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  "author": "Franz Harke, Philipp Otto",
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  "author": "Franz Harke",
            
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  "author_email": "",
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  "author_email": "",
            
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  "creator_user_id": "17755db4-395a-4b3b-ac09-e8e3484ca700",
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  "creator_user_id": "17755db4-395a-4b3b-ac09-e8e3484ca700",
            
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  "doi": "10.25835/r1myox7h",
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  "doi": "10.25835/r1myox7h",
            
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  "doi_date_published": "2022-11-24",
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  "doi_date_published": "2022-11-24",
            
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  "doi_publisher": "LUIS",
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  "doi_publisher": "LUIS",
            
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  "doi_status": "true",
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  "doi_status": "true",
            
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  "domain": "https://data.uni-hannover.de",
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  "domain": "https://data.uni-hannover.de",
            
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  "extra_authors": [
            
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    {
            
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      "extra_author": " Philipp Otto"
            
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    }
            
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  ],
            
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  "groups": [],
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  "groups": [],
            
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  "have_copyright": "Yes",
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  "have_copyright": "Yes",
            
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  "id": "8da64379-3e44-4e1e-bf03-165df07cd396",
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  "id": "8da64379-3e44-4e1e-bf03-165df07cd396",
            
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  "isopen": false,
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  "license_id": "CC0-1.0",
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  "license_id": "CC0-1.0",
            
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  "license_title": "CC0-1.0",
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  "license_title": "CC0-1.0",
            
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  "maintainer": "Franz Harke",
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  "maintainer": "Franz Harke",
            
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  "maintainer_email": "",
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  "maintainer_email": "",
            
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  "metadata_created": "2023-01-12T13:14:25.860418",
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  "metadata_modified": "2023-08-04T08:46:47.898033",
            
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  "name": 
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  "name": 
            
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ent-households-as-a-driving-factor-for-sustainability-transformation",
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ent-households-as-a-driving-factor-for-sustainability-transformation",
            
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  "notes": "To get the __consumption model__ from Section 3.1, one 
27
  "notes": "To get the __consumption model__ from Section 3.1, one 
            
23
needs load execute the file consumption_data.R. Load the data for the 
28
needs load execute the file consumption_data.R. Load the data for the 
            
24
3 Phases ./data/CONSUMPTION/PL1.csv, PL2.csv, PL3.csv, transform the 
29
3 Phases ./data/CONSUMPTION/PL1.csv, PL2.csv, PL3.csv, transform the 
            
25
data and build the model (starting line 225). The final consumption 
30
data and build the model (starting line 225). The final consumption 
            
26
data can be found in one file for each year in 
31
data can be found in one file for each year in 
            
27
./data/CONSUMPTION/MEGA_CONS_list.Rdata\r\n\r\nTo get the results for 
32
./data/CONSUMPTION/MEGA_CONS_list.Rdata\r\n\r\nTo get the results for 
            
28
the __optimization problem__, one needs to execute the file 
33
the __optimization problem__, one needs to execute the file 
            
29
analyze_data.R. It provides the functions to compare production and 
34
analyze_data.R. It provides the functions to compare production and 
            
30
consumption data, and to optimize for the different values (PV, 
35
consumption data, and to optimize for the different values (PV, 
            
31
MBC,).\r\n\r\nTo __reproduce the figures__ one needs to execute the 
36
MBC,).\r\n\r\nTo __reproduce the figures__ one needs to execute the 
            
32
file visualize_results.R. It provides the functions to reproduce the 
37
file visualize_results.R. It provides the functions to reproduce the 
            
33
figures.\r\n\r\nTo calculate the __solar radiation__ that is needed in 
38
figures.\r\n\r\nTo calculate the __solar radiation__ that is needed in 
            
34
the Section Production Data, follow file 
39
the Section Production Data, follow file 
            
35
calculate_total_radiation.R.\r\n\r\nTo reproduce the __radiation data 
40
calculate_total_radiation.R.\r\n\r\nTo reproduce the __radiation data 
            
36
from from ERA5__, that can be found in data.zip, do the following 
41
from from ERA5__, that can be found in data.zip, do the following 
            
37
steps: 1. 
42
steps: 1. 
            
