Home
This Title All WIREs
WIREs RSS Feed
How to cite this WIREs title:
WIREs Data Mining Knowl Discov
Impact Factor: 2.541

Performance evaluation in non‐intrusive load monitoring: Datasets, metrics, and tools—A review

Full article on Wiley Online Library:   HTML PDF

Can't access this content? Tell your librarian.

Non‐intrusive load monitoring (also known as NILM or energy disaggregation) is the process of estimating the energy consumption of individual appliances from electric power measurements taken at a limited number of locations in the electric distribution of a building. This approach reduces sensing infrastructure costs by relying on machine learning techniques to monitor electric loads. However, the ability to evaluate and benchmark the proposed approaches across different datasets is key for enabling the generalization of research findings and consequently contributes to the large‐scale adoption of this technology. Still, only recently researchers have focused on creating and standardizing the existing datasets in order to deliver a single interface to run NILM evaluations. Furthermore, there is still no consensus regarding, which performance metrics should be used to measure and report the performance of NILM systems and their underlying algorithms. This paper provides a review of the main datasets, metrics, and tools for evaluating the performance of NILM systems and technologies. Specifically, we review three main topics: (a) publicly available datasets, (b) performance metrics, and (c) frameworks and toolkits. The review suggests future research directions in NILM systems and technologies, including cross‐datasets, performance metrics for evaluation and generalizable frameworks for benchmarking NILM technology. This article is categorized under: Application Areas > Science and Technology Application Areas > Data Mining Software Tools Technologies > Computational Intelligence Technologies > Machine Learning
Example of event‐based energy disaggregation (Reprinted with permission from Hart (). Copyright 1992 IEEE)
[ Normal View | Magnified View ]
Example of event‐less energy disaggregation (Reprinted with permission from Hart (). Copyright 1992 IEEE)
[ Normal View | Magnified View ]

Browse by Topic

Technologies > Machine Learning
Technologies > Computational Intelligence
Application Areas > Data Mining Software Tools
Application Areas > Science and Technology

Access to this WIREs title is by subscription only.

Recommend to Your
Librarian Now!

The latest WIREs articles in your inbox

Sign Up for Article Alerts