A systematic review of artificial intelligence driven energy management in smart grids

The rapid migration of electrical grids towards Smart Grid (SG) architectures produces high-dimensional data streams that must be processed intelligently in real time. Traditional modelling, optimisation, and control approaches cannot handle the growing complexity of contemporary power systems. This comprehensive study aims to provide a synopsis of AI applications in smart grid settings for load forecasting, transient and voltage stability analysis, fault detection, and cybersecurity. This work provides a unified analytical framework that links supervised, unsupervised, reinforcement, and ensemble learning techniques to specific smart grid operational problems, in contrast to prior surveys that address these areas separately. The main contributions are (i) a structured AI-smart grid functional taxonomy; (ii) critical analysis of the challenges of data quality, model interpretability, computational scalability and adversarial security; and (iii) a roadmap for explainable, reliable and carbon-neutral intelligent energy systems. Compared with more conventional methods, AI-based solutions often outperform them in prediction accuracy, stability, categorisation speed, and defect identification accuracy. However, there are major challenges in real-world deployment, standardisation and regulatory compliance.