Summary of A Survey on Semantic Modeling For Building Energy Management, by Miracle Aniakor et al.
A Survey on Semantic Modeling for Building Energy Management
by Miracle Aniakor, Vinicius V. Cogo, Pedro M. Ferreira
First submitted to arxiv on: 17 Apr 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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 This paper surveys leading semantic modeling techniques used for energy management in buildings. The authors focus on reducing buildings’ energy consumption by leveraging data from building systems and environments. However, differences in device representations create challenges for semantic interoperability and scalable applications. The survey explores prominent models, their limitations, and use cases, providing insights for researchers to choose the most suitable approaches. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Buildings are a big user of energy worldwide. To make buildings more energy-efficient, you need data from building systems and the environment. But different devices from various manufacturers store this data in unique ways, making it hard to share or use. This paper looks at the top methods for using semantic models to manage energy in buildings. It also shows how these models can be applied in real-life scenarios, highlighting their strengths and weaknesses. |
