twin4build: A python package for Data-driven and Ontology-based modeling and simulation of buildings

twin4build is a python package which aims to provide a flexible and automated framework for dynamic modelling of indoor climate and energy consumption in buildings. It leverages the SAREF core ontology and its extensions SAREF4BLDG and SAREF4SYST.

This is a work-in-progress library and the functionality is therefore updated regularly. More information on the use of the framework and code examples are coming in the near future!

Installation

Python version	Windows	Ubuntu
3.8
3.9
3.10
3.11

The package can be installed with pip and git using one of the above python versions:

python -m pip install git+https://github.com/JBjoernskov/Twin4Build

Graphviz

Graphviz must be installed separately:

Ubuntu

sudo add-apt-repository universe
sudo apt update
sudo apt install graphviz

Windows

On windows, the winget or choco package managers can be used:

winget install graphviz

choco install graphviz

MacOS

brew install graphviz

Getting started

Below is an example of a system with two components, a schedule and a damper. In this example the simulation model (components and connections) is defined manually for instructional purposes. However, the simulation model can also be built automatically from a configuration file.

import twin4build as tb
import twin4build.utils.plot.plot as plot


def fcn(self):
    ##############################################################
    ################## First, define components ##################
    ##############################################################

    #Define a schedule for the damper position
    position_schedule = tb.ScheduleSystem(
            weekDayRulesetDict = {
                "ruleset_default_value": 0,
                "ruleset_start_minute": [0,0,0,0,0,0,0],
                "ruleset_end_minute": [0,0,0,0,0,0,0],
                "ruleset_start_hour": [6,7,8,12,14,16,18],
                "ruleset_end_hour": [7,8,12,14,16,18,22],
                "ruleset_value": [0,0.1,1,0,0,0.5,0.7]}, #35
            add_noise=False,
            saveSimulationResult = self.saveSimulationResult,
            id="Position schedule")

    # Define damper component
    damper = tb.DamperSystem(
        nominalAirFlowRate = Measurement(hasValue=1.6),
        a=5,
        saveSimulationResult=self.saveSimulationResult,
        id="Damper")

    #################################################################
    ################## Add connections to the model #################
    #################################################################
    self.add_connection(position_schedule, damper, 
                        "scheduleValue", "damperPosition")

    # Cycles are not allowed (with the exeption of controllers - see the controller example). If the following line is commented in, 
    # a cycle is introduced and the model will generate an error when "model.get_execution_order()" is run". 
    # You can see the generated graph with the cycle in the "system_graph.png" file.
    # self.add_connection(damper, damper, "airFlowRate", "damperPosition") #<------------------- comment in to create a cycle


model = tb.Model(id="example_model", saveSimulationResult=True)
model.load_model(infer_connections=False, fcn=fcn)

# Create a simulator instance
simulator = tb.Simulator()

# Simulate the model
stepSize = 600 #Seconds
startTime = datetime.datetime(year=2021, month=1, day=10, hour=0, minute=0, second=0)
endTime = datetime.datetime(year=2021, month=1, day=12, hour=0, minute=0, second=0)
simulator.simulate(model,
                    stepSize=stepSize,
                    startTime=startTime,
                    endTime=endTime)

plot.plot_damper(model, simulator, "Damper", show=False) #Set show=True to plot

Documentation

The core modules of this package are currently:

model.py: Contains the Model class, which represents the simulation model of the building.

simulator.py: Contains the Simulator class, which can simulate a Model instance for a given period.

monitor.py: Contains the Monitor class, which can monitor and evaluate the performance of a building for a certain period by comparing readings from virtual measuring devices with readings from physical measuring devices.

evaluator.py: Contains the Evaluator class, which can evaluate and compare Model instances on different metrics, e.g. energy consumption and indoor comfort.

Model and Simulator

An example scipt showing the use of the Model class and how to simulate a Model instance is given in test_model.py.

Monitor

This example script demonstrates the use of the Monitor class.

Running this example generates the following figures, which compares physical with virtual sensor and meter readings on different components. The red line indicates the timestamp where operation of the physical system was drastically changed. A binary classification signal is also generated for each component which informs whether a component performs as expected (0) or not (1).

Evaluator

This example script demonstrates the use of the Evaluator class. Running this example generates the following figures, which compares two different scenarios.

Accessing time series data for running examples

This folder contains the necessary files for running some of the examples. It is password protected - contact JBjoernskov for password. Download the folder and paste the content into twin4build/test/data/time_series_data.

Publications

• Bjørnskov, J., & Jradi, M. (2023). An Ontology-Based Innovative Energy Modeling Framework for Scalable and Adaptable Building Digital Twins. Energy and Buildings, 292, [113146].

• Bjørnskov, J., & Jradi, M. (Accepted/In press). Implementation and demonstration of an automated energy modeling framework for scalable and adaptable building digital twins based on the SAREF ontology. Building Simulation.

• 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},
}