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and Heating","monthly_downloads":411,"total_dependent_repos":1,"total_dependent_packages":1,"readme":"![E.ON EBC RWTH Aachen University](./doc/_static/EBC_Logo.png)\n\n\n[![Build Status](https://travis-ci.org/RWTH-EBC/pyCity.svg?branch=master)](https://travis-ci.org/RWTH-EBC/pyCity)\n[![Coverage Status](https://coveralls.io/repos/github/RWTH-EBC/pyCity/badge.svg)](https://coveralls.io/github/RWTH-EBC/pyCity)\n[![License](http://img.shields.io/:license-mit-blue.svg)](http://doge.mit-license.org)\n\n\n# pycity_base\n\nPython package for data handling and scenario generation of city districts and urban energy systems.\n\n## Contributing\n\n1. Clone repository: `git clone git@github.com:RWTH-EBC/pyCity.git` (for SSH usage)\nAlternatively: Clone via https: `git clone https://github.com/RWTH-EBC/pyCity.git`\n2. Open an issue at [https://github.com/RWTH-EBC/pyCity/issues](https://github.com/RWTH-EBC/pyCity/issues)\n3. Checkout development branch: `git checkout development` \n4. Update local development branch (if necessary): `git pull origin development`\n5. Create your feature branch: `git checkout -b issueXY_explanation`\n6. Commit your changes: `git commit -m \"Add some feature #XY\"`\n7. Push to the branch: `git push origin issueXY_explanation`\n8. Submit a pull request from issueXY_explanation to development branch via [https://github.com/RWTH-EBC/pyCity/pulls](https://github.com/RWTH-EBC/pyCity/pulls)\n\n## Installation\n\n*One important issue at the beginning: Please do NOT confuse pycity_base with\nthe pycity package on pypi! This (other) pycity package is installable via \npip. However, if you want to install pycity_base, follow this instruction.*\n\npycity_base requires the following Python packages:\n- numpy==1.26.0\n- matplotlib==3.8.0\n- pandas==2.1.1\n- Shapely==2.0.1\n- openpyxl==3.1.2\n- networkx==2.5.1\n- pyproj==3.6.1\n\nas well as the EBC Python packages:\n\n- richardsonpy==0.2.1\n\nwhich is available at [https://github.com/RWTH-EBC/richardsonpy](https://github.com/RWTH-EBC/richardsonpy)\n\n- uesgraphs==0.6.4\n(with dependencies to shapely and pyproj)\n\nwhich is available at [https://github.com/RWTH-EBC/uesgraphs](https://github.com/RWTH-EBC/uesgraphs)\n\nrichardsonpy and uesgraphs can be installed via pip.\n\n### Installation of pycity_base\n\nThe latest version of pycity_base is 0.3.3.\n\nWhen uesgraph and its dependencies are installed, you should be able to install\npycity_base via pip:\n\n`pip install pycity_base`\n\nor:\n\n`pip install -e '\u003cyour_path_to_pycity_setup_folder\u003e'`\n\nor:\n\n`\u003cpath_to_your_python_dist\\Python.exe\u003e -m pip install -e '\u003cyour_path_to_pycity_setup_folder\u003e'`\n\n\nYou can check if installation / adding packages to python has been successful\nby adding new .py file and trying to import uesgraphs and pyCity.\n\n`import uesgraphs`\n\n`import pycity_base`\n\nImport should be possible without errors.\n\n## Example usage\n\n```Python\nimport shapely.geometry.point as point\nimport matplotlib.pyplot as plt\n\nimport uesgraphs.visuals as uesvis\n\nimport pycity_base.classes.timer as time\nimport pycity_base.classes.weather as weath\nimport pycity_base.classes.prices as price\nimport pycity_base.classes.environment as env\nimport pycity_base.classes.demand.apartment as apart\nimport pycity_base.classes.demand.occupancy as occ\nimport pycity_base.classes.demand.domestic_hot_water as dhw\nimport pycity_base.classes.demand.electrical_demand as eldem\nimport pycity_base.classes.demand.space_heating as spaceheat\nimport pycity_base.classes.building as build\nimport pycity_base.classes.city_district as citydist\nimport pycity_base.classes.supply.building_energy_system as besys\nimport pycity_base.classes.supply.boiler as boil\nimport pycity_base.classes.supply.photovoltaic as pvsys\n\n\ndef main():\n    #  Define the time discretization for the timer object\n    timestep = 3600  # in seconds\n\n    #  Define the total number of timesteps (in this case for one year)\n    nb_timesteps = int(365 * 24 * 3600 / timestep)\n\n    #  Generate environment with timer, weather, and prices objects\n    #  ######################################################################\n    timer = time.Timer(timeDiscretization=timestep,\n                       