Final version summercourse with final graphs. Pie chart mistake correction

input_files/eilat.geojson

@@ -0,0 +1,249 @@
+{
+  "type": "FeatureCollection",
+  "features": [
+    {
+      "type": "Feature",
+      "id": 1,
+      "properties": {
+        "heightmax": 9,
+        "ANNEE_CONS": 1978,
+        "CODE_UTILI": "residential"
+	  },
+      "geometry": {
+        "coordinates": [
+          [
+            [
+              34.95217088371581,
+              29.56694805860026
+            ],
+            [
+              34.95262396587913,
+              29.566952667742285
+            ],
+            [
+              34.95261999147337,
+              29.567024109421467
+            ],
+            [
+              34.952169558914704,
+              29.567019500282157
+            ],
+            [
+              34.95217088371581,
+              29.56694805860026
+            ]
+          ]
+        ],
+        "type": "Polygon"
+      }
+    },
+	{
+      "type": "Feature",
+      "id": 3,
+      "properties": {
+		"heightmax": 16,
+        "ANNEE_CONS": 2012,
+        "CODE_UTILI": "dormitory"
+	  },
+      "geometry": {
+        "coordinates": [
+          [
+            [
+              34.95176644317411,
+              29.56827388702702
+            ],
+            [
+              34.95176550020565,
+              29.568180388329026
+            ],
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+              29.568180388329026
+            ],
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+              34.95179850408434,
+              29.5681303582886
+            ],
+            [
+              34.95176644317411,
+              29.5681303582886
+            ],
+            [
+              34.95176644317411,
+              29.568038499789708
+            ],
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+              29.568038499789708
+            ],
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+              34.951874884488376,
+              29.568058183760357
+            ],
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+              29.568058183760357
+            ],
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+              29.56804178045124
+            ],
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+              29.568042600617147
+            ],
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+              29.568129538124154
+            ],
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+              29.5681303582886
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+              29.568176287507143
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+              29.568274707190284
+            ],
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+              29.56827388702702
+            ]
+          ]
+        ],
+        "type": "Polygon"
+      }
+    },
+    {
+      "type": "Feature",
+      "id": 2,
+      "properties": {
+		"heightmax": 24,
+        "ANNEE_CONS": 2002,
+        "CODE_UTILI": "Hotel employ"
+	  },
+      "geometry": {
+        "coordinates": [
+          [
+            [
+              34.94972280674813,
+              29.566224752287738
+            ],
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+              29.56597561012454
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+              29.565980668855033
+            ],
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+              29.566233605043536
+            ],
+            [
+              34.94972280674813,
+              29.566224752287738
+            ]
+          ]
+        ],
+        "type": "Polygon"
+      }
+    },
+    {
+      "type": "Feature",
+      "id": 4,
+      "properties": {
+		"heightmax": 24,
+        "ANNEE_CONS": 2002,
+        "CODE_UTILI": "Hotel employ"
+	  },
+      "geometry": {
+        "coordinates": [
+          [
+            [
+              34.9492572120038,
+              29.565899295950373
+            ],
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+              34.949411568175805,
+              29.565906093785898
+            ],
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+              29.56615761339161
+            ],
+            [
+              34.94924158099852,
+              29.566149116117728
+            ],
+            [
+              34.9492572120038,
+              29.565899295950373
+            ]
+          ]
+        ],
+        "type": "Polygon"
+      }
+    },
+    {
+      "type": "Feature",
+      "id": 5,
+      "properties": {
+		"heightmax": 9,
+        "ANNEE_CONS": 1978,
+        "CODE_UTILI": "residential"
+	  },
+      "geometry": {
+        "coordinates": [
+          [
+            [
+              34.95213398400861,
+              29.567197394967593
+            ],
+            [
+              34.95258172690612,
+              29.56720586098278
+            ],
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+              34.95258172690612,
+              29.56727217807996
+            ],
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+              34.952132361751325,
+              29.56726935607685
+            ],
+            [
+              34.95213398400861,
+              29.567197394967593
+            ]
+          ]
+        ],
+        "type": "Polygon"
+      }
+    }
+  ]
+}

main.py

@@ -3,7 +3,6 @@ from pathlib import Path
 from hub.imports.geometry_factory import GeometryFactory
 from hub.imports.construction_factory import ConstructionFactory
 from hub.imports.usage_factory import UsageFactory
-from hub.imports.weather_factory import WeatherFactory
 from hub.helpers.dictionaries import Dictionaries
 from hub.imports.energy_systems_factory import EnergySystemsFactory
 import hub.helpers.constants as cte
@@ -13,14 +12,11 @@ from sra_engine import SraEngine
 
 
 try:
-  file_path = (Path(__file__).parent / 'input_files' / '195_v1.geojson')
+  file_path = (Path(__file__).parent / 'input_files' / 'eilat.geojson')
   climate_reference_city = 'Montreal'
-  weather_format = 'epw'
+  construction_format = 'eilat'
-  construction_format = 'nrcan'
+  usage_format = 'eilat'
-  usage_format = 'nrcan'
   energy_systems_format = 'montreal_custom'
-  attic_heated_case = 0
-  basement_heated_case = 1
 
