Cognitive Based Electric Power Management System

Akıncı, Tahir Çetin

doi:10.17694/bajece.1060998

Cited by 5 publications

(2 citation statements)

References 24 publications

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“…Intelligent energy management: Smart grids use machine learning-based communication protocols to collect, analyze, and manage data in electricity transmission and distribution systems [154].…”

Section: Aa Machine Learning-based Channel Estimation Methods For Veh...mentioning

confidence: 99%

Machine Learning-Based Error Correction Codes and Communication Protocols for Power Line Communication: An Overview

Akinci,

Erdemir,

Zengin

et al. 2023

IEEE Access

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This study endeavors to investigate the effectiveness of machine learning-based methodologies in enhancing the performance and reliability of Power Line Communication (PLC) systems. PLC systems constitute a critical component within the domains of energy management, monitoring, and automation. The fundamental objective herein is to contribute significantly to the scholarly discourse by conducting a comprehensive review encompassing research investigations and practical applications documented in the extant literature. The primary motivation underpinning this research is predicated upon the necessity for a meticulous evaluation of machine learning techniques that hold the potential to enhance the efficacy and stability of PLC systems. The deployment of these techniques bears the promise of engendering heightened levels of efficiency across the spectrum of energy management, communication, and automation systems. Within this scholarly quest, the study posits a hypothesis: Machine learning-based methodologies possess the capacity to effect marked improvements in the performance and reliability of PLC systems. Methodological scrutiny is executed through a comprehensive evaluation of diverse machine learning techniques, including, but not limited to, deep learning, support vector machines, and random forests, facilitated by a series of empirical experiments and simulations. Empirical findings resoundingly corroborate the proposition, substantiating a significant enhancement in the operational performance of PLC systems when these machine learning methods are judiciously employed. In summation, this study assumes the role of a catalyst in exploring latent, untapped potential inherent within machine learning-based methodologies, customarily calibrated to resonate within the intricate matrix of PLC systems. The zenith of this rigorous investigation stands poised to illuminate the path toward transformative advancements in the domains of energy management, communication, monitoring, and automation systems. The findings contribute significantly to the academic discourse, offering a compass for future research inquiries and practical applications within this burgeoning and dynamic field. INDEX TERMSPower line communication, error correction codes, machine learning, transmission control protocols, communication protocols, power networks.

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Section: Aa Machine Learning-based Channel Estimation Methods For Veh...mentioning

confidence: 99%

Machine Learning-Based Error Correction Codes and Communication Protocols for Power Line Communication: An Overview

Akinci,

Erdemir,

Zengin

et al. 2023

IEEE Access

Self Cite

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“…Data collection and feature extraction are crucial tasks in this context, necessitating extensive gameplay sessions with a large number of agents/humans [45,46]. The dataset content encompasses substantial repetitions of memory utilizing observations, rewards, and actions.…”

Section: ) Sand-box Game Platformmentioning

confidence: 99%

Design of the Integrated Cognitive Perception Model for Developing Situation-Awareness of an Autonomous Smart Agent

DAĞLARLI

2023

Balkan Journal of Electrical and Computer Engineering

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This study explores the potential for autonomous agents to develop environmental awareness through perceptual attention. The main objective is to design a perception system architecture that mimics human-like perception, enabling smart agents to establish effective communication with humans and their surroundings. Overcoming the challenges of modeling the agent's environment and addressing the coordination issues of multi-modal perceptual stimuli is crucial for achieving this goal. Existing research falls short in meeting these requirements, prompting the introduction of a novel solution: a cognitive multi-modal integrated perception system. This computational framework incorporates fundamental feature extraction, recognition tasks, and spatial-temporal inference while facilitating the modeling of perceptual attention and awareness. To evaluate its performance, experimental tests and verification are conducted using a software framework integrated into a sandbox game platform. The model's effectiveness is assessed through a simple interaction scenario. The study's results demonstrate the successful validation of the proposed research questions.

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Optimal energy management of integrated energy systems for strategic participation in competitive electricity markets

Wang

Zheng

Yang

2023

Energy

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Cognitive Based Electric Power Management System

Cited by 5 publications

References 24 publications

Machine Learning-Based Error Correction Codes and Communication Protocols for Power Line Communication: An Overview

Machine Learning-Based Error Correction Codes and Communication Protocols for Power Line Communication: An Overview

Design of the Integrated Cognitive Perception Model for Developing Situation-Awareness of an Autonomous Smart Agent

Optimal energy management of integrated energy systems for strategic participation in competitive electricity markets

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