Welcome to Twin4Build’s documentation!
Overview
This documentation is organized into three main sections:
- Getting Started
Contains tutorials and installation instructions to help you begin using Twin4Build:
Installation - Instructions for installing Twin4Build and its dependencies
Examples and Tutorials - Step-by-step guides showing basic usage
- API Reference
Detailed documentation of all Twin4Build modules and their components.
- Developer Reference
Guide for developers who want to contribute to Twin4Build.
A typical workflow would look like this:
Below is a code snippet showing the basic functionality of the package.
import twin4build as tb
# Create a model
model = tb.Model(id="example_model")
# Define components
damper = tb.DamperTorchSystem(id="damper")
space = tb.BuildingSpaceTorchSystem(id="space")
# Add connections to the model
model.add_connection(damper, space,
"airFlowRate", "supplyAirFlowRate")
# Load the model
model.load()
# Create a simulator instance
simulator = tb.Simulator(model)
# Simulate the model
step_size = 600 #Seconds
start_time = datetime.datetime(year=2025, month=1, day=10, hour=0, minute=0, second=0) # Optionally set the timezone
end_time = datetime.datetime(year=2025, month=1, day=12, hour=0, minute=0, second=0) # Optionally set the timezone
simulator.simulate(step_size=step_size,
start_time=start_time,
end_time=end_time)
# Plot the results
plot.plot_component(simulator,
components_1axis=[("Damper", "airFlowRate")],
components_2axis=[("Damper", "damperPosition")],
ylabel_1axis="Air flow rate", #Optional
ylabel_2axis="Damper position", #Optional
show=True,
nticks=11)
[1] Bjørnskov, J. & Thomsen, A. & Jradi, M. (2025). Large-scale field demonstration of an interoperable and ontology-based energy modeling framework for building digital twins. Applied Energy, 387, [125597]
[2] Bjørnskov, J. & Jradi, M. & Wetter, M. (2025). Automated Model Generation and Parameter Estimation of Building Energy Models Using an Ontology-Based Framework. Energy and Buildings 329, [115228]
[3] Bjørnskov, J. & Jradi, M. (2023). An Ontology-Based Innovative Energy Modeling Framework for Scalable and Adaptable Building Digital Twins. Energy and Buildings, 292, [113146].
[4] Bjørnskov, J., Badhwar, A., Singh, D., Sehgal, M., Åkesson, R., & Jradi, M. (2025). Development and demonstration of a digital twin platform leveraging ontologies and data-driven simulation models. Journal of Building Performance Simulation, 1–13.
[5] Bjørnskov, J. & Jradi, M. (2023). Implementation and demonstration of an automated energy modeling framework for scalable and adaptable building digital twins based on the SAREF ontology. Building Simulation.
[6] Andersen, A. H. & Bjørnskov, J. & Jradi, M. (2023). Adaptable and Scalable Energy Modeling of Ventilation Systems as Part of Building Digital Twins. In Proceedings of the 18th International IBPSA Building Simulation Conference: BS2023 International Building Performance Simulation Association.
Cite as
@article{OntologyBasedBuildingModelingFramework,
title = {An ontology-based innovative energy modeling framework for scalable and adaptable building digital twins},
journal = {Energy and Buildings},
volume = {292},
pages = {113146},
year = {2023},
issn = {0378-7788},
doi = {https://doi.org/10.1016/j.enbuild.2023.113146},
url = {https://www.sciencedirect.com/science/article/pii/S0378778823003766},
author = {Jakob Bjørnskov and Muhyiddine Jradi},
keywords = {Digital twin, Data-driven, Building energy model, Building simulation, Ontology, SAREF},
}