timestepsTotal=nb_timesteps)\n    weather = weath.Weather(timer=timer)\n    prices = price.Prices()\n\n    environment = env.Environment(timer=timer, weather=weather, prices=prices)\n\n    #  Generate city district object\n    #  ######################################################################\n    city_district = citydist.CityDistrict(environment=environment)\n    #  Annotations: To prevent some methods of subclasses uesgraph / nx.Graph\n    #  from failing (e.g. '.subgraph()) environment is set as optional input\n    #  parameter. However, it is necessary to use an environment object as\n    #  input parameter to initialize a working cityDistrict object!\n\n    #  Empty dictionary for building positions\n    dict_pos = {}\n\n    #  Generate shapely point positions\n    dict_pos[0] = point.Point(0, 0)  # (x, y)\n    dict_pos[1] = point.Point(20, 0)\n\n    #  Use for loop to generate two identical building objects for city\n    #  district\n    #  ######################################################################\n    for i in range(2):\n        living_area = 200  # in m^2\n        spec_sh_dem = 160  # Specific space heating demand in kWh/m^2\n        number_occupants = 3  # Total number of occupants\n\n        #  Generate space heating demand object (holding loadcurve attribute\n        #  with space heating power)\n        heat_demand = spaceheat.SpaceHeating(\n            environment=environment,\n            method=1,  # Standard load profile\n            livingArea=living_area,  # in m^2\n            specificDemand=spec_sh_dem)  # in kWh/m^2\n\n        #  Generate occupancy object with stochastic user profile\n        occupancy = occ.Occupancy(environment=environment,\n                                  number_occupants=number_occupants)\n\n        #  Generate electrical demand object\n        el_dem_stochastic = eldem.ElectricalDemand(\n            environment=environment,\n            method=2,  # stochastic Richardson profile (richardsonpy)\n            total_nb_occupants=number_occupants,  # Number of occupants\n            randomizeAppliances=True,  # Random choice of installed appliances\n            lightConfiguration=10,  # Light bulb configuration nb.\n            occupancy=occupancy.occupancy,  # Occupancy profile (600 sec resolution)\n            prev_heat_dev=True,  # Prevent space heating and hot water devices\n            annualDemand=None,  # Annual el. demand in kWh could be used for\n            do_normalization=False)  # rescaling (if do_normalization is True)\n        #  Annotation: The calculation of stochastic electric load profiles\n        #  is time consuming. If you prefer a faster method, you can either\n        #  hand over an own array-like load curve (method=0) or generate a\n        #  standardized load profile (SLP) (method=1)\n\n        #  Generate domestic hot water demand object\n        dhw_obj = dhw.DomesticHotWater(\n            environment=environment,\n            tFlow=60,  # DHW output temperature in degree Celsius\n            method=2,  # Stochastic dhw profile\n            supplyTemperature=25,  # DHW inlet flow temperature in degree C.\n            occupancy=occupancy.occupancy)  # Occupancy profile (600 sec resolution)\n\n        #  Generate apartment and add demand curves\n        apartment = apart.Apartment(environment)\n        apartment.addMultipleEntities([heat_demand,\n                                       el_dem_stochastic,\n                                       dhw_obj])\n\n        #  Generate building and add apartment\n        building = build.Building(environment)\n        building.addEntity(apartment)\n\n        #  Add buildings to city district\n        city_district.addEntity(entity=building,\n                                position=dict_pos[i])\n\n    #  Access information on city district object instance\n    #  ######################################################################\n    print('Get number of building entities:')\n    print(city_district.get_nb_of_building_entities())\n    print()\n\n    print('Get list with node ids of building entities:')\n    print(city_district.get_list_build_entity_node_ids())\n    print()\n\n    print('Get city district overall space heating power load curve:')\n    print(city_district.get_aggr_space_heating_power_curve(current_values=True))\n    