Energy grid optimization using deep machine learning: A review of challenges, opportunities, and implementation strategies in MATLAB
1 Automation and Process Control Engineer, Gist Limited, Bristol, United Kingdom.
2 Western Illinois University - Department of Economics and Decision sciences, USA
3 Researcher, Department of Physics and Astronomy, University of Arkansas at Little Rock, USA
4 Graduate Assistant, University of Northern Iowa, USA.
5 Senior Software Engineer, Crossoverhealth, USA.
6 Process Engineer and Renewable Energy Technologist, University of Applied Sciences Bremerhaven, Germany.
Review Article
World Journal of Advanced Research and Reviews, 2024, 23(02), 1591–1609
Publication history:
Received on 09 July 2024; revised on 19 August 2024; accepted on 21 August 2024
Abstract:
The optimization of energy grids is critical for enhancing efficiency, reliability, and sustainability in modern power systems. This paper explores the implementation of deep learning algorithms for energy grid optimization, emphasizing the use of MATLAB as a versatile tool for developing and testing these advanced methods. The study begins with an overview of the current challenges faced by energy grids, including the integration of renewable energy sources, demand forecasting, and grid stability. It then delves into the opportunities presented by deep learning, such as improved prediction accuracy, real-time decision-making, and adaptive control strategies. By leveraging MATLAB’s powerful computational capabilities and extensive libraries, various deep learning techniques, including neural networks, reinforcement learning, and deep reinforcement learning, are applied to optimize grid performance. The paper also discusses the practical challenges of implementing these algorithms, such as computational complexity, data requirements, and model interpretability. Through detailed case studies, the effectiveness of deep learning in addressing specific grid optimization problems is demonstrated, providing valuable insights for researchers and practitioners. This work highlights the potential of combining MATLAB with deep learning to advance energy grid optimization, paving the way for smarter, more resilient power systems
Keywords:
Energy Grid Optimization; Deep Learning; Renewable Energy Integration; Demand Forecasting; Grid Stability; MATLAB; Neural Networks; Reinforcement Learning
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Copyright © 2024 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0