Summary of Benchmarking Active Learning For Nilm, by Dhruv Patel et al.
Benchmarking Active Learning for NILM
by Dhruv Patel, Ankita Kumari Jain, Haikoo Khandor, Xhitij Choudhary, Nipun Batra
First submitted to arxiv on: 24 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed work focuses on improving non-intrusive load monitoring (NILM) by developing an active learning approach that selectively collects appliance-level data from a limited number of households. The method utilizes uncertainty-aware neural networks for NILM and installs sensors in homes where disaggregation uncertainty is highest. The approach is benchmarked on the Pecan Street Dataport dataset, demonstrating significant performance gains over a standard random baseline and comparable accuracy to models trained on the entire dataset. The proposed method achieves up to a 2x reduction in disaggregation errors for a fixed number of sensors. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper proposes an innovative way to improve household power consumption monitoring. It uses special computers called neural networks that can learn from data. However, collecting this data is expensive and time-consuming. To solve this problem, the researchers developed a method to choose which homes to install special devices in. These devices measure how much energy different appliances use. By choosing the right homes, they can collect more useful information with less effort. The results show that their approach works well and reduces errors by up to 2 times. |
Keywords
* Artificial intelligence * Active learning
