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Quantum Computing and Machine Learning for Smart Grid Management
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Author(s): Pravin Vishnu Shinde (Shah and Anchor Engineering College, University of Mumbai, India), Renato R. Maaliw III (College of Engineering, Southern Luzon State University, Philippines), A. Lakshmanarao (Aditya College of Engineering and Technology, Jawaharlal Nehru Technological University, Kakinada, India)and Gopal Ghosh (Lovely Professional University, India)
Copyright: 2024
Pages: 22
Source title:
Real-World Challenges in Quantum Electronics and Machine Computing
Source Author(s)/Editor(s): Christo Ananth (Samarkand State University, Uzbekistan), T. Ananth Kumar (IFET College of Engineering, India)and Osamah Ibrahim Khalaf (Al-Nahrain University, Iraq)
DOI: 10.4018/979-8-3693-4001-1.ch016
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Abstract
Quantum computers can solve difficult optimization issues, unlike regular computers. The proposed system optimizes smart grid energy distribution, load balancing, and resource allocation using quantum annealing and Grover's method. Quantum optimization should boost processing speed and accuracy. Quantum algorithms optimize electricity flow, mitigate transmission loss, and boost grid efficiency. By monitoring real-time data and changing loads, dynamic load balancing reduces smart grid bottlenecks and optimizes resource utilization. Machine learning algorithms will precisely forecast energy demand, enhancing grid control and resource distribution. Quantum computing and machine learning enhance smart grid management. From this connectivity, the smart grid gains exceptional efficiency, dependability, and agility, providing a more robust and environmentally friendly energy infrastructure.
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