Summary of Large Language Model-based Interpretable Machine Learning Control in Building Energy Systems, by Liang Zhang et al.
Large Language Model-Based Interpretable Machine Learning Control in Building Energy Systems
by Liang Zhang, Zhelun Chen
First submitted to arxiv on: 14 Feb 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Human-Computer Interaction (cs.HC)
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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 Interpretable Machine Learning (IML) framework combines Shapley values and Large Language Models (LLMs) to enhance transparency and understanding of Machine Learning Control (MLC) models. This innovation aims to improve the credibility of MLC-based decision-making in HVAC systems, which is currently hindered by opaque nature and inference mechanisms. The developed framework packages insights into a coherent narrative, demonstrating its feasibility for model predictive control-based precooling under demand response events in a virtual testbed. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper explores how Machine Learning can improve the way we control heating, ventilation, and air conditioning (HVAC) systems. Right now, it’s hard to understand why certain decisions are made by these computer models. To fix this, scientists created a new way of doing machine learning that makes the process more transparent. They combined two powerful tools: Shapley values and Large Language Models. This combination helps us see how different factors contribute to a decision, making the whole process more trustworthy. |
Keywords
* Artificial intelligence * Inference * Machine learning
