The requirement of demandlib for pandas is pandas >= 0.18.0, < 0.25.

I install the demandlib in a package that uses xarray, which is incompatible with demandlib as it requires pandas >= 0.25.

Do you see a problem in loosening the limit and allowing pandas < 1.1.0 or pandas >= 0.25.? I didn't have a problem with that creating power and heat demand profiles but as there are no tests of the demandlib, I cannot be sure, of course.

What is called "unit test", is actually an integration test calculating the sum. This does not find serious issues, e.g. if timestamps are shuffled in the profiles. (This happened in #34 but was is detected by the tests)

During the run of the function get_sigmoid_parameters() in heating_building.py the following FutureWarning is raised:

FutureWarning: Calling float on a single element Series is deprecated and will raise a TypeError in the future. Use float(ser.iloc[0]) instead

The warning concerns the script between lines 271-275.

        a = float(sigmoid["parameter_a"])
        b = float(sigmoid["parameter_b"])
        c = float(sigmoid["parameter_c"])
        if self.ww_incl:
            d = float(sigmoid["parameter_d"])

I ran this script through the oemof-B3 model with a Windows 10 operating system and Python 3.10.12
This could be adapted to avoid TypeErrors.

It might be a valuable addition to have a simple physical model for heating demand. One option would be, that you give insulating surfaces (areas with corresponding widths and u-values for the insulation) and two time series for temperatures. In the most simple case, we could just take the transmission as demands.

What do you think?

see here:

SLP data is copyrighted. Currently, I cannot see a copyright note. (Examples I found are released under CC-by, so the copyright holder has to be named. At the moment, I cannot find an appropriate naming.)

Hello everybody,

I've been working on creating loadshapes as well when I found this very nice project. I would like to contribute parts of my work to the demandlib. As a first suggestion I have a few lines to calculate the coefficient of performance for air and ground heatpumps. The formulas can be found in this publication:
https://www.researchgate.net/publication/255759857_A_review_of_domestic_heat_pumps

Do you think this is an appropriate addition?
In which file should the code be added? In my Fork I wrote it into the bdew.py file as a function of the HeatBuilding class.

Greetings,
Pyosch

I suggest to keep it easy and add matplotlib to the requirements. @oemof/demandlib what do you think?

If you check the result, which is returned by bdew.HeatBuilding(), you'll find that its sum differs slightly from your input in annual_heat_demand.

To reproduce this, you can add these two lines to heat_demand_example.py here after calling bdew.HeatBuilding() for a single family housing ('efh'):

    result_annual_heat_demand = sum(demand["efh"])
    print("Sum of heat demand 'efh': ", result_annual_heat_demand)

Hi everyone,

I am new to the demandlib and the BDEW profiles.
Right know I am trying to figure out what kind of buildungs a certain set of arguments represent. As far as I understand it is not possible to create a profile for a stock of buildings that consists mainly (>60%) of new buildings (ger. "Neubauten").
Is that correct?
And does anyone of you know a way to do so, like using another set of sigmoid parameters?

Thanks!

Will be published on Friday 21st of October @oemof/demandlib

Still todo (feel free to add more)

• version number update
• release tag/message on github
• add whats new
• post at wordpress

The explanation of the values (1-11) for the parameter building_class is missing. In this document building_class is described. So the docs should be updated with this information.

There are several issues fixed in dev (see next comment on #21 (review)). There should be a new release.

Hello,
I'm new to the demandlib package and wanted to figure out what the source of this error might be:

Compilation of the heat_demand_example always leads to the error

which I am not able to sort out alone. I updated the oemof, demandlib and pandas package. The power example instead works completely fine. I'm using Python 3.8.3 Would be really nice if someone could help me out with this.

Regards,
Benjamin

@oemof/demandlib: We should write a documentation comparable to the one of the feedinlib (we can use the structure of the feedinlib documentation to start with).

• Setup a sphinx documentation
• Create a Readme file
• Check Docstrings

I will move the idea of this post to a new issue to keep it in mind.

@caro-rli : Please post comments here if you need feedback.

