A critical review of state-of-the-art non-intrusive load monitoring datasets

Nowadays Non-Intrusive Load Monitoring (NILM) is considered a hot topic among researchers. The energy disaggregation datasets are used as the benchmark to validate the performance of energy disaggregation algorithms. It is indeed rather difficult to record the load monitoring of devices and appliances; therefore various benchmarking datasets have been proposed during the past few years. This paper presentsa comprehensive review of 42 NILM datasets aided by comparison tables, generated to elaborate on the diverse features of existing datasets. Moreover, the strengths and limitations of present NILM datasets are highlighted with an outlook on present challenges and future research directions as a contribution to the field of energy disaggregation and load identification. The review will help the researchers to evaluate the performance of new NILM algorithms. We believe that this work could be served as a guideline and can potentially open new research perspectives to the scientific community working on developing new NILM datasets. ABSTRACT Nowadays Non-Intrusive Load Monitoring (NILM) is considered a hot topic among researchers. The energy disaggregation datasets are used as the benchmark to validate the performance of energy disaggregation algorithms. It is indeed rather difficult to record the load monitoring of devices and appliances; therefore various benchmarking datasets have been proposed during the past few years. This paper presentsa comprehensive review of 42 NILM datasets aided by comparison tables, generated to elaborate on the diverse features of existing datasets. Moreover, the strengths and limitations of present NILM datasets are highlighted with an outlook on present challenges and future research directions as a contribution to the field of energy disaggregation and load identification. The review will help the researchers to evaluate the performance of new NILM algorithms. We believe that this work could be served as a guideline and can potentially open new research perspectives to the scientific community working on developing new NILM datasets.

Automated summary

NILM is a hot topic among researchers, with energy disaggregation datasets used as benchmarks for algorithm validation. This paper provides a comprehensive review of 42 NILM datasets, highlighting strengths, limitations, and future research directions.