TL;DR
The feedback processes to reduce the estimation discrepancy in NILM are discussed and a closed-loop learning approach is proposed that may significantly improve the accuracy of NILM, and it is applicable for practical applications.
A literature review of Machine Learning applications in the domain of Electricity Theft Detection (ETD) that is grounded in 71 scholarly studies produced between 2021 and 2026 and selected according to criteria of machine learning application in the process of theft/NTL detection and accompanying empirical validation.
The role of NILM is highlighted in supporting smarter energy monitoring, conservation initiatives, and sustainable energy management strategies for future smart grid systems, and the potential of advanced machine learning algorithms in NILM applications is underscored.
A Tsetlin Machine (TM)-based NILM framework is proposed, targeting real-time applications on resource-constrained microcontrollers (MCUs), enabling privacy-preserving edge deployment and achieves an average precision of 90% and recall of 96% for two-appliance classification.
Nonintrusive load monitoring (NILM) extracts appliance-level energy consumption from aggregate power measurements and is essential for smart metering systems. However, Transformer-based methods such as NILMFormer suffer from high computational complexity and distribution shifts across households, limiting edge deployme...
Non-intrusive load monitoring (NILM) estimates appliance-level consumption from a whole-home meter, but appliance-specific models and fixed output inventories make coverage costly to extend. We present FM4NILM (Foundation Model for NILM), a single prompt-programmable model that estimates a requested appliance's power t...
Accurate forecasting of appliance-level power consumption is essential for advancing energy efficiency in smart grids and improving load management systems. However, simultaneous appliance operation, diverse usage patterns, and limited access to appliance-level data introduce significant challenges for effective load f...
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