tssm 0.0.4

Package to create out of a single load profile a profile for a whole district using the diversity factor

Time Series Scaling Module (TSSM)

TSSM is a python package for the up-scaling of time series or load such as electricity, heating, etc.

Warning

This package is under heavy development!

Getting started

Install TSSM

Install tssm directly from PyPi as follows:

pip install tssm

Further installation instructions can be found in the documentation under 'Getting started'.

Usage

example usages can be found in the examples' folder.

Basic workflow

A small example how tssm can be used is described as follows:

# import module and Daily period variable
from tssm import TimeSeriesScalingModule as tssm, DAILY

# initialize class with a number of buildings of 202 with a simultaneity factor of 0.786
scaling = tssm(number_of_buildings=202, simultaneity_factor=0.786)
# read profile from data.csv file and use the Electricity and Date column
scaling.data.read_profile_from_csv_with_date(path="./data.csv", column_of_load="Electricity", column_of_date="Date")
# calculate linear scaled values with a daily simultaneity factor and average value
daily_scaled_values = scaling.calculate_using_average_values(period=DAILY)

Examples

A first example shows the linear approach. It scales the time series between the scaled time series and an average.

A second example shows the scaling approach. It scales the time series between the scaled time series and a scaling time series.

A third example shows the normal distribution approach. It scales the time series by applying a normal distribution to every time step.

A fourth example shows the different ways to import the data.

A fifth example shows the speed of the different approaches.

License

The module is licensed under BSD 3-Clause License.

Further, License information can be found here.

Reference

Acknowledgements

Content

The documentation of the tssm code can be found here.