APPLICATION OF MACHINE LEARNING TO NON-INTRUSIVE LOAD MONITORING SYSTEM
Crisis energy is a trouble that can happened, if people lifestyles nowadays don’t change. The necesarry change is a lifestyle in using electrical energy in houses. To change that lifestyle, humans need a good management electrical energy. To do management energy, the absolute condition that needed i...
Saved in:
| Main Author: | |
|---|---|
| Format: | Final Project |
| Language: | Indonesia |
| Subjects: | |
| Online Access: | https://digilib.itb.ac.id/gdl/view/41747 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Institution: | Institut Teknologi Bandung |
| Language: | Indonesia |
| Summary: | Crisis energy is a trouble that can happened, if people lifestyles nowadays don’t change. The necesarry change is a lifestyle in using electrical energy in houses. To change that lifestyle, humans need a good management electrical energy. To do management energy, the absolute condition that needed is an ability to read electrical energy consumption. To read that, there is a method called Non-Intrusive Load Monitoring (NILM). NILM method reads all the electrical energy consumption of all appliances from one point of measurement. To split the data into each appliances, it’s used energy disaggregation algorithm. Energy disaggregation algorithm is a complex, so it’s used Machine Learning as a subtitute to make it easier. But in real life, there are a lot of electrical appliances that have characteristic and need to be recognized before using energy disaggregation. For that, characteristic for each appliances are needed to be recognized first using Machine Learning. |
|---|