Through the DistrictGenerator, we introduce an python-based open-source tool aimed at urban planners, energy suppliers, housing associations, engineering firms, architectural professionals, as well as academic and research institutions. This tool furnishes crucial insights into energy demands, pivotal for the effective design and operation of neighborhoods energy systems. Consequently, users can discern actionable measures to harmonize energy supply. The DistrictGenerator offers a pioneering approach by mapping entire urban building stocks in neighborhood models for automated load profile calculations and dimensioning of distributed energy resources. By integrating several open-source data bases and tools like TEASER and richardsonpy.
The DistrictGenerator is being developed at RWTH Aachen University, E.ON Energy Research Center, Institute for Energy Efficient Buildings and Indoor Climate.
In the early stages of neighborhood planning, crucial data such as demand profiles of electricity, heating, domestic hot water, and occupancy profiles are often not available. The absence of this data hampers accurate evaluations of energy systems in districts. The DistrictGenerator seeks to advance the applicability of sustainable, cross-sectoral energy systems in neighborhoods, with a specific emphasis on exploiting synergy potentials among buildings of diverse usage structures through integrated concepts. We summarize the key contributions of the DistrictGenerator as follows:
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An open-source tool with minimal input requirements. Leveraging pre-set elements and default values of temporally resolved demand profiles, as well as decentralized heat generator sizing conforming to DIN standards.
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The tool enables the bottom-up representation of entire urban structures through neighborhood models, affording a sufficiently detailed analysis foundation.
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Facilitation of central operational optimization and presentation of analytical results and key performance indicators. This supports the examination of various neighborhood types and supply scenarios concerning technology selection and penetrations. We thereby create a platform for early-stage comparison of neighborhood concepts with the flexibility of selecting different variants, given the tool's rapid recalculations.
To install, first clone this repository with
git clone https://github.com/RWTH-EBC/districtgenerator
and secondly run:
pip install -e districtgenerator
Once you have installed the DistrictGenerator, you can check the examples to learn how to use the different components.
To generate your district, you need to know some information about its buildings. The minimal input data set was defined following the TABULA archetype approach:
- id: building ID (just numerate the buildings)
- building: residential building type (single family house, terraced house, multi family house or apartment block)
- year: construction year (the calendar year in which the building was constructed)
- retrofit: retrofit state according to TABULA (0: existing state, 1: usual refurbishment, 2: advanced refurbishment)
- area: reference floor area (given in square meters)
The example.csv file can be used as template.
After executing district generation you can find building-specific and time-dependent profiles in the .csv format in folder results/demands. The results contain:
- heat: space heating demand
- dhw: domestic hot water demand
- elec: electricity demand for lighting and electric household devices
- occ: number of persons present
- gains: internal gains from persons, lighting and electric household devices
Once you have installed the DistrictGenerator, you can check the examples to learn how to use the different components.
To test the tool's executability, run test_examples.py in the tests folder. This functional testing checks the entire chain of the tool, from data input and initialization to the output of the calculated profiles. It does not correspond to a test of the functional units of the entire process. This functional testing is based on the examples automatically executed one after another.
The documentation and examples should be understandable and the code bug-free. As all users have different backgrounds, you may not understand everything or encounter bugs. If you have questions, want to contribute new features or fix bugs yourself, please raise an issue here.
If you wrote a new feature, create a pull request and assign a reviewer before merging. Once review is finished, you can merge.
- Joel Schölzel (corresponding)
- Tobias Beckhölter
- Carla Wüller
- Sarah Henn
We presented or applied the library in the following publications:
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J. Schölzel, S. Henn, R. Streblow, D. Müller. Evaluation of Energy Sharing on a Local Energy Market Through Comparison of Energy Management Techniques. 36th International Conference on Efficiency, Cost, Optimization, Simulation and Environmental Impact of Energy Systems. https://doi.org/10.52202/069564-0307
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J. Schölzel, T. Beckhölter, S. Henn, C.Wüller, R. Streblow, D. Müller. Districtgenerator: A Novel Open-Source Webtool to Generate Building-Specific Load Profiles and Evaluate Energy Systems of Residential Districts. 37th International Conference on Efficiency, Cost, Optimization, Simulation and Environmental Impact of Energy Systems.
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C. Wüller, J. Schölzel, R. Streblow, D. Müller.Optimizing Local Energy Trading in Residential Neighborhoods:A Price Signal Approach in Local Energy Markets. 37th International Conference on Efficiency, Cost, Optimization, Simulation and Environmental Impact of Energy Systems.
The DistrictGenerator is released by RWTH Aachen University, E.ON Energy Research Center, Institute for Energy Efficient Buildings and Indoor Climate, under the MIT License.
The districtgenerator has been developed within the public funded project "BF2020 Begleitforschung ENERGIEWENDEBAUEN - Modul Quartiere" (promotional reference: 03EWB003B) and with financial support by BMWK (German Federal Ministry for Economic Affairs and Climate Action).
