Research towards energy optimization in buildings heavily relies on building-related data such as measured indoor climate factors. While data collection is a labor- and cost-intensive task, simulations are a cheap alternative to generate datasets of arbitrary sizes, particularly useful for data-intensive deep learning methods. In this paper, we present the tool EnergyPlus Room Simulator, which enables the simulation of indoor climate in a specific room of a building using the simulation software EnergyPlus. It allows to alter room models and simulate various factors such as temperature, humidity, and CO2 concentration. In contrast to manually working with EnergyPlus, this tool enhances the simulation process by offering a convenient interface, including a user-friendly graphical user interface (GUI) as well as a REST API. The tool is intended to support scientific, building-related tasks such as occupancy detection on a room level by facilitating fast access to simulation data that may, for instance, be used for pre-training machine learning models.
@article{arxiv.2410.19888,
title = {EnergyPlus Room Simulator},
author = {Manuel Weber and Philipp Bogdain and Sophia Viktoria Weißenberger and Diana Marjanovic and Katharina Sammet and Jan Vellmer and Farzan Banihashemi and Peter Mandl},
journal= {arXiv preprint arXiv:2410.19888},
year = {2024}
}
Comments
Presented at BuildSim Nordic 2024. The conference was held from June 9 to 11, 2024, in Espoo, Finland