Explainable AI in smart grid applications

The transformation of conventional electrical networks into Smart Grids represents a significant advancement in power systems, driven by the integration of cutting-edge developments in artificial intelligence (AI), communication, and computational technologies. Unlike traditional grids, Smart Grids utilize vast volumes of data collected from smart meters, sensors, remote control units, and end-user devices. This data abundance enhances system monitoring, supports accurate demand forecasting, and enables intelligent decision-making to improve energy efficiency, reliability, and power quality. However, the complexity and scale of such datasets often exceed the capabilities of conventional computational methods, making AI-based approaches increasingly indispensable. Despite their advantages, AI models are often criticized for their lack of transparency and interpretability, which poses challenges in safety-critical and regulated environments such as power systems. To address these concerns, Explainable Artificial Intelligence (XAI) has emerged as a promising paradigm to make AI decisions more understandable and trustworthy. XAI techniques provide insights into model behavior, enabling greater user trust, regulatory compliance, and informed operational decisions. This paper highlights the essential role of XAI in the advancement of smart grid technologies and presents a concise review of current applications where XAI has been successfully integrated, including anomaly detection, load forecasting, energy management, and fault diagnosis. By bridging the gap between model performance and interpretability, XAI contributes significantly to the development of sustainable, secure, and transparent smart energy systems.