38
copernicus.eu/cdsapp#!/dataset/reanalysis-era5-single-levels?tab=form)  
43
copernicus.eu/cdsapp#!/dataset/reanalysis-era5-single-levels?tab=form)  
            
39
- download the reanalysis datasets as GRIB file. For FDIR select 
44
- download the reanalysis datasets as GRIB file. For FDIR select 
            
40
\"Total sky direct solar radiation at surface\", for GHI select 
45
\"Total sky direct solar radiation at surface\", for GHI select 
            
41
\"Surface solar radiation downwards\", and for ALBEDO select 
46
\"Surface solar radiation downwards\", and for ALBEDO select 
            
42
\"Forecast albedo\". 2. convert GRIB to csv with the file 
47
\"Forecast albedo\". 2. convert GRIB to csv with the file 
            
43
era5toGRID.sh 3. convert the csv file to the data that is used in this 
48
era5toGRID.sh 3. convert the csv file to the data that is used in this 
            
44
paper with the file convert_year_to_grid.R\r\n",
49
paper with the file convert_year_to_grid.R\r\n",
            
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    "description": "Appelstra\u00dfe 9 a, 30167 Hannover, 
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    "description": "Appelstra\u00dfe 9 a, 30167 Hannover, 
            
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https://www.ikg.uni-hannover.de/",
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https://www.ikg.uni-hannover.de/",
            
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ni-hannover.de/fileadmin/_processed_/a/0/csm_IKG_Logo_0680c1122c.png",
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ni-hannover.de/fileadmin/_processed_/a/0/csm_IKG_Logo_0680c1122c.png",
            
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    "is_organization": true,
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    "name": "institut-fur-kartographie-und-geoinformatik",
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    "state": "active",
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    "title": "Institut f\u00fcr Kartographie und Geoinformatik",
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  "repository_name": "Leibniz University Hannover",
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  "repository_name": "Leibniz University Hannover",
            
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    {
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    {
            
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      "created": "2022-12-16T12:03:46.832283",
            
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      "description": "To get the __consumption model__ from Section 
76
      "description": "To get the __consumption model__ from Section 
            
72
3.1, one needs load execute the file consumption_data.R. Load the data 
77
3.1, one needs load execute the file consumption_data.R. Load the data 
            
73
for the 3 Phases ./data/CONSUMPTION/PL1.csv, PL2.csv, PL3.csv, 
78
for the 3 Phases ./data/CONSUMPTION/PL1.csv, PL2.csv, PL3.csv, 
            
74
transform the data and build the model (starting line 225). The final 
79
transform the data and build the model (starting line 225). The final 
            
75
consumption data can be found in one file for each year in 
80
consumption data can be found in one file for each year in 
            
76
./data/CONSUMPTION/MEGA_CONS_list.Rdata\r\n\r\nTo get the results for 
81
./data/CONSUMPTION/MEGA_CONS_list.Rdata\r\n\r\nTo get the results for 
            
77
the __optimization problem__, one needs to execute the file 
82
the __optimization problem__, one needs to execute the file 
            
78
analyze_data.R. It provides the functions to compare production and 
83
analyze_data.R. It provides the functions to compare production and 
            
79
consumption data, and to optimize for the different values (PV, 
84
consumption data, and to optimize for the different values (PV, 
            
80
MBC,).\r\n\r\nTo __reproduce the figures__ one needs to execute the 
85
MBC,).\r\n\r\nTo __reproduce the figures__ one needs to execute the 
            
81
file visualize_results.R. It provides the functions to reproduce the 
86
file visualize_results.R. It provides the functions to reproduce the 
            
82
figures.\r\n\r\nTo calculate the __solar radiation__ that is needed in 
87
figures.\r\n\r\nTo calculate the __solar radiation__ that is needed in 
            
83
the Section Production Data, follow file 
88
the Section Production Data, follow file 
            
84
calculate_total_radiation.R.\r\n\r\nTo reproduce the __radiation data 
89
calculate_total_radiation.R.\r\n\r\nTo reproduce the __radiation data 
            
85
from from ERA5__, that can be found in data.zip, do the following 
90
from from ERA5__, that can be found in data.zip, do the following 
            
86
steps: 1. 
91
steps: 1. 
            
