The energy sector faces challenges related to the optimization of energy systems, the integration of renewable energy sources, grid management, and the reduction of environmental impacts. Traditional methods may struggle to address the complexity and variability of modern energy systems. Machine learning (ML) provides tools and algorithms that can analyze large datasets, identify patterns, and make predictions or decisions without explicit programming. In the context of energy, ML can be applied to optimize energy production, improve energy storage systems, enhance grid management, and support the transition to cleaner and more sustainable energy sources. It can also be employed in optimizing the performance of various energy conversion technologies, such as solar panels, wind turbines, and energy storage systems. By exploring the intersection of machine learning and energy conversion/utilization, researchers aim to address critical issues, optimize energy systems, and contribute to the global transition towards a more sustainable and efficient energy landscape.
The overarching goal of research in "Machine Learning in Energy Conversion and Utilization" is to leverage advanced machine learning techniques to address challenges, enhance efficiency, and promote sustainability in energy systems. The primary objectives include optimizing energy conversion processes, improving energy utilization, and contributing to the development of intelligent, adaptive, and environmentally friendly energy systems. The Research Topic and its key technologies include, but are not limited to:
1. Optimization of Energy Conversion Technologies: Develop machine learning models to optimize the performance of solar, wind, and energy storage systems.
2. Intelligent Energy Management and Smart Grids: Explore the application of machine learning in smart grids for real-time decision-making, grid optimization, and demand-side management.
3. Renewable Energy Forecasting: Investigate advanced machine learning models for accurate forecasting of renewable energy generation, including solar and wind power.
4. Environmental Impact Mitigation: Explore machine learning applications in minimizing the environmental impact of energy systems, including life cycle analysis and emission prediction.
5. Explainable AI in Energy Systems: Investigate the use of explainable AI models in decision support systems for transparent and trustworthy energy management.
6. Emerging Technologies and Cross-Disciplinary Approaches: Explore the synergy of machine learning with emerging technologies (e.g., edge computing) and cross-disciplinary approaches in energy research.
Keywords: machine learning, energy, conversion, utilization, sustainability
Important note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
