Name
Abstract | ||
|---|---|---|
Recently, there is an alarming increase in world energy consumption which would have a significant impact on future energy resources. It is now evident to explore multiple options of sustainable use of energy management and provide appropriate and efficient means energy management that would benefit our consumers at a residential scale. The proposed energy management policy is based on three kinds of load: continuous load, intermittent load and phantom load. Such loads were recognized from various data collection devices and smart plugs. In this paper, the load classification and an approach to recognize the load with machine learning systems like k-NN and compare it with other classifiers are described. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/SAS.2019.8706089 | 2019 IEEE Sensors Applications Symposium (SAS) |
Keywords | Field | DocType |
Home appliances,Energy management,Machine learning,Machine learning algorithms,Smart homes,Classification algorithms,Plugs | Data collection,Energy management,Standby power,Computer science,Home automation,Real-time computing,World energy consumption,Statistical classification,Energy resources,Sustainability | Conference |
ISBN | Citations | PageRank |
978-1-5386-7713-1 | 0 | 0.34 |
References | Authors | |
0 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Win Thandar Soe | 1 | 0 | 0.34 |
| Cécile Belleudy | 2 | 84 | 12.98 |