deploy: 6354bca

.buildinfo

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+# Sphinx build info version 1
+# This file hashes the configuration used when building these files. When it is not found, a full rebuild will be done.
+config: fcdc215d0bedfc57a14a8d537aa21449
+tags: 645f666f9bcd5a90fca523b33c5a78b7

_sources/about.md.txt

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+# About this code
+
+This documentation is under construction

_sources/autoapi/aauvarmeplan2021/index.rst.txt

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+aauvarmeplan2021
+================
+
+.. py:module:: aauvarmeplan2021
+
+.. autoapi-nested-parse::
+
+   Pre-Processing of the Varmeplan 2021 dataset
+
+   Aalborg dataset, can be used to get individual and district heat gross supply:
+   https://vbn.aau.dk/da/datasets/kommunepakker-varmeplan-danmark-2021
+   - Note that heat demand should be combined wrt. the s1-s5 scenarios (sums to 53.95 TWh without heat efficiency investments)
+
+   Assumption for distribution of low-, mid- and high temperatures should probably come from:
+   Viegand Maagø and Energistyrelsen. ‘Kortlægning Af Energiforbrug Og Opgørelse Af Energisparepotentialer i Produktionserhvervene’, 2022.
+   See page 8:
+   “
+   - Omkring 45% af forbruget ligger ved temperaturer under 100 ºC – totalt 26.798 TJ/år 
+   - Godt 40% ligger ved temperaturer over 200 ºC – totalt 23.774 TJ/år 
+   - Resten, altså ca. 15%, ligger i intervallet 100 – 200 ºC eller derover
+   ”, Viegand Maagø and Energistyrelsen, 2022, p. 8
+
+   One simple, first assumption could be to say that 40-55% is high temperature, 
+   and use that amount of TJ to distribute the sum of TJ in the "over 80C" category
+   of Aalborg data into high and middle temperature (middle temperature is the remaining over 80C)  
+   - E.g.: 
+   - 59496 TJ/år in total
+   - 32723 TJ/år for >100 C (55%)
+   AAU data:
+   GJ_over_80    17.1217 %       3369.050427 TJ
+   GJ_60_80C     12.1648 %       2393.687324 TJ
+   GJ_under_6    70.7135 %       13914.38463 TJ
+   ..doesn't really fit - maybe the assumption that surplus heat above 80C comes from high, 60-80C from mid and below 60C from low is good enough?
+   for that to be true:
+   - 17.1217 + 12.1648 = 29.2865
+   - 40 - 29.2865 = 10.7135
+   - 10.7135 / 0.7 = 15.305% of GJ_under_6 is high, plus 100% of GJ_60_80C and 100% of GJ_over_80
+   - 15% / 0.7 = 21.42857142857143 of GJ_under_6 is mid
+   - 45% / 0.7 = 64.28571428571429 of GJ_under_6 is low
+
+   Python package requirements:
+   - xlrd 
+
+   Created on 26.08.2024
+   @author: Mathias Berg Rosendal, PhD Student at DTU Management (Energy Economics & Modelling)
+
+
+
+Attributes
+----------
+
+.. autoapisummary::
+
+   aauvarmeplan2021.style
+   aauvarmeplan2021.fc
+   aauvarmeplan2021.data
+
+
+Classes
+-------
+
+.. autoapisummary::
+
+   aauvarmeplan2021.VPDK21
+
+
+Module Contents
+---------------
+
+.. py:data:: style
+   :value: 'report'
+
+
+.. py:data:: fc
+   :value: 'white'
+
+
+.. py:class:: VPDK21(scenario: str = 'SUM_GWh_uden_besp')
+
+   .. py:attribute:: files
+
+
+   .. py:attribute:: DH
+
+
+   .. py:attribute:: IND
+
+
+   .. py:attribute:: correct_names
+
+
+   .. py:attribute:: temp
+
+
+   .. py:attribute:: names
+      :value: ['year', 'municipality', 'user']
+
+
+
+.. py:data:: data
+

