EnTiSe Documentation
EnTiSe (Energy Time Series) is a Python framework for generating synthetic time series data for energy systems research. It provides a flexible and extensible platform for creating realistic time series for:
Drinking Hot-Water
Electricity
Heat Pumps
HVAC
Occupancy
PV
Wind
EnTiSe is designed to support a wide range of research applications in the energy domain:
Building Energy Modeling: Simulate thermal behavior and energy consumption of buildings
Renewable Energy Integration: Model the variability of renewable energy sources and their impact on energy systems
Demand Response: Analyze the potential for demand-side management and flexibility
Energy System Planning: Support the design and sizing of energy systems with realistic load profiles
The framework can be integrated with other energy modeling tools and workflows, serving as a foundation for comprehensive energy systems analysis.
Key Features
Modular Design: Easily extensible with new methods and strategies independent of existing methods
Flexible Pipeline: Automatic dependency resolution between methods
Multiple Domains: Support for HVAC, electricity, and more
Quick Start
For those wanting to quickly get started with EnTiSe, here is a simple example of how to use the Generator to create synthetic time series data for a building’s thermal behavior. We recommend having a look at the Examples to get a better understanding of the available parameters and methods.
from entise import Generator
import pandas as pd
# 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)