A simple tool for generating time series data related to energy systems and building operations.

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:

• Python (3.10 or newer)
• Git for version control (optional, only for development)

EnTiSe can be installed directly from PyPI:

pip install entise

For faster installation, you can use uv:

pip install uv
uv pip install entise

For development or the latest features, you can install directly from the repository:

pip install git+https://github.com/tum-ens/entise.git

Or 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.
  • services/: Auxiliary services supporting the core functionality.
• 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, you can use the Generator and either add objects one by one or in bulk using DataFrames or dictionaries:

from entise import Generator

# Initialize the generator
gen = Generator()

# 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 more granular access you can work with the 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:

• Electricity demand
• Domestic Hot Water (DHW)
• Heat pump (COP)
• HVAC (Heating, Ventilation, and Air Conditioning)
• Occupancy data
• Solar Photovoltaic (PV)
• Wind energy

Planned:

• Concentrated Solar Power (CSP) (https://oemof-thermal.readthedocs.io/en/latest/concentrating_solar_power.html)
• Geothermal energy
• Hydroelectric power (https://github.com/IdahoLabResearch/HydroGenerate or https://github.com/9505-PNNL/wmpy_power or https://www.theoj.org/joss-papers/joss.03294/10.21105.joss.03294.pdf)
• Mobility (https://emobpy.readthedocs.io/en/latest/)
• Solar thermal (https://oemof-thermal.readthedocs.io or https://www.theoj.org/joss-papers/joss.03294/10.21105.joss.03294.pdf)
• Tidal energy
• Wave energy

Please see the documentation for further information.

We are happy if you want to contribute to this repository! Have a look at the guides in our docs for more information on how to contribute and our coding standards.

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.