A simple tool for generating time series data related to energy systems and building operations.
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EnTiSe (En-ergy Ti-me Se-ries) is a Python package designed to generate realistic time series data for various energy systems and building operations. It provides a flexible, pipeline- and strategy-based approach to create time series for different applications including HVAC, domestic hot water, electricity, mobility, and occupancy patterns.
- Multiple time series types including DHW, HVAC, PV, and more in the works.
- Flexible pipeline- and strategy-based architecture for customizable time series generation.
- Support for dependent methods to create related time series.
- Two access patterns: batch processing via TimeSeriesGenerator or direct method imports for individual control.
To get started, follow these steps:
EnTiSe can be installed directly from PyPI:
pip install entiseFor faster installation, you can use uv:
pip install uv
uv pip install entiseFor development or the latest features, you can install directly from the repository:
pip install git+https://github.com/tum-ens/need/entise.gitOr clone the repository and install in development mode:
git clone https://github.com/tum-ens/need/entise.git
cd entise
pip install -e ".[dev]"Now you're ready to use EnTiSe! Check the examples directory for usage examples.
- entise/: Main project code organized in a Python package.
- constants/: Definitions of time series types and other constants.
- core/: Core functionality and base classes.
- data/: Data files and data handling utilities.
- methods/: Implementation of various time series generation methods.
- tests/: Folder for tests; structured by functionality.
- docs/: Documentation source files.
- examples/: Example scripts demonstrating usage of the package.
EnTiSe provides two flexible ways to generate time series data:
For generating multiple time series at once:
from entise.core.generator import TimeSeriesGenerator
# Initialize the generator
gen = TimeSeriesGenerator()
# Add objects (e.g., buildings)
gen.add_objects({
"id": "building1",
"hvac": "1R1C",
"resistance": 2.0,
"capacitance": 1e5,
"temp_min": 20.0,
"temp_max": 24.0,
})
# Prepare input data (e.g., weather)
data = {
"weather": pd.DataFrame({
"temp_out": [0.0] * 24,
}, index=pd.date_range("2025-01-01", periods=24, freq="h"))
}
# Generate time series
summary, df = gen.generate(data)For working with individual methods directly:
from entise.methods.pv import PVLib
# Create an instance
pvlib = PVLib()
# Generate time series
result = pvlib.generate(
latitude=48.1,
longitude=11.6,
power=5000,
weather=weather_df
)
# Access results
summary = result["summary"]
timeseries = result["timeseries"]For more detailed examples, check the examples directory.
EnTiSe supports generating time series for the following types:
Integrated:
- Domestic Hot Water (DHW)
- HVAC (Heating, Ventilation, and Air Conditioning)
- Occupancy data
- Solar Photovoltaic (PV)
Planned:
- Concentrated Solar Power (CSP)
- Electricity demand or supply
- Geothermal energy
- Hydroelectric power
- Mobility (transportation-related data)
- Tidal energy
- Wave energy
- Wind energy
Please see the documentation for further information.
Everyone is invited to develop this repository. Please follow the workflow described in the CONTRIBUTING.md.
This repository follows consistent coding styles. Refer to CONTRIBUTING.md for detailed standards.
The changelog is maintained in the CHANGELOG.md file. It lists all changes made to the repository. Follow instructions there to document any updates.