Getting Started

This guide will walk you through the basics of setting up and using EnTiSe for the first time.

Prerequisites

Before getting started, ensure you have:

  • Python 3.10 to 3.13 installed.

  • EnTiSe installed. If not, refer to the Installation guide.

Quickstart Example

EnTiSe was designed to allow users to generate time series quickly for multiple objects. Therefore, the tool focuses on batch processing so that users do not have to deal with aspects such as multiprocessing but can focus on defining the time series they need. However, the tool also allows to use the methods directly if they need more control. Below you find an example for both ways to get you started quickly.

Batch Processing

In batch processing you define all the parameters for all time series types beforehand and then create them all at once. This is a simple example for one object and one time series type but you can extend it to thousands and millions of time series. In the Examples, we defined several objects in a csv file and then load them into the generator. The same applies to the input data.

  1. Import the Required Components

Start by importing the necessary classes:

from entise import Generator
import pandas as pd

  1. Prepare Input Data

Define objects (metadata) and data that EnTiSe will process. Objects describe the characteristics of the time series you want to generate, while data provides the necessary input for the generation process, i.e. all data that does not fit into a dataframe but is required for the generation process. In this example, we define a simple object for an HVAC system and mock weather data.

# Define an object for HVAC
object_row = {
    "id": "building1",  # Unique identifier for the object which always needs to be specified
    "hvac": "1R1C",  # This specifies the method to use for generating the HVAC time series
    "resistance": 2.0,  # This is the thermal resistance in degrees Celsius per watt
    "capacitance": 1e5,  # This is the thermal capacitance in joules per degree Celsius
    "temp_init": 20.0,  # This is the initial indoor temperature in degrees Celsius
    "temp_min": 20.0,  # This is the minimum allowed indoor temperature in degrees Celsius
    "temp_max": 24.0,  # This is the maximum allowed indoor temperature in degrees Celsius
    "power_heating": 3000.0,  # This is the maximum heating power in watts
    "power_cooling": 3000.0,  # This is the maximum cooling power in watts
    "weather": "weather",  # This references the key in the data dictionary that contains the weather DataFrame
}

# Mock weather data
index = pd.date_range(start="2025-01-01", periods=24, freq="h", tz="UTC")
weather_df = pd.DataFrame({
    "datetime": index,
    "temp_out": [0.0] * 24,
}, index=index)

# Combine into a dictionary
data = {"weather": weather_df}

  1. Create and Run the Generator

Use the Generator to process your objects and data:

# Initialize the generator
gen = Generator()

# Add the object(s)
gen.add_objects(object_row)

# Generate timeseries
summary, df = gen.generate(data, workers=1)

  1. View the Results

The summary will contain summary metrics, and timeseries will hold the generated data:

print("Summary Metrics:")
print(summary)

print("\nTimeseries Data:")
print(df)

Direct Method Access

You can also call a method directly when you want fine-grained control or to embed EnTiSe in an existing pipeline. In this case you can either provide the parameters directly or use the same objects and data as in the batch processing example. Below is an example for the PVLib method using the direct method access.

from entise.methods.pv import PVLib

pv = PVLib()
result = pv.generate(
    latitude=48.1,  # Latitude of the location
    longitude=11.6,  # Longitude of the location
    power=5000,  # Maximum power output of the solar system in watts
    weather=weather_df,  # DataFrame with solar radiation data
)

summary = result["summary"]
timeseries = result["timeseries"]

Understanding the Workflow

  • Define objects and provide input data (e.g., weather).

  • EnTiSe selects methods and strategies based on your inputs.

  • A dependency resolver ensures correct execution order.

  • You get a summary and a detailed timeseries DataFrame.

See Workflow for the full conceptual guide, decision charts, and troubleshooting.

Available Methods

For a list of available methods, refer to the Methods section in this documentation.