1. Creating the City Model from a GeoJSON File

Start by using the GeometryFactory class to create a city model from a GeoJSON file. This class reads building geometries and attributes from the file and constructs a city model.

city = GeometryFactory(
  "geojson",
  input_file,
  height_field="height",
  year_of_construction_field="contr_year",
  function_field="function_c",
  adjacency_field="adjacency",
  function_to_hub=Dictionaries().montreal_function_to_hub_function
).city

• "geojson": Specifies the format of the input file.
• input_file: The path to your GeoJSON file.
• height_field="height": The attribute in the GeoJSON file that contains the building height.
• year_of_construction_field="contr_year": The attribute for the building's year of construction.
• function_field="function_c": The attribute for the building's function or usage.
• adjacency_field="adjacency": The attribute indicating whether the building is attached or detached.
• function_to_hub: A dictionary that maps the building functions to standardized functions used by HUB.

The city object now contains all the buildings from the GeoJSON file with their associated attributes.

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2. Enriching the City with Construction Details

Use the ConstructionFactory to enrich the city model with construction details and calculation of the thermal zones and total floor area.

ConstructionFactory('nrcan', city).enrich()

• 'nrcan': Specifies the source of construction data, in this case, NRCan.
• city: The city model created in the previous step.

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3. Assigning Energy Demands Using Archetypes

Finally, we assign energy demands to each building using the ResultFactory class and an archetype-based demand model. This basically maps buildings to predefined archetypes and assigning corresponding energy demand profiles.

ResultFactory('archetypes', city, demand_file).enrich()

• 'archetypes': Specifies the handler we are using which in this case is archetype-based demand model.
• city: The city.
• demand_file: The path to the CSV file containing the archetype demand profiles.

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4. Rules for Finding Archetypes and Assigning Demands

This mapping is based on the following building attributes:

• Function
• Height
• Adjacency: Whether the building is attached or is detached.
• Year of Construction

Mapping Rules:

Based on the building's function, height, and adjacency:

• Residential Buildings ('residential', 'multifamily house', 'single family house'):

  • Single Family House:
    • Height less than 6 meters.
    • Adjacency: 'detached'.
    • usage = 'Single Family'.
  • Row House:
    • Height less than 6 meters.
    • Adjacency: 'attached'.
    • usage = 'Row house'.
  • Duplex/Triplex:
    • Height between 6 and 10 meters.
    • Adjacency: 'detached'.
    • usage = 'Duplex/triplex'.
  • Small MURBs (Multi-Unit Residential Buildings):
    • Height between 6 and 10 meters.
    • Adjacency: 'attached'.
    • usage = 'Small MURBs'.
  • Medium MURBs:
    • Height between 10 and 15 meters.
    • usage = 'Medium MURBs'.
  • Large MURBs:
    • Height greater than 15 meters.
    • usage = 'Large MURBs'.

• Office Buildings ('office', 'office and administration'):

  • usage = 'Office'.

• Commercial Buildings ('commercial', 'retail shop without refrigerated food', 'retail shop with refrigerated food', 'stand alone retail', 'strip mall'):

  • If adjacency is 'attached':
    • usage = 'Commercial attached'.
  • Else:
    • usage = 'Commercial detached'.

Determine Building Vintage (vintage):

Based on the building's year of construction:

• Pre-1947:
  • year <= 1947.
  • vintage = 'Pre 1947'.
• 1947-1983:
  • 1947 < year <= 1983.
  • vintage = '1947-1983'.
• 1984-2010:
  • 1983 < year <= 2010.
  • vintage = '1984-2010'.
• Post-2010:
  • year > 2010.
  • vintage = 'Post 2010'.

Combine usage and vintage to create the key:

archetype_key = f"{usage} {vintage}"

This key is used to look up the corresponding demand profiles in the archetype demand file.

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Assigning Demands:

1. Lookup Demand Profiles:

   Using the archetype_key, retrieve the hourly demand profiles from the demand_file. Each archetype has associated hourly demands for different energy uses.

2. Scale Demands by Floor Area:

   Multiply the demand values by the building's total floor area.

3. Assign Demands to Building:

   Set the building's demand attributes:

   building.heating_demand = [value * area for value in demand['Heating']]
   building.cooling_demand = [value * area for value in demand['Cooling']]
   building.domestic_hot_water_heat_demand = [value * area for value in demand['DHW']]
   building.appliances_electrical_demand = [value * area for value in demand['Equipment']]
   building.lighting_electrical_demand = [value * area for value in demand['Lighting']]
   

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Example:

For a residential building with the following attributes:

• Function: 'residential'
• Height: 9 meters
• Adjacency: 'attached'
• Year of Construction: 1986

Mapping Steps:

1. Usage:

   • Height is between 6 and 10 meters.
   • Adjacency is 'attached'.
   • Therefore, usage = 'Small MURBs'.

2. Vintage:

   • Year is 1986, which falls in 1984-2010.
   • Therefore, vintage = '1984-2010'.

3. Archetype Key:

   • archetype_key = 'Small MURBs 1984-2010'.