Name
Title | ||
|---|---|---|
Machine learning approaches for non-intrusive load monitoring: from qualitative to quantitative comparation |
Abstract | ||
|---|---|---|
Non-intrusive load monitoring (NILM) is the prevailing method used to monitor the energy profile of a domestic building and disaggregate the total power consumption into consumption signals by appliance. Whilst the most popular disaggregation algorithms are based on Hidden Markov Model solutions based on deep neural networks have attracted interest from researchers. The objective of this paper is to provide a comprehensive overview of the NILM method and present a comparative review of modern approaches. In this effort, many obstacles are identified. The plethora of metrics, the variety of datasets and the diversity of methodologies make an objective comparison almost impossible. An extensive analysis is made in order to scrutinize these problems. Possible solutions and improvements are suggested, while future research directions are discussed. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1007/s10462-018-9613-7 | Artificial Intelligence Review |
Keywords | Field | DocType |
Non-intrusive load monitoring (NILM),Power disaggregation algorithms,Hidden Markov Model,Deep learning | Data mining,Computer science,Artificial intelligence,Deep learning,Hidden Markov model,Deep neural networks,Machine learning,Power consumption,Information and Computer Science | Journal |
Volume | Issue | ISSN |
52.0 | 1.0 | 1573-7462 |
Citations | PageRank | References |
2 | 0.38 | 26 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Christoforos Nalmpantis | 1 | 3 | 2.43 |
| Dimitris Vrakas | 2 | 251 | 23.98 |