   out_path = (Path(__file__).parent / 'output_files')
   tmp_folder = (Path(__file__).parent / 'tmp')
@@ -31,12 +27,9 @@ try:
                          height_field='heightmax',
                          year_of_construction_field='ANNEE_CONS',
                          function_field='CODE_UTILI',
-                         function_to_hub=Dictionaries().montreal_function_to_hub_function).city
+                         function_to_hub=Dictionaries().eilat_function_to_hub_function).city
-  city.climate_reference_city = climate_reference_city
+
-  city.climate_file = (tmp_folder / f'{climate_reference_city}.cli').resolve()
   print(f'city created from {file_path}')
-  WeatherFactory(weather_format, city).enrich()
-  print('enrich weather... done')
   ConstructionFactory(construction_format, city).enrich()
   print('enrich constructions... done')
   UsageFactory(usage_format, city).enrich()

peak_load_class.py

@@ -0,0 +1,44 @@
+from pathlib import Path
+import pandas as pd
+import hub.helpers.constants as cte
+
+def Peak_load (building):
+  array = [None] * 12
+  heating = 0
+  cooling = 0
+  for system in building.energy_systems:
+    for demand_type in system.demand_types:
+      if demand_type == cte.HEATING:
+        heating = 1
+      if demand_type == cte.COOLING:
+        cooling = 1
+  if cte.MONTH in building.heating_peak_load.keys() and cte.MONTH in building.cooling_peak_load.keys():
+    peak_lighting = 0
+    peak_appliances = 0
+    for thermal_zone in building.internal_zones[0].thermal_zones:
+      lighting = thermal_zone.lighting
+      for schedule in lighting.schedules:
+        for value in schedule.values:
+          if value * lighting.density * thermal_zone.total_floor_area > peak_lighting:
+            peak_lighting = value * lighting.density * thermal_zone.total_floor_area
+      appliances = thermal_zone.appliances
+      for schedule in appliances.schedules:
+        for value in schedule.values:
+          if value * appliances.density * thermal_zone.total_floor_area > peak_appliances:
+            peak_appliances = value * appliances.density * thermal_zone.total_floor_area
+
+    monthly_electricity_peak = [0.9 * peak_lighting + 0.7 * peak_appliances] * 12
+    conditioning_peak = []
+    for i, value in enumerate(building.heating_peak_load[cte.MONTH]):
+      if cooling * building.cooling_peak_load[cte.MONTH][i] > heating * value:
+        conditioning_peak.append(cooling * building.cooling_peak_load[cte.MONTH][i])
+      else:
+        conditioning_peak.append(heating * value)
+      monthly_electricity_peak[i] += 0.8 * conditioning_peak[i]
+
+    electricity_peak_load_results = pd.DataFrame(monthly_electricity_peak
+                                                 , columns=[f'{building.name} electricity peak load W'])
+  else:
+    electricity_peak_load_results = pd.DataFrame(array, columns=[f'{building.name} electricity peak load W'])
+
+    return electricity_peak_load_results

results.py

@@ -151,8 +151,8 @@ class Results:
     with open(full_path_metadata, 'w') as metadata_file:
       metadata_file.write(file)
 
-    def outputsforgraph(self):
+  def outputsforgraph(self):
-      file = 'city name: ' + self._city.name + '\n'
+      outputs_energy =pd.DataFrame()
       array = [None] * 12
       for building in self._city.buildings:
         if cte.MONTH in building.heating_demand.keys():
@@ -262,36 +262,20 @@ class Results:
                                                       columns=[
                                                         f'{building.name} electrical consumption for distribution Wh'])
 
-        if print_results is None:
+        listgraph = [lighting_results.values.sum(),
-          print_results = heating_results
+                     appliances_results.values.sum(),
-        else:
+                     heating_consumption_results.values.sum(),
-          print_results = pd.concat([print_results, heating_results], axis='columns')
+                     cooling_consumption_results.values.sum(),
-        print_results = pd.concat([print_results,
+                     dhw_consumption_results.values.sum(),
-                                   cooling_results,
+                     extra_electrical_consumption.values.sum()
-                                   lighting_results,
+                    ]
-                                   appliances_results,
+        outputs_energy[f'building {building.name}'] = listgraph
-                                   dhw_results,
+        outputs_energy.index = ['Lighting consumption', 'Appliances consumption', 'Heating consumption',
-                                   heating_consumption_results,
+                                'Cooling consumption', 'DHW consumption', 'Extra electrical consumption']
-                                   cooling_consumption_results,
+        total_final_energy = (lighting_results.values.sum() + appliances_results.values.sum() + \
-                                   dhw_consumption_results,
+                            heating_consumption_results.values.sum() + cooling_consumption_results.values.sum() + \
-                                   heating_peak_load_results,
+                            dhw_consumption_results.values.sum() + extra_electrical_consumption.values.sum())/1000
-                                   cooling_peak_load_results,
+
-                                   electricity_peak_load_results,
+      return outputs_energy, total_final_energy
-                                   onsite_electrical_production,
+
-                                   extra_electrical_consumption], axis='columns')
-        file += '\n'
-        file += f'name: {building.name}\n'
-        file += f'year of construction: {building.year_of_construction}\n'
-        file += f'function: {building.function}\n'
-        file += f'floor area: {building.floor_area}\n'
-        if building.average_storey_height is not None and building.eave_height is not None:
-          file += f'storeys: {int(building.eave_height / building.average_storey_height)}\n'
-        else:
-          file += f'storeys: n/a\n'
-        file += f'volume: {building.volume}\n'
 
-      full_path_results = Path(self._path / 'demand.csv').resolve()
-      print_results.to_csv(full_path_results, na_rep='null')
-      full_path_metadata = Path(self._path / 'metadata.csv').resolve()
-      with open(full_path_metadata, 'w') as metadata_file:
-        metadata_file.write(file)