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About Learning Techniques For NILMTK
The document discusses techniques for non-intrusive load monitoring (NILM) to disaggregate total home energy usage into appliance-level usage. It describes several machine learning algorithms that can be used for NILM, including supervised algorithms like random forest regression and decision tree regression, and unsupervised algorithms like K-means clustering and support vector machines. It also discusses deep learning approaches like LSTM and autoencoders. The techniques can be categorized as event-based or non-event-based, and as supervised or unsupervised depending on their data and training requirements.
Detailed Information
| Author: | ['UMAR'] |
|---|---|
| Publication Year: | 2021 |
| Pages: | 9 |
| Language: | English |
| Format: | |
| Price: | FREE |
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