87
copernicus.eu/cdsapp#!/dataset/reanalysis-era5-single-levels?tab=form)  
92
copernicus.eu/cdsapp#!/dataset/reanalysis-era5-single-levels?tab=form)  
            
88
- download the reanalysis datasets as GRIB file. For FDIR select 
93
- download the reanalysis datasets as GRIB file. For FDIR select 
            
89
\"Total sky direct solar radiation at surface\", for GHI select 
94
\"Total sky direct solar radiation at surface\", for GHI select 
            
90
\"Surface solar radiation downwards\", and for ALBEDO select 
95
\"Surface solar radiation downwards\", and for ALBEDO select 
            
91
\"Forecast albedo\". 2. convert GRIB to csv with the file 
96
\"Forecast albedo\". 2. convert GRIB to csv with the file 
            
92
era5toGRID.sh 3. convert the csv file to the data that is used in this 
97
era5toGRID.sh 3. convert the csv file to the data that is used in this 
            
93
paper with the file convert_year_to_grid.R",
98
paper with the file convert_year_to_grid.R",
            
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      "format": "TXT",
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      "hash": "",
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      "url": 
            
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1d97df/resource/b7498a91-e168-418e-953c-ef20ecfce7b1/download/readme",
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1d97df/resource/b7498a91-e168-418e-953c-ef20ecfce7b1/download/readme",
            
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      "created": "2022-12-16T12:04:45.484360",
            
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      "description": "Used to convert the data from one large CSV file 
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      "description": "Used to convert the data from one large CSV file 
            
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to the smaller data sets that we use in the code",
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to the smaller data sets that we use in the code",
            
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      "url": 
            
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6cd6e522-7283-47fc-81b5-0cbcfe0544c1/download/convert_year_to_grid.r",
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6cd6e522-7283-47fc-81b5-0cbcfe0544c1/download/convert_year_to_grid.r",
            
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      "created": "2022-12-16T12:05:49.229026",
            
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      "description": "This file that contains the optimization problem 
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      "description": "This file that contains the optimization problem 
            
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from Section 3. Can optimize for PV area, MBC, etc.",
144
from Section 3. Can optimize for PV area, MBC, etc.",
            
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      "format": "R",
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      "format": "R",
            
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      "hash": "",
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      "url": 
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      "url": 
            
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esource/e8756f50-8cb4-4306-a29e-add52a5430aa/download/analyze_data.r",
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esource/e8756f50-8cb4-4306-a29e-add52a5430aa/download/analyze_data.r",
            
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      "url_type": ""
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    },
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      "created": "2022-12-16T12:06:31.794852",
            
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      "description": "Used to gain the plots that are in the paper.",
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      "description": "Used to gain the plots that are in the paper.",
            
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      "format": "R",
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      "format": "R",
            
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      "hash": "",
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parameters for the 1221 respected points around Germany.",
            
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      "description": "Contains the data for the Radiation, 
            
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Consumption, Albedo.\r\nThe data is also available here 
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Consumption, Albedo.\r\nThe data is also available here 
            
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.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-single-levels?tab=form 
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.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-single-levels?tab=form 
            
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. It can be transformed with the provided code.\r\nThe data in this 
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. It can be transformed with the provided code.\r\nThe data in this 
            
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file is already transformed and can be used to run the code.",
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file is already transformed and can be used to run the code.",
            
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    {
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      "display_name": "Multi-Parameter Optimization",
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      "name": "Self-Sufficiency",
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    },
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    {
            
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      "display_name": "Time-Series Model",
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      "display_name": "Time-Series Model",
            
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      "id": "9a46f529-2f10-4555-ab90-971eb431ba30",
            
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      "name": "Time-Series Model",
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  ],
            
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  "terms_of_usage": "Yes",
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  "terms_of_usage": "Yes",
            
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  "title": "Solar self-sufficient households as a driving factor for 
391
  "title": "Solar self-sufficient households as a driving factor for 
            
386
sustainability transformation",
392
sustainability transformation",
            
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  "type": "vdataset",
393
  "type": "vdataset",
            
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  "url": 
394
  "url": 
            
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ent-households-as-a-driving-factor-for-sustainability-transformation",
395
ent-households-as-a-driving-factor-for-sustainability-transformation",
            
390
  "version": ""
396
  "version": ""
            
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}
397
}