Challenges and Prospects of Different NILM Frameworks for Real-World Applications

Non-Intrusive Load Monitoring (NILM) is an emerging technology that aims to disaggregate household or industrial electrical consumption by identifying and monitoring individual appliances from a single point of measurement, typically at the main power source. While NILM offers significant potential for energy efficiency, its real-world application faces several challenges. This paper provides an overview of the various NILM frameworks, focusing on both classical and machine learning-based approaches. Key challenges include the lack of standardized datasets, the complexity of feature extraction from noisy real-world data, scalability across different environments, and limitations in accurately identifying appliances with similar power consumption patterns. Despite these hurdles, recent advancements in deep learning, edge computing, and the integration of Internet of Things (IoT) technologies have opened new prospects for improving NILM accuracy and adaptability. The paper explores these innovations, highlighting their potential to enhance NILM's reliability and scalability in diverse settings, such as smart homes, industrial plants, and smart grids. Finally, the discussion addresses future research directions to bridge the gap between laboratory-level performance and largescale deployment in real-world applications.