Summary of A Pid-controlled Non-negative Tensor Factorization Model For Analyzing Missing Data in Nilm, by Dengyu Shi
A PID-Controlled Non-Negative Tensor Factorization Model for Analyzing Missing Data in NILM
by DengYu Shi
First submitted to arxiv on: 9 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| 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 Proportional-Integral-Derivative (PID) Controlled Non-Negative Latent Factorization of Tensor (PNLF) model addresses the challenges of Non-Intrusive Load Monitoring (NILM) datasets suffering from sensor failures, data loss, and sparse data. The PNLF model dynamically adjusts parameter gradients to improve convergence, stability, and accuracy, significantly outperforming state-of-the-art tensor completion models in both accuracy and efficiency. This study enhances load disaggregation precision and optimizes energy management by providing reliable data support for smart grid applications and policy formulation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way to analyze how much energy different appliances use at home or in a business is called Non-Intrusive Load Monitoring (NILM). It helps people understand their energy usage patterns, save energy, and detect any problems. But the data used in NILM often has gaps or missing information, which makes it hard to make accurate predictions. To fix this problem, researchers created a new model that can fill in these gaps more accurately than other models. This study shows that this new model works better than others and will help people manage their energy usage more effectively. |
Keywords
* Artificial intelligence * Precision
