereyCA United States University of Wisconsin Milwaukee Electrical Engineering Dept. MilwaukeeWI United States

ISBN: (纸本)9798350376067

Nanogrids are small-scale power distribution systems that can operate independently or integrated with the grid like microgrids. These nanogrids can interconnect in a network and have built-in redundancy. This paper investigates the potential of bringing zonal shipboard power ashore with zonal nanogrids to enhance energy reliability at industrial facilities which addresses the limitations of current microgrid systems and emergency backup generators. A reliability analysis is conducted by using the reliability block diagram method to compare microgrid and zonal nanogrid configurations with various control architectures to identify the most reliable solution. The two common types of control strategies are centralized and decentralized. The results indicate that decentralized zonal nanogrids offer higher reliability over a 14-day grid power outage. Furthermore, a sensitivity analysis is conducted to identify the most impactful component in the reliability of zonal nanogrids which is the power transmission lines and fiber optic cables. © 2024 IEEE.

关键词： Power distribution reliability

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Single-Phase Unidirectional Multilevel Rectifiers Based on Cascaded Cells

Single-Phase Unidirectional Multilevel Rectifiers Based on C...

引用

2024 IEEE Energy Conversion Congress and Exposition, ECCE 2024

作者： Monteiro, Amanda Pereira Jacobina, Cursino Brandão Da Costa Bahia, Filipe Antônio De Sousa, Reuben Palmer Rezende Cardoso, Jean Torelli Pinto, Alan Robson Andrade Federal University of Ceará Mechanical Engineering Dept. Fortaleza Brazil Federal University of Campina Grande Electrical Engineering Dept. Campina Grande Brazil Federal University of Bahia Electrical and Computer Engineering Dept. Salvador Brazil Federal University of Pernambuco Electronics and Systems Dept. Recife Brazil

ISBN: (纸本)9798350376067

This paper proposes and investigates two new single-phase unidirectional multilevel rectifiers based on cascaded cells. Compared to conventional topology, as the number of cascaded cells increases, proposed ones employ, proportionally, a reduced controlled switch count and driver circuits, which may reduce control complexity and increase reliability. The dc-link voltage ratios are calculated for asymmetrical operation to provide the maximum number of voltage levels generated in the ac side of the rectifiers, ensuring a high power quality. Besides, the proposed configurations can synthesize a higher number of voltage levels by employing the same controlled switch count as the conventional one so that it is possible to provide a higher levels-per-switch ratio. Simulation and experimental results are presented to validate the performance and feasibility of the proposed topologies. © 2024 IEEE.

关键词： Topology

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CNN Performances for Stunting Face Image Classification 4

CNN Performances for Stunting Face Image Classification

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4th International Conference on Electrical engineering and computer Science, ICECOS 2024

作者： Yunidar Roslidar Yusni Nasaruddin Fitriarnia Faculty of Engineering Universitas Syiah Kuala Doctoral Program School of Engineering Dept. of Electrical and Computer Engineering Banda Aceh Indonesia Faculty of Engineering Universitas Syiah Kuala Dept. of Electrical and Computer Engineering Banda Aceh Indonesia Faculty of Medicine Universitas Syiah Kuala Banda Aceh Indonesia

ISBN: (数字)9798350368253

ISBN: (纸本)9798350368253

Detecting stunting in children is an important issue currently faced by healthcare providers to follow up efforts to reduce the number of stunted children in a region. Emerging research has applied deep learning to many aspects of stunting, including classifying stunting in children through facial images. This study aims to extend previous work on CNN benchmarking in classifying facial images of stunted children and normal children. Here, we trained, validated, and tested five more popular CNN architectures, namely ShuffleNetV2, InceptionV3, MobileNetV2, EfficientNetB0, and VGG19;the research was applied to the same dataset built in previous work. We presented the training and validation loss graphs, and the confusion matrices of all architectures. We measured CNN's accuracy, precision, recall, and F1-measures of the testing stage. Simulation results indicated that three CNN models, namely MobileNetV2, VGG19, and EfficientNetB0 performed comparably or better than previous CNNs in classifying children's facial images. VGG19 and EfficientNetB0 achieved testing accuracies of 100%. © 2024 IEEE.

关键词： Convolutional neural networks

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