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Notebook","category":"Energy Systems","sub_category":"Load and Demand Forecasting","monthly_downloads":0,"total_dependent_repos":0,"total_dependent_packages":0,"readme":"# DemandRegio\n\nThis project aims at setting up both a database and a python toolkit called `disaggregator` for\n- temporal and\n- spatial disagregation\n\nof demands of \n- electricity,\n- heat and\n- natural gas\n\nof the final energy sectors\n- private households,\n- commerce, trade \u0026 services (CTS) and\n- industry.\n\n\n## Installation\n\nBefore we really start, please install `conda` through the latest [Anaconda package](https://www.anaconda.com/distribution/) or via [miniconda](https://docs.conda.io/en/latest/miniconda.html). After successfully installing `conda`, open the **Anaconda Powershell Prompt**.  \nFor experts: You can also open a bash shell (Linux) or command prompt (Windows), but then make sure that your local environment variable `PATH` points to your anaconda installation directory.\n\nNow, in the root folder of the project create an environment to work in that will be called `disaggregator` via\n\n```bash\n$ conda env create -f environment.yml\n```\n\nwhich installs all required packages. Then activate the environment\n\n```bash\n$ conda activate disaggregator\n```\n\n## How to start\n\nOnce the environment is activated, you can start a Jupyter Notebook from there\n\n```bash\n(disaggregator) $ jupyter notebook\n```\n\nAs soon as the Jupyter Notebook opens in your browser, click on the `01_Demo_data-and-config.ipynb` file to start with a demonstration:\n\n![Jupyter_View][img_01]\n\n[img_01]: img/jupyter_notebook.png \"Jupyter Notebook View\"\n\n## Results\n\n![Jupyter_View][img_02]\n\n[img_02]: img/spatial_elc_by_household_sizes.png \"Year Electricity Consumption of Private Households\"\n\n## How does it work?\n\nFor each of the three sectors 'private households', 'commerce, trade \u0026 services' and 'industry' the spatial and temporal disaggregation is accomplished through application of various functions. These functions take input data from a database and return the desired output as shwon in the diagram. There are four Demo-Notebooks to present these functions and demonstrate their execution.\n\n![Jupyter_View][img_03]\n\n[img_03]: img/model_overview.png \"Schematic diagram of modelling approach\"\n\n## Acknowledgements\n\nThe development of disaggregator was part of the joint [DemandRegio-Project](https://www.ffe.de/en/topics-and-methods/production-and-market/736-harmonization-and-development-of-methods-for-a-spatial-and-temporal-resolution-of-energy-demands-demandregio) which was carried out by\n\n- Forschungszentrum Jülich GmbH (Simon Burges, Bastian Gillessen, Fabian Gotzens)\n- Forschungsstelle für Energiewirtschaft e.V. (Tobias Schmid)\n- Technical University of Berlin (Stephan Seim, Paul Verwiebe)\n\n## License\n\nCurrent version of software written and maintained by Paul A. Verwiebe (TUB)\n\nOriginal version of software written by Fabian P. Gotzens (FZJ), Paul A. Verwiebe (TUB), Maike Held (TUB), 2019/20.\n\ndisaggregator is released as free software under the [GPLv3](http://www.gnu.org/licenses/gpl-3.0.en.html), see [LICENSE](LICENSE) for further information.\n","funding_links":[],"readme_doi_urls":[],"works":{},"citation_counts":{},"total_citations":0,"keywords_from_contributors":["energy-system","energy-system-model"],"project_url":"https://ost.ecosyste.ms/api/v1/projects/288","html_url":"https://ost.ecosyste.ms/projects/288"}