Machine learning methodologies for real-world resilient energy systems - Boodhoo, Khivishta (2024)
With the growing global population, energy demand and consumption have increased. Higher fossil fuel consumption has led to increased greenhouse gas emissions and global warming. However, the 4th Industrial Revolution, driven by digital, biological, and physical innovations, has shown an increased focus on environmental protection and clean energy use. Technologies such as Artificial Intelligence can help meet the rising energy requirements sustainably. Artificial Intelligence mimics human intelligence to automate tasks, optimise processes, and enable predictive analytics for better decision-making. Machine learning, which is a branch of Artificial Intelligence, uses data driven algorithms to solve problems. Its applications have been widely investigated in data-rich sectors, including the energy sector. Despite its promising benefits, such as cost savings, improved efficiency, grid maintenance, and reliability, there are still multiple areas of well-needed research. For instance, it has been found that energy systems are complex, everchanging, and may need more robust algorithms owing to their dynamic and intermittent nature. The constant updates, upgrades, replacement, and integration of renewable energies in such systems require further research to improve their scalability and resilience, and to allow the full harness of the potential of machine learning in addressing those pressing energy challenges.