AI‐Driven Optimization of Renewable Energy Systems

The increasing reliance of renewable energies to meet global energy needs necessitates smart mechanisms that might accommodate the variability and intermittency inherent in the sources of energy like solar and wind would have to be addressed accordingly. This chapter aims to incorporate training methods of Artificial intelligence in the task of optimizing the forecasting, load balancing and efficiency in the grid of renewable energy systems. To further enhance accuracy of the prediction of the energy generation and consumption process, the paper suggests the applications of time series forecasting algorithms and deep learning networks on the basis of the machine learning models. Also, reinforcement learning and optimization algorithms are used to gain dynamically managed load distribution and optimization of grid stability. Through our AIMS framework, many of the aforementioned issues can be eliminated, including facilitation of energy optimal distribution through distributed network design, minimizing fossil versions of back up systems used, and real time decision making. The outcomes of the simulation provide the ability of AI in reforming the capable modern energy infrastructures through substantial enhancement to the predictive performance and grid reaction. This chapter towards the development of the natural environment as a potential source of energy contributes to the realization of sustainable multiple intelligent energy systems that coincide with the global environmental interest as well as comply with the shift to more precise and green energy provided to be supplied to the power grids.