Skip to main content

DigiTwin CO2 Sample Project

Reproducible code and data for this publication Digital Twin applications using the SIMULTAN data model and Python

This repository contains the three packages CO2_Prediction, MonitoringFaker and SimultanCO2Prediction to calculate the trend of the CO2-concentration in a zone ventilated by windows with real-time data.

CO2_Prediction:

Classes and methods to calculate the CO2 concentration trend for a zone were implemented. The calculation of the trend is done with a simple analytical model, where the air flow rate is calculated as a function of the opening area and the temperature difference between the inside and outside according to OENORM B 8110-3. With the calculated air volume flow, the CO2-concentration inside and outside and the CO2-emission in the zone, the trend of the CO2-concentration can be calculated for constant boundary conditions. In addition, classes for a database and sensors were implemented using SQLalchemy, which can read and write the latest measured value of a sensor in a database. The measurements are then used as a boundary condition for the calculation of the CO2 concentration.

MonitoringFaker

Generate measurement values for sensors, which initializes the databases for the sensors in the imported project and writes artificially generated measurement values to the databases.

SimultanCO2Prediction

Package, which integrates the SIMULTAN data-model in the CO2_Prediction-package.

Installation

Install via pip:

pip install https://github.com/DerMaxxiKing/DigiTwin_CO2_SampleProject

This installs the packages CO2_Prediction, MonitoringFaker and SimultanCO2Prediction

Usage:

Run fake measurement generation:

Run in cmd:

run_measurement_generator

Run CO2 trend calculation:

Run in cmd:

run_co2_prediction

SIMULTAN model

The SIMULTAN model can be found here:

Resources
├── database_test.simultan

Databases

The database with the measurements can be found here:

Resources
├── measurements.db

The database with the weather data can be found here:

Resources
├── weather_database.db

Metadata

Release files for DigiTwin-CO2-SampleProject 1

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for DigiTwin-CO2-SampleProject 1
File Size Uploaded Provenance
DigiTwin_CO2_SampleProject-1.tar.gz 13.7 kB Without provenance Details

Built distribution (wheel)

Table of built distributions (wheels) for DigiTwin-CO2-SampleProject 1
File Interpreter ABI Platform
DigiTwin_CO2_SampleProject-1-py3-none-any.whl Python 3 none any Details

Total release size: 31.4 kB

Release files / DigiTwin_CO2_SampleProject-1.tar.gz

Download URL DigiTwin_CO2_SampleProject-1.tar.gz
Size 13.7 kB
Tags Source
SHA-256 checksum
How to use checksums
def4a574a516015a6909ea601b15d34c48838ed259c214944bc7c233a16d6c26
BLAKE2b-256 checksum
How to use checksums
5b41a78f32b8e247cdbe47271de8af7b81c4f4eeeaaf7890a8004ef2865284f7
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No
Uploaded via twine/4.0.1 CPython/3.9.10

Release files / DigiTwin_CO2_SampleProject-1-py3-none-any.whl

Download URL DigiTwin_CO2_SampleProject-1-py3-none-any.whl
Size 17.8 kB
Tags Python 3
SHA-256 checksum
How to use checksums
a1c4ca7f7513e6fb0c76522aa2e9b6a99455d1b35b57dd408a4f0131c85eb02c
BLAKE2b-256 checksum
How to use checksums
4af81bf7ce7fe261d572f5b603437070de93c37587798fd8ec6fc0a1332a0afd
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No
Uploaded via twine/4.0.1 CPython/3.9.10

Security for DigiTwin-CO2-SampleProject 1

Provenance

Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.

No provenance found

None of the files in this release were uploaded with provenance metadata.

A note on package security

PyPI is a repository for distributing packages, not a service for auditing them. While third-party security researchers and our staff do their best to detect and remove malicious packages, PyPI cannot guarantee the quality, security or suitability of any given package.

Release history Release notifications | RSS feed

This release

1 This release

2 release files No provenance

Anthropic, PBC Visionary sponsor Bloomberg Visionary sponsor Hudson River Trading Visionary sponsor Meta Visionary sponsor NVIDIA Visionary sponsor Microsoft Sustainability sponsor Depot Continuous Integration AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Sentry Error logging StatusPage Status page