Summary of Energyplus Room Simulator, by Manuel Weber et al.
EnergyPlus Room Simulator
by Manuel Weber, Philipp Bogdain, Sophia Viktoria Weißenberger, Diana Marjanovic, Katharina Sammet, Jan Vellmer, Farzan Banihashemi, Peter Mandl
First submitted to arxiv on: 25 Oct 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 A novel deep learning method utilizes simulated indoor climate data from EnergyPlus Room Simulator to optimize energy consumption in buildings. The simulator, built upon EnergyPlus software, enables the generation of large datasets with precise control over factors like temperature, humidity, and CO2 concentration. This tool streamlines simulation processes through a user-friendly graphical interface (GUI) and REST API, making it suitable for pre-training machine learning models in scientific building-related tasks such as occupancy detection. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A team developed a helpful tool to make it easier to create indoor climate simulations for buildings. They used EnergyPlus software to make a simulator that can generate lots of data about things like temperature, humidity, and air quality. This simulator is super easy to use with both a visual interface (like a game) and instructions you can give a computer (like API). The purpose of this tool is to help scientists learn more about how people use buildings by quickly giving them the data they need. |
Keywords
» Artificial intelligence » Deep learning » Machine learning » Temperature
