MopoHeatSectorDataProcessing

Data processing scripts for dealing with heat sector data required in the Mopo project.

This package is part of the EU project Mopo building stock data processing, and is the last step of three:

1. AmBIENCe2ABM handles EU-wide building stock input data processing for ArchetypeBuildingModel.jl (ABM.jl).
2. Mopo AmBIENCe2ABM Demo contains the Spine Toolbox workflow using ABM.jl for calculating EU-wide heating and cooling timeseries.
3. MopoHeatSectorDataProcessing post-processes the output from step 2 into input data suitable for the Mopo project case studies.

The geographical data coverage is illustrated below:

Documentation

MopoHeatSectorDataProcessing has been documented mainly through README.md files including this one. Each sub-folder has its own README.md describing its contents in more detail. This was done to make browsing the contents of this repository in GitHub as convenient and self-explanatory as possible.

Eventually, the D2.2 deliverable will also be published on the Mopo project website, potentially better explaining the overall data processing workflow.

Key contents

1. input-data/ contains the raw data sources used for the processing scripts.
2. output/ contains the processed output .csv files.
3. cop_export.ipynb postprocesses and exports ArchetypeBuildingModel heat pump COP timeseries for Mopo WP5.
4. demand_scaling.ipynb postprocesses the ArchetypeBuildingModel heating/cooling demand timeseries, as well as processess Hotmaps heating/cooling demand scenario data.
5. demand_testing.ipynb contains experiments and examination of the data underlying demand_scaling.ipynb.
6. examine_capacity_data.ipynb examines the raw data available for heat-only capacity estimation for process_capacity_data.ipynb.
7. examine_generation_data.ipynb examines the generation technology data for process_generation_data.ipynb.
8. examine_storage_data.ipynb examines the storage technology data for process_storage_data.ipynb.
9. process_capacity_data.ipynb processes and output estimated heat-only generation capacity data for Mopo WP5.
10. process_generation_data.ipynb processes and outputs generation data for Mopo WP5.
11. process_storage_data.ipynb processes and outputs storage technology parameters for Mopo WP5.

Installation

Clone (or download) this repository on your machine. Since the processing scripts are written in Julia, you'll need to have it installed on your machine.

Note that since the processing scripts are Jupyter notebooks, you'll also need to install the IJulia package to your Julia. Depending on how you run the scripts, this step might be automatic, but you can do this manually in Julia REPL as well:

julia> ]
(@v1.11) pkg> add IJulia

The Jupyter notebooks contain code to automatically set up the necessary Julia environment. However, you can also do this manually by opening the Julia REPL in the folder this README.md is located in

julia>

entering the package manager

julia> ]

and manually instantiating the dependencies

(MopoHeatSectorDataProcessin...) pkg> instantiate

Required input data

The data processing scripts require quite a bit of input data, and including them within this GitHub repository is not really ideal. Thus, an example input data set has been archived in Zenodo, which needs to be extracted under the input-data folder for the scripts to run correctly.

Use

The processed output files are not included in this repository, as the timeseries especially take up quite some space.

In order to recreate them, simply run the Jupyter notebooks handling the data processing. However, note that you will need to download the required input data separately as described above.

License

The codebase and the output are licensed under the MIT License, see LICENSE for more information.

The required input files all have slightly different licenses, see the README in the Zenodo dataset for more information.

Acknowledgements

This work was supported by EU project Mopo (2023-2026), which has received funding from European Climate, Infrastructure and Environment Executive Agency under the European Union’s HORIZON Research and Innovation Actions under grant agreement N°101095998.