SIJ | Solar-Institut Jülich
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
# initialize class with a number of buildings of 202 with a simultaneity factor of 0.786
scaling = TimeSeriesScalingModule(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 normal distribution with a reference profile approach. It scales the time series by applying a normal distribution to the difference of the original and a reference profile for every time step.
A fifth example shows the different ways to import the data.
A sixth example shows the speed of the different approaches.
A seventh example shows an example for an BDEW electrical profile generation and an eighth example example for a heat profile generation.
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.
Metadata
Release files for tssm 0.0.5
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | Provenance | Actions |
|---|---|---|---|---|
| tssm-0.0.5.tar.gz | 118.8 kB | Without provenance | Details for tssm-0.0.5.tar.gz |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Actions Reset |
|---|---|---|---|---|
| tssm-0.0.5-py3-none-any.whl | Python 3 | none | any | Details for tssm-0.0.5-py3-none-any.whl |
Total release size: 235.1 kB
Release files / tssm-0.0.5.tar.gz
| Download URL | tssm-0.0.5.tar.gz |
|---|---|
| Size | 118.8 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
c3e559665a513926d0ec387469568e1b1ea81903eb615cac9539d77135db61e4
|
|
BLAKE2b-256 checksum How to use checksums |
736dd05d1a3576e91dfabc8db43e72610d6fbdeff00336ab9bc4d01f43b6b41c
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/6.2.0 CPython/3.14.6
|
Release files / tssm-0.0.5-py3-none-any.whl
| Download URL | tssm-0.0.5-py3-none-any.whl |
|---|---|
| Size | 116.3 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
6debb59b338f7ea6501ec8f52faf109961c74e3012411b7bdd6495ec8b79f95d
|
|
BLAKE2b-256 checksum How to use checksums |
02905f57f7d825e17e9df4a169c400169e0f524fb9e189726f154a8d8b85d240
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/6.2.0 CPython/3.14.6
|
Security for tssm 0.0.5
Provenance
Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.
No provenance found
None of the files in this release were uploaded with provenance metadata.
A note on package security
PyPI is a repository for distributing packages, not a service for auditing them. While third-party security researchers and our staff do their best to detect and remove malicious packages, PyPI cannot guarantee the quality, security or suitability of any given package.
To learn more about protecting yourself from malicious actors, visit our package security documentation.