(Copied from wrong lo cation at oemof/feedinlib#49.)

Standard lows profiles (SLP) are – by construction – static. This can be a problem especially for electric demands of small sites (like single family homes). Thus, I suggest to introduce an option to get some randomness:

• Part of the SLP is considered base load (constant over time).
• The rest is composed of a static and a dynamic part.
  • The static part scales with the original SLP. Thus, setting this to 1 will just reproduce the SLP.
  • The dynamic part is modelled by a Poisson distribution with an expectation value scaled with the original SLP.

So – by construction – the SLP is reconstructed on average. Of course, this simple model is not very realistic, as it does not allow correlations between following values. However, it should be better then plain SLP.

Hello,
I wanted to know if it is possible to get the hourly warm water load separetely. I want to do the sizing of a solar thermal field based on the warm water demand from July.

Best regards.
korcun

• Optional: Open a release branch to avoid conflicts with other uses while the release progress.
• Run examples and tests check the Travis-CI results
• If you have failing test fix them or postpone the release.
• Check the whatsnew-file.
• Check if the latest documentation looks right at readthedocs.
• Change the version number everywhere!!! [TODO] (e.g. from 0.2.1dev to 0.2.1)
• If everything seems to work, merge the release branch into the master branch. (--no-ff)
• Open a working python3 virtual environment.
• Make sure that the release tools are up-to-date: pip install --upgrade setuptools wheel twine
• Make sure you are on the commit you want to release and create the package: python3 setup.py sdist bdist_wheel
• Check the package with twine: twine check dist/*
• If errors occur fix them or postpone the release.
• Set a release on github. Select the master branch (default: dev)! A label is set automatically.
• Use git fetch to see the label in your local master branch.
• If everything works as expected you can now upload the package version to PyPI: twine upload dist/*
• Merge the release branch into the dev branch. (--no-ff)
• Remove the obsolete release branch.
• Prepare the dev branch for further development: Adapt the version number. Also add a link in the whatsnew.rst file. Open the file locally or use the RAW button to see the links.

I am trying to generate an electricity demand profile for a business whose yearly consumption is known.
How would I do it? Sorry my German is not that great to understand the underlying documents which are mentioned in the documentation.

I tried to replicate power_demand_example.py, but keep getting the error get_profile() got an unexpected keyword argument 'dyn_function_h0'

Code:

import datetime
import demandlib.bdew as bdew
import demandlib.particular_profiles as profiles
from datetime import time as settime
import matplotlib.pyplot as plt


holidays = {
    datetime.date(2010, 5, 24): 'Whit Monday',
    datetime.date(2010, 4, 5): 'Easter Monday',
    datetime.date(2010, 5, 13): 'Ascension Thursday',
    datetime.date(2010, 1, 1): 'New year',
    datetime.date(2010, 10, 3): 'Day of German Unity',
    datetime.date(2010, 12, 25): 'Christmas Day',
    datetime.date(2010, 5, 1): 'Labour Day',
    datetime.date(2010, 4, 2): 'Good Friday',
    datetime.date(2010, 12, 26): 'Second Christmas Day'}


def power_example(testmode=False):
    year = 2010

    ann_el_demand_per_sector = {
        'g0': 3000,
        'h0': 3000,
        'i0': 3000,
        'i1': 5000,
        'i2': 6000,
        'g6': 5000}

    # read standard load profiles
    e_slp = bdew.ElecSlp(year, holidays=holidays)

    # multiply given annual demand with timeseries
    elec_demand = e_slp.get_profile(ann_el_demand_per_sector,
                                    dyn_function_h0=True)


    [......]

I talked to the developer of lpagg. There are some intersections between both packages. We agreed to think about a cooperation or even a merge of the functions.

https://github.com/jnettels/lpagg

Hi @ALL,

@TheOneAndra and I want to use the demandlib for estimating a demand profile in South America. Of course, for that we would have to tweak the profile in a way that their actual seasons are taken into account. We found something about seasons in the docstrings and a part of the code here:

However the function does not work and, indeed, there is no utilization of self.seasons anywhere else. Is this old code that should be deleted, or is this still in progress somewhere?

