s_ranjbar
01cd78eb3e
The classes and scripts of costs library are copied in scripts folder fix: updating the energy system catalogue parameter importer fix: units are fixed in the sizing and simulation modules fix: adding costing workflow feat: new function created to store current and new system analysis results fix: updating the code to implement all the changes feat: new attributes added to energy system catalogue fix: samll bug in calculating capital cost of TES is solved feat: a new method for calculating peak dhw demand is created in building class fix: small bug in generation system class of CDM is fixed fix: small issues in current system simulation and sizing modules are resolved feat: new class called EnergySystemsSimulationFactory is created to handle all the system simulation models fix: the operational cost class is modified and completed fix: slight changes before merge fix: The simulation model for 1st archetype is modified. fix: small changes to building code that affect cost and total operational cost code feat: new attribute added to store fuel consumption values found from simulation fix: cleaning fix: redundant attributes removed from energy system data model feat: new setters added to classes Fix: codes modified to accommodate the changes feat: added cop curves for heating and cooling of HP and finalized the heating and cooling simulation models of archetype 1 feat: redundant files in the input folder are removed fix: cost values are checked and fixed fix: System analysis report is fixed fix: SRA fixed
114 lines
5.0 KiB
Python
114 lines
5.0 KiB
Python
"""
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This project aims to assign energy systems archetype names to Montreal buildings.
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The random assignation is based on statistical information extracted from different sources, being:
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- For residential buildings:
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- SHEU 2015: https://oee.nrcan.gc.ca/corporate/statistics/neud/dpa/menus/sheu/2015/tables.cfm
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- For non-residential buildings:
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- Montreal dataportal: https://dataportalforcities.org/north-america/canada/quebec/montreal
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- https://www.eia.gov/consumption/commercial/data/2018/
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"""
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import json
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import random
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from hub.city_model_structure.building import Building
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energy_systems_format = 'montreal_custom'
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# parameters:
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residential_systems_percentage = {'system 1 gas': 44,
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'system 1 electricity': 6,
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'system 2 gas': 0,
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'system 2 electricity': 0,
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'system 3 and 4 gas': 0,
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'system 3 and 4 electricity': 0,
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'system 5 gas': 0,
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'system 5 electricity': 0,
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'system 6 gas': 0,
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'system 6 electricity': 0,
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'system 8 gas': 44,
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'system 8 electricity': 6}
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residential_new_systems_percentage = {'PV+ASHP+GasBoiler+TES': 100,
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'PV+4Pipe+DHW': 0,
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'PV+ASHP+ElectricBoiler+TES': 0,
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'PV+GSHP+GasBoiler+TES': 0,
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'PV+GSHP+ElectricBoiler+TES': 0,
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'PV+WSHP+GasBoiler+TES': 0,
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'PV+WSHP+ElectricBoiler+TES': 0}
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non_residential_systems_percentage = {'system 1 gas': 0,
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'system 1 electricity': 0,
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'system 2 gas': 0,
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'system 2 electricity': 0,
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'system 3 and 4 gas': 39,
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'system 3 and 4 electricity': 36,
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'system 5 gas': 0,
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'system 5 electricity': 0,
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'system 6 gas': 13,
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'system 6 electricity': 12,
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'system 8 gas': 0,
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'system 8 electricity': 0}
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def _retrieve_buildings(path, year_of_construction_field=None,
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function_field=None, function_to_hub=None, aliases_field=None):
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_buildings = []
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with open(path, 'r', encoding='utf8') as json_file:
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_geojson = json.loads(json_file.read())
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for feature in _geojson['features']:
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_building = {}
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year_of_construction = None
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if year_of_construction_field is not None:
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year_of_construction = int(feature['properties'][year_of_construction_field])
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function = None
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if function_field is not None:
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function = feature['properties'][function_field]
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if function_to_hub is not None:
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# use the transformation dictionary to retrieve the proper function
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if function in function_to_hub:
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function = function_to_hub[function]
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building_name = ''
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building_aliases = []
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if 'id' in feature:
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building_name = feature['id']
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if aliases_field is not None:
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for alias_field in aliases_field:
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building_aliases.append(feature['properties'][alias_field])
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_building['year_of_construction'] = year_of_construction
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_building['function'] = function
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_building['building_name'] = building_name
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_building['building_aliases'] = building_aliases
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_buildings.append(_building)
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return _buildings
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def call_random(_buildings: [Building], _systems_percentage):
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_buildings_with_systems = []
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_systems_distribution = []
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_selected_buildings = list(range(0, len(_buildings)))
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random.shuffle(_selected_buildings)
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total = 0
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maximum = 0
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add_to = 0
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for _system in _systems_percentage:
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if _systems_percentage[_system] > 0:
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number_of_buildings = round(_systems_percentage[_system] / 100 * len(_selected_buildings))
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_systems_distribution.append({'system': _system, 'number': _systems_percentage[_system],
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'number_of_buildings': number_of_buildings})
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if number_of_buildings > maximum:
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maximum = number_of_buildings
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add_to = len(_systems_distribution) - 1
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total += number_of_buildings
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missing = 0
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if total != len(_selected_buildings):
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missing = len(_selected_buildings) - total
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if missing != 0:
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_systems_distribution[add_to]['number_of_buildings'] += missing
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_position = 0
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for case in _systems_distribution:
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for i in range(0, case['number_of_buildings']):
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_buildings[_selected_buildings[_position]].energy_systems_archetype_name = case['system']
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_position += 1
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return _buildings
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