Smart Grid Optimization Using AI-Driven Load Forecasting and Renewable Energy Integration

Abstract

There is currently an immense revolution in the modern energy world, as it is greatly influenced by massive entry of renewable energy, heightened thoughts on sustainability, and the incredibly fast move to artificial intelligence (AI). This transition revolves around smart grids that are meant to enhance efficiency, versatility, and stability of the power networks. One of the most important facilitators of smart grids is the AI-induced load forecasting that optimizes the prediction of energy demand and allows the easy integration of renewable energy. This paper will find out how AI can be used to optimize smart grid operations relating to load forecasting accuracy, renewable integration and energy efficiency issues. It is possible to identify the novelty of machine learning, metaheuristic optimization, and hybrid modelling through reviewing state-of-the-art works that implemented solutions to the uncertainties in the demand and the supply.