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LPagg

The load profile aggregator combines profiles for heat and power demand of buildings from different sources. Data for households is taken from VDI 46551, while BDEW2 is used for commercial buildings. Both sources provide 24h profiles in resolutions of 15 minutes for defined typical-days, which LPagg combines to construct a selected calendar year. It takes into account input weather data (from DWD3), local holidays and daylight saving time. Moreover, a random time shift derived from a normal distribution can be applied to each building, in order to approximate the effects of simultaneity present in larger groups of buildings. While the sources only represent very generalized load profiles, LPagg still provides a fast and easy method to create reliable input data for annual simulations of district energy systems. All settings have to be provided via a YAML configuration file. LPagg was created as part of the publicly funded project futureSuN4.

With the configuration setting use_demandlib=True you may choose to let the VDI 4655 profiles be generated by demandlib. These functions are currently being ported over to demandlib, providing improved performance for large numbers of buildings.

The following table contains a summary of the available profiles and their sources:

lpagg lpagg + demandlib
HH (households) Heating VDI 4655 (lpagg) VDI 4655 (demandlib)
Domestic hot water VDI 4655 (lpagg) VDI 4655 (demandlib)
Electricity VDI 4655 (lpagg) VDI 4655 (demandlib)
GHD (commercial) Heating futuresolar futuresolar
Domestic hot water DOE DOE
Electricity BDEW-typical days BDEW-typical days

Installation

Installation with Anaconda

LPagg is a Python package. The recommended way to install the latest release is by using Anaconda:

conda install lpagg -c jnettels

In case of package conflicts, this might work instead:

conda install lpagg -c jnettels -c conda-forge

Installation from source

  1. You need Python. The recommended way is to install Python with Anaconda, a package manager that distributes Python with data science packages. During installation, (despite the warning) please set the advanced option:
[x] Add Anaconda to my PATH environment variable

  1. You also need to install Git for downloading this repository.

  2. Then you can clone this repository to a directory of your choice by opening a cmd window and writing:

git clone https://github.com/jnettels/lpagg.git
  1. Now you need to change directory into the new folder:
cd lpagg
  1. From here you can build and install lpagg with conda:
conda build conda.recipe
conda install --use-local lpagg -y

Update

When an update to lpagg is available in this repository, you can simply change to the folder from step 4 and download the latest files with:

git pull

Afterwards, repeat step 5 to build and install the update.

Usage

lpagg

You should be able to start the program from a cmd window:

lpagg

This will bring up a file dialog for choosing a YAML configuration file that contains all the settings required for the program. To try it, you can choose the example lpagg\examples\VDI_4655_config_example.yaml.

You can also show a help message:

lpagg --help

Another approach is to place a shortcut where you would like to use it. Moreover, you can now write you own Python scripts that use lpagg. Use the script __main__.py in this repository as an example.

simultaneity

One feature of lpagg is creating the effects of a simultaneity factor. Copies of a given time series are created and, if a standard deviation sigma is given, a time shift is applied to the copies. This can also be used as a standalone script, where you have to provide a file with time series data. In a cmd window, write the following to learn more:

simultaneity --help

There is a version with a graphical user interface, which can be started with the command:

simlty_GUI

Changelog

At the moment, no dedicated changelog is maintained. However, important changes are noted on the release page.

Literature

1 VDI 4655, 2008: Referenzlastprofile von Ein- und Mehrfamilienhäusern für den Einsatz von KWK-Anlagen. ↩

2 BDEW (1999): Repräsentative VDEW-Lastprofile. Unter Mitarbeit von BTU Cottbus. Frankfurt am Main. Online verfügbar unter https://www.bdew.de/media/documents/1999_Repraesentative-VDEW-Lastprofile.pdf ↩

3 Deutscher Wetterdienst (2017): Ortsgenaue Testreferenzjahre von Deutschland für mittlere und extreme Witterungsverhältnisse. Handbuch. Unter Mitarbeit von Bundesamt für Bauwesen und Raumordnung (BBR). Offenbach. Online verfügbar unter http://www.bbsr.bund.de/BBSR/DE/FP/ZB/Auftragsforschung/5EnergieKlimaBauen/2013/testreferenzjahre/try-handbuch.pdf ↩

4 Bonk, Natalie; Juschka, Winfried; Kofler, Philipp; Nettelstroth, Joris; Pröll, Markus; Bestenlehner, Dominik et al. (2020): futureSuN. Analyse, Bewertung und Entwicklung zukunftsfähiger Anlagenkonzepte für solare Nahwärmeanlagen mit saisonaler Wärmespeicherung. SIZ energie+, SIZ EGS, IGTE, ZAE Bayern. Braunschweig, Stuttgart, München. Online verfügbar unter https://siz-energie-plus.de/projekte/futuresun. ↩