Quantum Computing and Machine Learning for Smart Grid Management

Author:

Shinde Pravin Vishnu1,Maaliw III Renato R.2ORCID,Lakshmanarao A.3,Ghosh Gopal4ORCID

Affiliation:

1. Shah and Anchor Engineering College, University of Mumbai, India

2. College of Engineering, Southern Luzon State University, Philippines

3. Aditya College of Engineering and Technology, Jawaharlal Nehru Technological University, Kakinada, India

4. Lovely Professional University, India

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.

Publisher

IGI Global

Reference14 articles.

1. . Adel Merah et al. 2019, “Quantum Machine Learning for Smart Grids: A Comprehensive Survey”

2. Ahmadi, S. & Sedghi, M. S. (2020). Quantum Machine Learning in Smart Grids. IEEE Transactions on Industrial Informatics. IEEE.

3. Human gene and disease associations for clinical‐genomics and precision medicine research

4. Discovering biomarkers associated and predicting cardiovascular disease with high accuracy using a novel nexus of machine learning techniques for precision medicine

5. An Effective and Secure Mechanism for Phishing Attacks Using a Machine Learning Approach

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