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Systems","sub_category":"Building Energy Monitoring","monthly_downloads":22,"total_dependent_repos":0,"total_dependent_packages":0,"readme":"\n\u003cp align=\"center\"\u003e\n    \u003cimg src=\"https://raw.githubusercontent.com/JBjoernskov/Twin4Build/main/Twin4build_logo.jpg\" width=\"400\"\u003e\n\u003c/p\u003e\n\n[![docs](https://app.readthedocs.org/projects/twin4build/badge/?version=latest)](https://twin4build.readthedocs.io/en/latest/)\n\n\n# twin4build: A python package for Data-driven and Ontology-based modeling and simulation of buildings\n\nDynamic modeling and simulation of buildings, featuring fully differentiable models for parameter estimation and optimal control. Supports integration of semantic models for automatic model generation and fast implementation. \n\n\n## Core Classes and Functionality\n\nTwin4Build provides several top-level classes for building, simulating, translating, calibrating, and optimizing building energy models:\n\n- **Model**:  \n  The main container for your building system, components, and their connections. Use this class to assemble your digital twin from reusable components. \n\n- **Simulator**:  \n  Runs time-based simulations of your Model, producing time series outputs for all components. Handles the simulation loop and time stepping.\n\n- **Translator**:  \n  Automatically generates a Model from a semantic model (ontology-based building description) and maintains a link between these. Enables ontology-driven, automated model creation.\n\n- **Estimator**:  \n  Performs parameter estimation (calibration) for your Model using measured data. Supports both least-squares and PyTorch-based optimization.\n\n- **Optimizer**:  \n  Optimizes building operation by adjusting setpoints or control variables to minimize objectives or satisfy constraints, using gradient-based methods.\n\n\n\nAll classes are accessible via the main package import:\n```python\nimport twin4build as tb\n```\n\nA typical workflow would look like this:\n\u003cp align=\"center\"\u003e\n    \u003cimg src=\"https://raw.githubusercontent.com/JBjoernskov/Twin4Build/main/docs/source/_static/t4b_workflow.png\" width=\"800\"\u003e\n\u003c/p\u003e\n\n\n## Examples and Tutorials\nBelow are some examples of how to use the package.\nMore examples are coming soon.\n\n### Basics of Twin4Build\n+ \u003ca target=\"_blank\" href=\"https://colab.research.google.com/github/JBjoernskov/Twin4Build/blob/main/twin4build/examples/minimal_example.ipynb\"\u003e\n  \u003cimg src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/\u003e\u003c/a\u003e Part 1: Connecting components, simulating a model, and visualization\n\n+ \u003ca target=\"_blank\" href=\"https://colab.research.google.com/github/JBjoernskov/Twin4Build/blob/main/twin4build/examples/space_co2_controller_example.ipynb\"\u003e\n  \u003cimg src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/\u003e\u003c/a\u003e Part 2: Modeling and control of indoor CO2 concentration\n\n### Translator\n\n+ \u003ca target=\"_blank\" href=\"https://colab.research.google.com/github/JBjoernskov/Twin4Build/blob/main/twin4build/examples/translator_example.ipynb\"\u003e\n  \u003cimg src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/\u003e\u003c/a\u003e Part 1: How to use the translator to generate simulation models from semantic models.