Growing concerns about climate change and the scarcity of non-renewable resources have driven the development of sustainable energy technologies based on renewable sources such as solar, wind, hydropower, and biomass. However, the variability and intermittency of these sources represent a significant challenge for their efficient integration into current energy systems. In this context, mathematical modeling allows us to understand the dynamic behavior of these systems, while automatic control optimizes their operation in real time. Recently, artificial neural networks have gained prominence due to their ability to learn complex patterns and make accurate predictions, even in nonlinear systems. The combination of these three tools offers advanced solutions to improve the efficiency, reliability, and sustainability of energy processes. This area of research is key to moving toward a smarter, more flexible, and environmentally friendly energy model.

One of the main challenges in the transition to sustainable energy systems is the efficient and stable operation of processes based on renewable sources, such as solar and wind energy, whose generation is variable, intermittent, and difficult to predict. This variability complicates their integration into conventional electricity grids and can affect the reliability of energy supply. Furthermore, many systems lack advanced control strategies that optimize performance and minimize losses. The central problem lies in the lack of accurate models and intelligent control systems capable of adapting to changing environmental conditions and efficiently managing available energy resources. Faced with this, it is necessary to develop approaches that integrate mathematical modeling, automatic control, and artificial neural networks to improve the prediction, regulation, and optimization of sustainable energy processes. To achieve this, we propose researching and applying artificial intelligence techniques combined with automated control strategies to enable real-time decision-making, improve system efficiency, and facilitate the integration of renewable energy. This will significantly contribute to the development of a more sustainable, intelligent, and resilient energy model.

Types of manuscripts we're interested in:

1) Dynamic modeling of solar, wind, and hybrid energy systems.
2) Design and implementation of automated controllers for renewable sources.
3) Application of neural networks to predict energy generation and demand.
4) Optimization of energy storage systems using artificial intelligence.
5) Integration of renewable energies into smart grids.
6) Assessment of the environmental impact and energy efficiency of automated systems.

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Control of Sustainable Energy Processes