AI-powered Smart Grids: Energy Optimization

This paper aims at explaining how AI can augment smart grids toward improved energy management. Mainly, the research is concerned with using artificial intelligence together in order to optimize energy distribution, minimize the cost of running organizations and finally establish means to harness sustainable energy resources. Four AI algorithms are utilized in this study: During the smart grid, load forecasting, fault detection, and energy optimization, ANN, SVM, GA, and RL approaches can be used. Based on the experimental findings, it is shown that the ANN achieved a 92% forecast precision; on the other hand, the utility of both the SVM and GA algorithms in energy optimization ranged from 18–23%. The RL algorithm returned the best result and cut wastage by 32% and enhanced load balance efficiency. These results demonstrate that AI strategy performs better than the conventional energy management systems, particularly for accommodating renewable generation and dynamic grid loads. By optimising the utilisation of the grid, and utilising artificial intelligence, energy losses are kept to an absolute minimum, thus supplementing sustainable efforts. The study establishes that smart grid using AI technology is the innovative way of enhancing energy management and thus sustainable smart infrastructures. In future work, issues of data security and scalability will be targeted in order to enhance the application of AI in smart grids.