Summary of Improving Building Temperature Forecasting: a Data-driven Approach with System Scenario Clustering, by Dafang Zhao et al.
Improving Building Temperature Forecasting: A Data-driven Approach with System Scenario Clustering
by Dafang Zhao, Zheng Chen, Zhengmao Li, Xiaolei Yuan, Ittetsu Taniguchi
First submitted to arxiv on: 21 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Signal Processing (eess.SP)
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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 data-driven room temperature prediction model, based on the k-means clustering method, aims to improve control systems with prediction capabilities for smart energy management in buildings. By extracting system operation features through historical data analysis and simplifying the system-level model, the approach enhances generalization and computational efficiency. The results demonstrate that this approach can significantly reduce modeling time without compromising prediction accuracy. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper introduces a new way to predict room temperatures using historical data and machine learning techniques. This helps buildings use less energy for heating and cooling, which is important because it uses about 40% of the total energy used in buildings. The method works by looking at patterns in how buildings are used and simplifying complex models to make predictions faster and more accurate. |
Keywords
* Artificial intelligence * Clustering * Generalization * K means * Machine learning * Temperature
