Package to create out of a single load profile a profile for a whole district using the diversity factor
Project description
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
from tssm import TimeSeriesScalingModule as tssm
# initialize class with a number of buildings of 202 with a simultaneity factor of 0.786
df_test = tssm(number_of_buildings=202, simultaneity_factor=0.786)
# read profile from data.csv file and use the Electricity and Date column
df_test.values.read_profile_from_csv_with_date(path="./data.csv", column_of_load="Electricity", column_of_date="Date")
# calculate linear scaled values with a daily (period=1) simultaneity factor and average value
daily_scaled_values = df_test.calculate_linear(period=1)
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.
Project details
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