hub/scripts/random_assignation.py

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Python
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"""
This project aims to assign energy systems archetype names to Montreal buildings.
The random assignation is based on statistical information extracted from different sources, being:
- For residential buildings:
- SHEU 2015: https://oee.nrcan.gc.ca/corporate/statistics/neud/dpa/menus/sheu/2015/tables.cfm
- For non-residential buildings:
- Montreal dataportal: https://dataportalforcities.org/north-america/canada/quebec/montreal
- https://www.eia.gov/consumption/commercial/data/2018/
"""
import json
import random
from hub.city_model_structure.building import Building
energy_systems_format = 'montreal_future'
# parameters:
residential_systems_percentage = {
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'Central Hydronic Air and Gas Source Heating System with Unitary Split Cooling and Air Source HP DHW and Grid Tied PV': 0,
'Central Hydronic Air and Electricity Source Heating System with Unitary Split Cooling and Air Source HP DHW and Grid Tied PV': 0,
'Central Hydronic Ground and Gas Source Heating System with Unitary Split Cooling and Air Source HP DHW and Grid Tied PV': 0,
'Central Hydronic Ground and Electricity Source Heating System with Unitary Split Cooling and Air Source HP DHW '
'and Grid Tied PV': 0,
'Central Hydronic Water and Gas Source Heating System with Unitary Split Cooling and Air Source HP DHW and Grid Tied PV': 0,
'Central Hydronic Water and Electricity Source Heating System with Unitary Split Cooling and Air Source HP DHW '
'and Grid Tied PV': 0,
'Central Hydronic Air and Gas Source Heating System with Unitary Split and Air Source HP DHW': 0,
'Central Hydronic Air and Electricity Source Heating System with Unitary Split and Air Source HP DHW': 0,
'Central Hydronic Ground and Gas Source Heating System with Unitary Split and Air Source HP DHW': 0,
'Central Hydronic Ground and Electricity Source Heating System with Unitary Split and Air Source HP DHW': 0,
'Central Hydronic Water and Gas Source Heating System with Unitary Split and Air Source HP DHW': 0,
'Central Hydronic Water and Electricity Source Heating System with Unitary Split and Air Source HP DHW': 0,
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'Grid Tied PV System': 10,
'system 1 gas': 0,
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'system 1 gas grid tied pv': 5,
'system 1 electricity': 0,
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'system 1 electricity grid tied pv': 10,
'system 2 gas': 0,
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'system 2 gas grid tied pv': 10,
'system 2 electricity': 0,
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'system 2 electricity grid tied pv': 10,
'system 3 and 4 gas': 0,
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'system 3 and 4 gas grid tied pv': 10,
'system 3 and 4 electricity': 0,
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'system 3 and 4 electricity grid tied pv': 5,
'system 6 gas': 0,
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'system 6 gas grid tied pv': 10,
'system 6 electricity': 0,
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'system 6 electricity grid tied pv': 10,
'system 8 gas': 0,
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'system 8 gas grid tied pv': 10,
'system 8 electricity': 0,
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'system 8 electricity grid tied pv': 10,
}
non_residential_systems_percentage = {'system 1 gas': 0,
'system 1 electricity': 0,
'system 2 gas': 0,
'system 2 electricity': 0,
'system 3 and 4 gas': 39,
'system 3 and 4 electricity': 36,
'system 5 gas': 0,
'system 5 electricity': 0,
'system 6 gas': 13,
'system 6 electricity': 12,
'system 8 gas': 0,
'system 8 electricity': 0}
def _retrieve_buildings(path, year_of_construction_field=None,
function_field=None, function_to_hub=None, aliases_field=None):
_buildings = []
with open(path, 'r', encoding='utf8') as json_file:
_geojson = json.loads(json_file.read())
for feature in _geojson['features']:
_building = {}
year_of_construction = None
if year_of_construction_field is not None:
year_of_construction = int(feature['properties'][year_of_construction_field])
function = None
if function_field is not None:
function = feature['properties'][function_field]
if function_to_hub is not None:
# use the transformation dictionary to retrieve the proper function
if function in function_to_hub:
function = function_to_hub[function]
building_name = ''
building_aliases = []
if 'id' in feature:
building_name = feature['id']
if aliases_field is not None:
for alias_field in aliases_field:
building_aliases.append(feature['properties'][alias_field])
_building['year_of_construction'] = year_of_construction
_building['function'] = function
_building['building_name'] = building_name
_building['building_aliases'] = building_aliases
_buildings.append(_building)
return _buildings
def call_random(_buildings: [Building], _systems_percentage):
_buildings_with_systems = []
_systems_distribution = []
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_selected_buildings = list(range(len(_buildings)))
random.shuffle(_selected_buildings)
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total = 0
maximum = 0
add_to = 0
for _system in _systems_percentage:
if _systems_percentage[_system] > 0:
number_of_buildings = round(_systems_percentage[_system] / 100 * len(_selected_buildings))
_systems_distribution.append({'system': _system, 'number': _systems_percentage[_system],
'number_of_buildings': number_of_buildings})
if number_of_buildings > maximum:
maximum = number_of_buildings
add_to = len(_systems_distribution) - 1
total += number_of_buildings
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missing = len(_selected_buildings) - total
if missing > 0:
_systems_distribution[add_to]['number_of_buildings'] += missing
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elif missing < 0:
for case in sorted(_systems_distribution, key=lambda x: -x['number_of_buildings']):
if case['number_of_buildings'] > 0:
reduce_by = min(-missing, case['number_of_buildings'])
case['number_of_buildings'] -= reduce_by
missing += reduce_by
if missing == 0:
break
total = sum(case['number_of_buildings'] for case in _systems_distribution)
assert total == len(_selected_buildings), f"Final total {total} does not match available {len(_selected_buildings)}"
_position = 0
for case in _systems_distribution:
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for _ in range(case['number_of_buildings']):
_buildings[_selected_buildings[_position]].energy_systems_archetype_name = case['system']
_position += 1
return _buildings