_sources/autoapi/clustering/index.rst.txt

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+clustering
+==========
+
+.. py:module:: clustering
+
+.. autoapi-nested-parse::
+
+   TITLE
+
+   Description
+
+   Created on 11.09.2024
+   @author: Mathias Berg Rosendal, PhD Student at DTU Management (Energy Economics & Modelling)
+
+
+
+Attributes
+----------
+
+.. autoapisummary::
+
+   clustering.cmap
+   clustering.style
+   clustering.fc
+   clustering.collected
+
+
+Functions
+---------
+
+.. autoapisummary::
+
+   clustering.truncate_colormap
+   clustering.correct_VRE_data
+   clustering.convert_municipal_code_to_name
+   clustering.collect_clusterdata
+   clustering.cluster
+
+
+Module Contents
+---------------
+
+.. py:data:: cmap
+
+.. py:function:: truncate_colormap(cmap, minval=0.0, maxval=1.0, n=100)
+
+.. py:data:: style
+   :value: 'ppt'
+
+
+.. py:data:: fc
+   :value: 'white'
+
+
+.. py:function:: correct_VRE_data(path_to_file, generation_name: str)
+
+.. py:function:: convert_municipal_code_to_name(to_be_converted: pandas.DataFrame, column_to_convert: (str, int), pivot_table: bool = False, exclude_regions: list = ['Herlev', 'Christiansø'], muni_geofile_path: str = 'C:\\Users\\mberos\\gitRepos\\balmorel-preprocessing\\RawDataProcessing\\Data\\Shapefiles\\Denmark\\Adm\\gadm36_DNK_2.shp')
+
+.. py:function:: collect_clusterdata(energinet_el: pandas.DataFrame, plot_cf: bool = False)
+
+.. py:function:: cluster(con: Modules.Submodules.municipal_template.DataContainer, n_clusters: int, use_connectivity: bool = True, manual_corrections: list = [['Bornholm', 'Christiansø', 1], ['Bornholm', 'Dragør', 1], ['Esbjerg', 'Fanø', 1], ['Rødovre', 'Frederiksberg', 1], ['Slagelse', 'Nyborg', 0], ['Samsø', 'Kalundborg', 0]], connection_remark: str = 'connec. included + artifical', data_remark: str = 'all combined + xy coords')
+
+.. py:data:: collected
+

_sources/autoapi/createDE/index.rst.txt

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+createDE
+========
+
+.. py:module:: createDE
+
+.. autoapi-nested-parse::
+
+   Create electricity demand profiles for Denmark
+   Use Energinet data for municipalities:
+   https://www.energidataservice.dk/tso-electricity/consumptionindustry
+   Convert LAU code to municipality name with: 
+   LAU – NUTS 2021, EU-27 and EFTA / available candidate countries
+   https://ec.europa.eu/eurostat/web/nuts/local-administrative-units
+   Note: The dataset above also contains population
+
+   Created on 22.08.2024
+
+   @author: Mathias Berg Rosendal, PhD Student at DTU Management (Energy Economics & Modelling)
+
+
+
+Attributes
+----------
+
+.. autoapisummary::
+
+   createDE.cmap
+   createDE.style
+   createDE.fc
+
+
+Functions
+---------
+
+.. autoapisummary::
+
+   createDE.convert_names
+   createDE.make_inc_file
+   createDE.main
+
+
+Module Contents
+---------------
+
+.. py:data:: cmap
+
+.. py:data:: style
+   :value: 'report'
+
+
+.. py:data:: fc
+   :value: 'white'
+
+
+.. py:function:: convert_names(conversion_file: str, el_dataset: str)
+
+.. py:function:: make_inc_file(xarray: xarray.Dataset, data_variable: str, selection: Tuple[dict, None] = None, sets_to_sum: Tuple[str, list, None] = None)
+
+.. py:function:: main(conversion_file: str, el_dataset: str, show_difference: bool = False)
+
+       Main function to process and convert dataset names, and create .inc files.
+   Args:
+       conversion_file (str): Path to the file containing conversion mappings.
+       el_dataset (str): Path to the dataset file to be processed.
+       show_difference (bool, optional): Flag to indicate whether to print the 
+                                           dataset before and after conversion. 
+                                           Defaults to False.
+   Returns:
+       None
+
+