print()\n    \n    print('Get city district overall space cooling power load curve:')\n    print(city_district.get_aggr_space_cooling_power_curve(current_values=True))\n    print()\n\n    #  We can use the Visuals class of uesgraphs to plot the city district\n\n    #  Generate uesgraphs visuals object instance\n    uesvisuals = uesvis.Visuals(uesgraph=city_district)\n\n    fig = plt.figure()\n    ax = fig.gca()\n    ax = uesvisuals.create_plot_simple(ax=ax)\n    plt.show()\n    plt.close()\n\n    #  Access buildings\n    #  ######################################################################\n    #  As city_district is a networkx graph object, we can access the building\n    #  entities with the corresponding building node,\n    #  Pointer to building object with id 1001:\n    building_1001 = city_district.nodes[1001]['entity']\n\n    print('Get building 1001 electric load curve:')\n    print(building_1001.get_electric_power_curve())\n    print()\n\n    #  Add energy systems to buildings\n    #  ######################################################################\n    #  We can also add building energy systems (BES) to each building object\n\n    #  Generate boiler object\n    boiler = boil.Boiler(environment=environment,\n                         qNominal=10000,  # Boiler thermal power in Watt\n                         eta=0.85)  # Boiler efficiency\n\n    #  Generate PV module object\n    pv = pvsys.PV(environment=environment,\n                  area=30,  # Area in m^2\n                  eta=0.15)  # Electrical efficiency at NOCT conditions\n\n    # Instantiate BES (container object for all energy systems)\n    bes = besys.BES(environment)\n\n    #  Add energy systems to bes\n    bes.addMultipleDevices([boiler, pv])\n\n    #  Add bes to building 1001\n    building_1001.addEntity(entity=bes)\n\n    print('Does building 1001 has a building energy system (BES)?')\n    print(building_1001.hasBes)\n\n    #  Access boiler nominal thermal power\n    print('Nominal thermal power of boiler in kW:')\n    print(building_1001.bes.boiler[0].qNominal / 1000)\n\n\nif __name__ == '__main__':\n    #  Run program\n    main()\n\n```\n\n## Tutorial\n\npycity_base also has also a jupyter notebook tutorial script under pycity/examples/tutorials/... \nTo open the jupyter notebook, open a command/terminal window and change your directory to the directory, \nwhere tutorial_pycity_calc_1.ipynb is stored. Then type 'jupyter notebook' (without '' signs) and press Enter.\nJupyter notebook should open within your browser (such as Firefox). Click on one notebook to start.\nIf your Pyhton path does not point at your Python installation, you have to\nopen jupyter notebook directly, e.g. by looking for the jupyter.exe in your distribution.\n\n## How to cite pycity_base\n\n+ Schiefelbein, J., Rudnick, J., Scholl, A., Remmen, P., Fuchs, M., Müller, D. (2019),\nAutomated urban energy system modeling and thermal building simulation based on OpenStreetMap data sets,\nBuilding and Environment,\nVolume 149,\nPages 630-639,\nISSN 0360-1323\n[pdf](https://doi.org/10.1016/j.buildenv.2018.12.025),\n[bibtex](https://github.com/RWTH-EBC/pyCity/tree/master/doc/S0360132318307686.bib)\n\nIf you require a reference in German language:\n+ Schiefelbein, J. , Javadi, A. , Fuchs, M. , Müller, D. , Monti, A. and Diekerhof, M. (2017), Modellierung und Optimierung von Mischgebieten. Bauphysik, 39: 23-32. doi:10.1002/bapi.201710001\n[pdf](https://doi.org/10.1002/bapi.201710001),\n[bibtex](https://github.com/RWTH-EBC/pyCity/tree/master/doc/pericles_1437098039.bib)\n\n## License\n\npyCity is released by RWTH Aachen University's E.ON Energy Research Center (E.ON ERC),\nInstitute for Energy Efficient Buildings and Indoor Climate (EBC) and\nInstitute for Automation of Complex Power Systems (ACS)\nunder the [MIT License](https://opensource.org/licenses/MIT)\n\n## Acknowledgements\n\nWe gratefully acknowledge the financial support by BMWi \n(German Federal Ministry for Economic Affairs and Energy) \nunder promotional references 03ET1138D and 03ET1381A.\n\n\u003cimg src=\"https://www.innovation-beratung-foerderung.de/INNO/Redaktion/DE/Bilder/Titelbilder/titel_foerderlogo_bmwi.jpg?__blob=normal\u0026v=3\" 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