Summary of Model Failure or Data Corruption? Exploring Inconsistencies in Building Energy Ratings with Self-supervised Contrastive Learning, by Qian Xiao et al.
Model Failure or Data Corruption? Exploring Inconsistencies in Building Energy Ratings with Self-Supervised Contrastive Learning
by Qian Xiao, Dan Liu, Kevin Credit
First submitted to arxiv on: 14 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 paper presents a new approach called CLEAR that aims to improve the accuracy of Building Energy Ratings (BERs) by identifying inconsistencies in existing measurements. The authors highlight the importance of reliable BERs in reducing carbon emissions and promoting climate improvement, but note that current assessment processes are prone to errors. They introduce a self-supervised contrastive learning method, which is validated using a dataset representing Irish building stocks. The results show evidence of inconsistent BER assessments, suggesting that measurement data corruption is a significant issue. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The study introduces CLEAR, an approach that helps make Building Energy Ratings (BERs) more accurate. BERs are important because they help us understand how buildings can be made more energy-efficient, which is good for the environment. But right now, there’s a problem: some measurements aren’t accurate or complete. This makes it hard to know how buildings really are performing when it comes to energy use. The researchers used a big dataset of building information from Ireland and found that even in this real-world data, some measurements didn’t match up. This means we need better ways to figure out what’s going on with our building energy ratings. |
Keywords
» Artificial intelligence » Self supervised