The SLPs by BDEW lead to erroneous results, when using them for district heating systems. In this publication we provide a set of SLPs to consider the influence of the district heating grid. Maybe this is of interest for the demandlib.

[ ] Add a paragraph about the limitations of the standardised load profiles
[ ] Fill usage section of the BDEW heat profiles
[ ] ...

Please add ToDos for the documentation here.

I couldn't find the implementation of the BDEW dynamization function ("Dynamisierungsfunktion") to smoothen the seasonal edges of the H0 slp:

f(x) = -3,92 * 10^-10 * x^4 + 3,2 * 10^-7 * x³ - 7,02 * 10^-5 * x²+0,0021 * x +1,24

Did I miss this or is it missing?

Hello everyone,

My name is Alexandre Jewell, and I work at a company focused on energy planning. We are currently working on the use of the BDEW hourly profiles for estimating the potential electricity flows within an energy community. To do so, I was thinking of using the standard load profiles from the BDEW, and I've got to thank you for how you structured them and made them so much clearer in your file shlp_hours_factors.csv contained within the folder bdew_data

However, after reading the documentation, I wasn't able to get a complete understanding of the structure of it. Here are my questions:

1. What is the difference between the column building_class and the column shlp_type ?
2. To which temperatures do the intervals temp_intervall_01, temp_intervall_02, etc. correspond to ?

Thank you very much for the help and the overall effort you've been putting into open source code so far !

Kind regards,

Alex

Hi,
after reviewing the calculation procedure of the demand profil, I was wondering why the demand profil in "get_normalized_bdew_profile" is calculated with a Sigmoid function. The BDEW guideline proposes an extension with a linear term, which in combination is called SigLinDe.

Did you already discuss this topic?

Hi everybody,

is the demandlib fully usable and are you already building projects on it?

I am reading some papers about heat load modelling atm and might use it for our project, too. But so far the documentation still seems to be a bit sparse ;)

I presume it uses the BDEW logic to model different sectors (GHD, ...) and building types (EFH, MFH, ...)? Is this right? And is there any external document that documents the underlying logic?

Best
Cord

the building_class is not explained in the documentation, the issue has occurred before and the links provided were not to be found.

Dear @oemof/demandlib,

I would like to cite the demandlib in a paper that will be submitted soon. Therefore, I plan to retrieve a DOI via Zenodo.
As we agreed for oemof to have "oemof developer group" as author, I would apply same style here. Does anyone have objections with this? Otherwise I'll go for it at beginning of next week.

Great, thanks for the first release of the demandlib. I tried to run the heat_demand example for the example directory and got the following error:

Traceback (most recent call last):
  File "/home/simon/uni/repos/demandlib/examples/heat_demand_example.py", line 37, in <module>
    name='EFH').get_bdew_profile()
  File "/home/simon/uni/repos/demandlib/demandlib/bdew.py", line 328, in get_bdew_profile
    how="geometric_series")
  File "/home/simon/uni/repos/demandlib/demandlib/bdew.py", line 207, in weighted_temperature
    temperature = self.df["temperature"].resample('D').mean().reindex(
AttributeError: 'numpy.float64' object has no attribute 'reindex'

You wanted to answer the following questions:

• Which parts should the damandlib include: profiles; annual demand; GIS-analyses ...(?)
• Who is the user - oemof or RLI?
• Is it usefull to determine the application area? (only germany)
• Are data included? How should they be stored? DB - Files - Links
• is this a universal application? Are detailed; specified elements allowed?

Does anyone remember if and how we decided on this issue? Does it make sense to add the ENTSO-E profiles to the demandlib? At the moment it would be comfortable for me to have these profiles and I would like to add them, but I'm not sure if we have decided against this.

• Add a paragraph about the limitations of the standardised load profiles
• Fill usage section of the BDEW heat profiles
• Add details for the types of electrical profiles -> PR #44
• ...

Please add ToDo's for the documentation here.