feat: add README.md file
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README.md
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README.md
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## 1. Creating the City Model from a GeoJSON File
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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.
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```python
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city = GeometryFactory(
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"geojson",
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input_file,
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height_field="height",
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year_of_construction_field="contr_year",
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function_field="function_c",
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adjacency_field="adjacency",
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function_to_hub=Dictionaries().montreal_function_to_hub_function
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).city
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```
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- `"geojson"`: Specifies the format of the input file.
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- `input_file`: The path to your GeoJSON file.
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- `height_field="height"`: The attribute in the GeoJSON file that contains the building height.
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- `year_of_construction_field="contr_year"`: The attribute for the building's year of construction.
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- `function_field="function_c"`: The attribute for the building's function or usage.
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- `adjacency_field="adjacency"`: The attribute indicating whether the building is attached or detached.
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- `function_to_hub`: A dictionary that maps the building functions to standardized functions used by HUB.
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The `city` object now contains all the buildings from the GeoJSON file with their associated attributes.
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---
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## 2. Enriching the City with Construction Details
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Use the `ConstructionFactory` to enrich the city model with construction details and calculation of the thermal zones and total floor area.
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```python
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ConstructionFactory('nrcan', city).enrich()
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```
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- `'nrcan'`: Specifies the source of construction data, in this case, NRCan.
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- `city`: The city model created in the previous step.
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---
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## 3. Assigning Energy Demands Using Archetypes
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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.
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```python
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ResultFactory('archetypes', city, demand_file).enrich()
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```
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- `'archetypes'`: Specifies the handler we are using which in this case is archetype-based demand model.
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- `city`: The city.
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- `demand_file`: The path to the CSV file containing the archetype demand profiles.
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---
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## 4. Rules for Finding Archetypes and Assigning Demands
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This mapping is based on the following building attributes:
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- **Function**
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- **Height**
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- **Adjacency**: Whether the building is attached or is detached.
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- **Year of Construction**
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### **Mapping Rules**:
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Based on the building's function, height, and adjacency:
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- **Residential Buildings** ('residential', 'multifamily house', 'single family house'):
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- **Single Family House**:
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- Height less than 6 meters.
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- Adjacency: 'detached'.
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- `usage = 'Single Family'`.
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- **Row House**:
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- Height less than 6 meters.
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- Adjacency: 'attached'.
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- `usage = 'Row house'`.
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- **Duplex/Triplex**:
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- Height between 6 and 10 meters.
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- Adjacency: 'detached'.
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- `usage = 'Duplex/triplex'`.
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- **Small MURBs** (Multi-Unit Residential Buildings):
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- Height between 6 and 10 meters.
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- Adjacency: 'attached'.
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- `usage = 'Small MURBs'`.
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- **Medium MURBs**:
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- Height between 10 and 15 meters.
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- `usage = 'Medium MURBs'`.
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- **Large MURBs**:
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- Height greater than 15 meters.
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- `usage = 'Large MURBs'`.
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- **Office Buildings** ('office', 'office and administration'):
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- `usage = 'Office'`.
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- **Commercial Buildings** ('commercial', 'retail shop without refrigerated food', 'retail shop with refrigerated food', 'stand alone retail', 'strip mall'):
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- If adjacency is 'attached':
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- `usage = 'Commercial attached'`.
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- Else:
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- `usage = 'Commercial detached'`.
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#### Determine Building Vintage (`vintage`):
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Based on the building's year of construction:
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- **Pre-1947**:
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- `year <= 1947`.
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- `vintage = 'Pre 1947'`.
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- **1947-1983**:
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- `1947 < year <= 1983`.
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- `vintage = '1947-1983'`.
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- **1984-2010**:
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- `1983 < year <= 2010`.
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- `vintage = '1984-2010'`.
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- **Post-2010**:
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- `year > 2010`.
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- `vintage = 'Post 2010'`.
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Combine usage and vintage to create the key:
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```python
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archetype_key = f"{usage} {vintage}"
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```
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This key is used to look up the corresponding demand profiles in the archetype demand file.
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---
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### Assigning Demands:
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1. **Lookup Demand Profiles**:
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Using the `archetype_key`, retrieve the hourly demand profiles from the `demand_file`. Each archetype has associated hourly demands for different energy uses.
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2. **Scale Demands by Floor Area**:
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Multiply the demand values by the building's total floor area.
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3. **Assign Demands to Building**:
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Set the building's demand attributes:
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```python
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building.heating_demand = [value * area for value in demand['Heating']]
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building.cooling_demand = [value * area for value in demand['Cooling']]
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building.domestic_hot_water_heat_demand = [value * area for value in demand['DHW']]
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building.appliances_electrical_demand = [value * area for value in demand['Equipment']]
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building.lighting_electrical_demand = [value * area for value in demand['Lighting']]
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```
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---
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### Example:
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For a residential building with the following attributes:
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- **Function**: 'residential'
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- **Height**: 9 meters
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- **Adjacency**: 'attached'
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- **Year of Construction**: 1986
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#### **Mapping Steps**:
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1. **Usage**:
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- Height is between 6 and 10 meters.
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- Adjacency is 'attached'.
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- Therefore, `usage = 'Small MURBs'`.
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2. **Vintage**:
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- Year is 1986, which falls in 1984-2010.
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- Therefore, `vintage = '1984-2010'`.
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3. **Archetype Key**:
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- `archetype_key = 'Small MURBs 1984-2010'`.
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