\n\n\n### Estimator\n\n+ \u003ca target=\"_blank\" href=\"https://colab.research.google.com/github/JBjoernskov/Twin4Build/blob/main/twin4build/examples/estimator_example.ipynb\"\u003e\n  \u003cimg src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/\u003e\u003c/a\u003e Part 1: Basic parameter estimation and calibration\n\n\n### Optimizer\n\n+ \u003ca target=\"_blank\" href=\"https://colab.research.google.com/github/JBjoernskov/Twin4Build/blob/main/twin4build/examples/optimizer_example.ipynb\"\u003e\n  \u003cimg src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/\u003e\u003c/a\u003e Part 1: Optimization of space heater power consumption, constrained by heating and cooling setpoints.\n\n\u003c!-- ### Neural Policy Controller\n\n+ \u003ca target=\"_blank\" href=\"https://colab.research.google.com/github/JBjoernskov/Twin4Build/blob/main/twin4build/examples/neural_policy_controller_example/neural_policy_example.ipynb\"\u003e\n  \u003cimg src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/\u003e\u003c/a\u003e Part 1: Training a neural policy controller for the space model --\u003e\n\n## Documentation\nThe documentation can be found [online](https://twin4build.readthedocs.io/en/latest/index.html).\nBelow is a code snippet showing the basic functionality of the package.\n```python \nimport twin4build as tb\n\n# Create a model\nmodel = tb.Model(id=\"example_model\")\n\n# Define components\ndamper = tb.DamperTorchSystem(id=\"damper\")\nspace = tb.BuildingSpaceTorchSystem(id=\"space\")\n\n# Add connections to the model\nmodel.add_connection(damper, space, \n                    \"airFlowRate\", \"supplyAirFlowRate\")\n\n# Load the model\nmodel.load()\n\n# Create a simulator instance\nsimulator = tb.Simulator(model)\n\n# Simulate the model\nstep_size = 600 #Seconds\nstart_time = datetime.datetime(year=2025, month=1, day=10, hour=0, minute=0, second=0) # Optionally set the timezone\nend_time = datetime.datetime(year=2025, month=1, day=12, hour=0, minute=0, second=0) # Optionally set the timezone\nsimulator.simulate(step_size=step_size,\n                   start_time=start_time,\n                   end_time=end_time)\n\n# Plot the results\nplot.plot_component(simulator, \n                    components_1axis=[(\"Damper\", \"airFlowRate\")],\n                    components_2axis=[(\"Damper\", \"damperPosition\")],\n                    ylabel_1axis=\"Air flow rate\", #Optional\n                    ylabel_2axis=\"Damper position\", #Optional\n                    show=True,\n                    nticks=11)\n```\n\n## Installation\n\nThe package is installed with pip:\n\n```bat\npip install twin4build\n```\n\nThe following python versions are supported:\n\n| Python version  | Windows  | Ubuntu |\n| :------------ |---------------:| -----:|\n| 3.9 | [![windows-python3.9](https://github.com/JBjoernskov/Twin4Build/actions/workflows/win-py3-9.yml/badge.svg?branch=main)](https://github.com/JBjoernskov/Twin4Build/actions/workflows/win-py3-9.yml)        |    [![ubuntu-python3.9](https://github.com/JBjoernskov/Twin4Build/actions/workflows/ub-py3-9.yml/badge.svg?branch=main)](https://github.com/JBjoernskov/Twin4Build/actions/workflows/ub-py3-9.yml) |\n| 3.10 | [![windows-python3.10](https://github.com/JBjoernskov/Twin4Build/actions/workflows/win-py3-10.yml/badge.svg?branch=main)](https://github.com/JBjoernskov/Twin4Build/actions/workflows/win-py3-10.yml)        |    [![ubuntu-python3.10](https://github.com/JBjoernskov/Twin4Build/actions/workflows/ub-py3-10.yml/badge.svg?branch=main)](https://github.com/JBjoernskov/Twin4Build/actions/workflows/ub-py3-10.yml) |\n| 3.11 | [![windows-python3.11](https://github.com/JBjoernskov/Twin4Build/actions/workflows/win-py3-11.yml/badge.svg?branch=main)](https://github.com/JBjoernskov/Twin4Build/actions/workflows/win-py3-11.yml)        |    [![ubuntu-python3.11](https://github.com/JBjoernskov/Twin4Build/actions/workflows/ub-py3-11.yml/badge.svg?branch=main)](https://github.com/JBjoernskov/Twin4Build/actions/workflows/ub-py3-11.yml) |\n| 3.12 | [![windows-python3.12](https://github.com/JBjoernskov/Twin4Build/actions/workflows/win-py3-12.yml/badge.svg?branch=main)](https://github.com/JBjoernskov/Twin4Build/actions/workflows/win-py3-12.yml)        |    [![ubuntu-python3.12](https://github.com/JBjoernskov/Twin4Build/actions/workflows/ub-py3-12.yml/badge.svg?branch=main)](https://github.com/JBjoernskov/Twin4Build/actions/workflows/ub-py3-12.yml) |\n\n\n\n\n### Graphviz (recomended)\n\nTo utilize the graph-drawing capabilities of twin4build, the drawing engine [Graphviz](https://graphviz.org/download) must be installed.\nIt can be installed by downloading the install-file from the official website or by using your favorite package manager: \n\n#### Ubuntu\n```bat\nsudo add-apt-repository universe\nsudo apt update\nsudo apt install graphviz\n```\n\n#### Windows\nOn windows, the winget or choco package managers can be used:\n```bat\nwinget install graphviz\n```\n```bat\nchoco install graphviz\n```\n\n#### MacOS\n```bat\nbrew install graphviz\n```\n\n\n## Publications\n\u003ca id=\"1\"\u003e[1]\u003c/a\u003e \n[Bjørnskov, J. \u0026 Thomsen, A. \u0026 Jradi, M. (2025). Large-scale field demonstration of an interoperable and ontology-based energy modeling framework for building digital twins. Applied Energy, 387, [125597]](https://doi.org/10.1016/j.apenergy.2025.125597)\n\n\u003ca id=\"2\"\u003e[2]\u003c/a\u003e \n[Bjørnskov, J. \u0026 Jradi, M. \u0026 Wetter, M. (2025). Automated Model Generation and Parameter Estimation of Building Energy Models Using an Ontology-Based Framework. Energy and Buildings 329, [115228]](https://doi.org/10.1016/j.enbuild.2024.115228)\n\n\u003ca id=\"3\"\u003e[3]\u003c/a\u003e \n[Bjørnskov, J. \u0026 Jradi, M. (2023). An Ontology-Based Innovative Energy Modeling Framework for Scalable and Adaptable Building Digital Twins. Energy and Buildings, 292, [113146].](https://doi.org/10.1016/j.enbuild.2023.113146)\n\n\u003ca id=\"3\"\u003e[4]\u003c/a\u003e \n[Bjørnskov, J., Badhwar, A., Singh, D., Sehgal, M., Åkesson, R., \u0026 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.](https://doi.org/10.1080/19401493.2025.2504005)\n\n\u003ca id=\"4\"\u003e[5]\u003c/a\u003e \n[Bjørnskov, J. \u0026 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.](https://portal.findresearcher.sdu.dk/en/publications/implementation-and-demonstration-of-an-automated-energy-modeling-)\n\n\u003ca id=\"5\"\u003e[6]\u003c/a\u003e \n[Andersen, A. H. \u0026 Bjørnskov, J. \u0026 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.](https://portal.findresearcher.sdu.dk/en/publications/adaptable-and-scalable-energy-modeling-of-ventilation-systems-as-)\n\n\n\n\n\n\n## Cite as\n```bibtex\n@article{OntologyBasedBuildingModelingFramework,\n    title = {An ontology-based innovative energy modeling framework for scalable and adaptable building digital twins},\n    journal = {Energy and Buildings},\n    volume = {292},\n    pages = {113146},\n    year = {2023},\n    issn = {0378-7788},\n    doi = {https://doi.org/10.1016/j.enbuild.2023.113146},\n    url = {https://www.sciencedirect.com/science/article/pii/S0378778823003766},\n    author = {Jakob Bjørnskov and Muhyiddine Jradi},\n    keywords = {Digital twin, Data-driven, Building energy model, Building simulation, Ontology, SAREF},\n}\n```\n\n","funding_links":[],"readme_doi_urls":["https://doi.org/10.1016/j.apenergy.2025.125597","https://doi.org/10.1016/j.enbuild.2024.115228","https://doi.org/10.1016/j.enbuild.2023.113146","https://doi.org/10.1080/19401493.2025.2504005"],"works":{},"citation_counts":{},"total_citations":0,"keywords_from_contributors":[],"project_url":"https://ost.ecosyste.ms/api/v1/projects/348040","html_url":"https://ost.ecosyste.ms/projects/348040"}