Recent trends of smart nonintrusive load monitoring in buildings: A review, open challenges, and future directions

Smart nonintrusive load monitoring (NILM) represents a cost-efficient technology for observing power usage in buildings. It tackles several challenges in transitioning into a more effective, sustainable, and digital energy efficiency environment. This paper presents a comprehensive review of recent trends in the NILM field, in which we propose a multiperspective classification of existing smart NILM techniques. More attention is devoted to describing the contributions of deep learning, feature extraction, computing platforms, and application scenarios for NILM development. Accordingly, NILM technical aspects are first investigated, including data collection devices and public data sets. Next, event-based and non-event-based NILM algorithms are overviewed. Furthermore, potential limitations of existing solutions are identified, highlighting their technical challenges, especially those related to security and privacy preservation, data scarcity, results reproduction, and implementation and business difficulties. Lastly, future directions are explored to overcome the identified limitations.

Automated summary

This paper provides a comprehensive review of recent trends in smart nonintrusive load monitoring (NILM) and proposes a multiperspective classification of existing techniques. Attention is given to the contributions of deep learning, feature extraction, computing platforms, and application scenarios for NILM development. Technical aspects, including data collection devices and public data sets, are investigated. Event-based and non-event-based NILM algorithms are overviewed. Potential limitations of existing solutions, such as security and privacy preservation issues, data scarcity, and implementation challenges, are identified. Future directions to overcome these limitations are explored.