_sources/autoapi/createDH/index.rst.txt

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+createDH
+========
+
+.. py:module:: createDH
+
+.. autoapi-nested-parse::
+
+   Creating Danish heat demand for Balmorel 
+
+   Futuregas dataset default. Can be used to get temporal profiles
+
+   Aalborg dataset, can be used to get individual and district heat gross supply:
+   https://vbn.aau.dk/da/datasets/kommunepakker-varmeplan-danmark-2021
+   - Note that heat demand should be combined wrt. the s1-s5 scenarios (sums to 53.95 TWh without heat efficiency investments)
+
+   Python package requirements:
+   - xlrd 
+
+   Created on 11.03.2024
+
+   @author: Mathias Berg Rosendal, PhD Student at DTU Management (Energy Economics & Modelling)
+
+
+
+Attributes
+----------
+
+.. autoapisummary::
+
+   createDH.cmap
+   createDH.style
+   createDH.fc
+   createDH.X
+
+
+Classes
+-------
+
+.. autoapisummary::
+
+   createDH.DistrictHeat
+   createDH.DistrictHeatAAU
+
+
+Functions
+---------
+
+.. autoapisummary::
+
+   createDH.find_value
+
+
+Module Contents
+---------------
+
+.. py:data:: cmap
+
+.. py:data:: style
+   :value: 'report'
+
+
+.. py:data:: fc
+   :value: 'white'
+
+
+.. py:class:: DistrictHeat(dataset: str = 'Denmark_Futuregas')
+
+   Class for district heating data
+
+   Existing datasets:
+       - Denmark_Futuregas
+
+   Args:
+       dataset (str, optional): _description_. Defaults to 'DK'.
+
+
+   .. py:method:: assign_DH(areas: geopandas.GeoDataFrame, df_intercepts: pandas.DataFrame, value_col: str = 'Value') -> None
+
+      DH data must have A and Y sets, where A matches the areas index
+
+      Ends up with format:
+
+          Y1  Y2      Y3      --      YN
+
+      A1
+
+      A2
+
+      |
+
+      AM
+
+
+
+   .. py:method:: assign_DHT(areas: geopandas.GeoDataFrame, df_intercepts: pandas.DataFrame, value_col: str = 'Value', agg_func: str = 'sum') -> None
+
+      DHT data must have S.T and A sets, where A matches the areas index
+
+      Ends up with format:
+
+                              A1      A2      A3      --      AN
+
+      S01     .       T001
+
+      S01     .       T002
+
+      |
+
+      S52     .       T168
+
+
+
+
+   .. py:method:: assign_DH_profile()
+
+
+   .. py:method:: join_geo_with(df: pandas.DataFrame, kwargs: dict = {'how': 'inner'}) -> None
+
+
+   .. py:method:: plot_original_data(year: str, areas: geopandas.GeoDataFrame, plot_density: bool = False, fc: str = 'white', area_fc: Union[str, list] = [0.85, 0.85, 0.85], ax: Union[matplotlib.axes._axes.Axes, str] = '') -> tuple[matplotlib.figure.Figure, matplotlib.axes._axes.Axes]
+
+
+   .. py:method:: plot_aggregated_data(year: str, areas: geopandas.GeoDataFrame, plot_density: bool = False) -> tuple[matplotlib.figure.Figure, matplotlib.axes._axes.Axes]
+
+
+.. py:function:: find_value(df: pandas.DataFrame, element: any, func: str = 'max', ind: int = 0) -> any
+
+.. py:class:: DistrictHeatAAU
+
+   .. py:attribute:: f1
+
+
+   .. py:attribute:: f2
+
+
+   .. py:method:: combine_data(plot: bool = False)
+
+
+.. py:data:: X
+