Non-Intrusive Load Monitoring Approaches for Disaggregated Energy Sensing: A Survey

By Ahmed Zoha, Alexander D. Gluhak, Muhammad Ali Imran, Sutharshan Rajasegarar. Italian National Conference on Sensors, 2012

963 citations recorded by the source corpus.

Appliance Load Monitoring (ALM) is essential for energy management solutions, allowing them to obtain appliance-specific energy consumption statistics that can further be used to devise load scheduling strategies for optimal energy utilization. Fine-grained energy monitoring can be achieved by deploying smart power outlets on every device of interest; however it incurs extra hardware cost and installation complexity. Non-Intrusive Load Monitoring (NILM) is an attractive method for energy disaggregation, as it can discern devices from the aggregated data acquired from a single point of measurement. This paper provides a comprehensive overview of NILM system and its associated methods and techniques used for disaggregated energy sensing. We review the state-of-the art load signatures and disaggregation algorithms used for appliance recognition and highlight challenges and future research directions.

Subfields: Computer Vision and Pattern Recognition, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/73cedf832de5e71fac9a5a8581015a3bdcbda68c

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

In Electrical and Electronic Engineering

Citations in the corpus

Big IoT data mining for real-time energy disaggregation in buildings (2016, background): “A more comprehensive discussion about these can be found in recent reviews, such as [15]–[17].”
Artificial intelligence and machine learning approaches to energy demand-side response: A systematic review (2020, background): “which is ‘invisible” to the individual energy user [100,253,254]), are key for assuring user-friendly friendly demand-side response, especially in residential settings — potentially enabling faster consumer adoption of demand response programmes.”
The ECO data set and the performance of non-intrusive load monitoring algorithms (2014, methodology): “Performing a performance evaluation of a NILM algorithm is difficult, because there is no standard evaluation procedure to apply [33].”
Revealing Household Characteristics from Smart Meter Data (2014, background): “This information allows in turn to provide detailed consumption feedback to the households [14, 15, 16].”