city_retrofit/imports/usage/comnet_usage_parameters.py
2022-03-08 20:08:03 -05:00

155 lines
6.6 KiB
Python

"""
ComnetUsageParameters model the usage properties
SPDX - License - Identifier: LGPL - 3.0 - or -later
Copyright © 2021 Project Author Pilar Monsalvete Alvarez de Uribarri pilar.monsalvete@concordia.ca
"""
import sys
from typing import Dict
import pandas as pd
import helpers.constants as cte
from helpers.configuration_helper import ConfigurationHelper as ch
from imports.geometry.helpers.geometry_helper import GeometryHelper
from imports.usage.helpers.usage_helper import UsageHelper
from city_model_structure.building_demand.usage_zone import UsageZone
from city_model_structure.building_demand.lighting import Lighting
from city_model_structure.building_demand.occupancy import Occupancy
from city_model_structure.building_demand.appliances import Appliances
from city_model_structure.building_demand.internal_gains import InternalGains
class ComnetUsageParameters:
"""
ComnetUsageParameters class
"""
def __init__(self, city, base_path):
self._city = city
self._base_path = str(base_path / 'comnet_archetypes.xlsx')
self._usage_archetypes = []
data = self._read_file()
for item in data['lighting']:
for usage in UsageHelper.usage_to_comnet:
comnet_usage = UsageHelper.usage_to_comnet[usage]
if comnet_usage == item:
usage_archetype = self._parse_zone_usage_type(comnet_usage, data)
self._usage_archetypes.append(usage_archetype)
def _read_file(self) -> Dict:
"""
reads xlsx file containing usage information into a dictionary
:return : Dict
"""
number_usage_types = 33
xl_file = pd.ExcelFile(self._base_path)
file_data = pd.read_excel(xl_file, sheet_name="Modeling Data", skiprows=[0, 1, 2],
nrows=number_usage_types, usecols="A:Z")
lighting_data = {}
plug_loads_data = {}
occupancy_data = {}
ventilation_rate = {}
water_heating = {}
process_data = {}
for j in range(0, number_usage_types):
usage_parameters = file_data.iloc[j]
usage_type = usage_parameters[0]
lighting_data[usage_type] = usage_parameters[1:6].values.tolist()
plug_loads_data[usage_type] = usage_parameters[8:13].values.tolist()
occupancy_data[usage_type] = usage_parameters[17:20].values.tolist()
ventilation_rate[usage_type] = usage_parameters[20:21].values.tolist()
water_heating[usage_type] = usage_parameters[23:24].values.tolist()
process_data[usage_type] = usage_parameters[24:26].values.tolist()
return {'lighting': lighting_data,
'plug loads': plug_loads_data,
'occupancy': occupancy_data,
'ventilation rate': ventilation_rate,
'water heating': water_heating,
'process': process_data}
@staticmethod
def _parse_zone_usage_type(usage, data):
_usage_zone = UsageZone()
_usage_zone.usage = usage
# lighting
_lighting = Lighting()
_lighting.latent_fraction = ch().comnet_lighting_latent
_lighting.convective_fraction = ch().comnet_lighting_convective
_lighting.radiative_fraction = ch().comnet_lighting_radiant
_lighting.average_internal_gain = data['lighting'][usage][4]
# plug loads
_appliances = None
if data['plug loads'][usage][0] != 'n.a.':
_appliances = Appliances()
_appliances.latent_fraction = ch().comnet_plugs_latent
_appliances.convective_fraction = ch().comnet_plugs_convective
_appliances.radiative_fraction = ch().comnet_plugs_radiant
_appliances.average_internal_gain = data['plug loads'][usage][0]
# occupancy
_occupancy = Occupancy()
_occupancy.occupancy_density = data['occupancy'][usage][0]
_occupancy.sensible_convective_internal_gain = data['occupancy'][usage][1] \
* ch().comnet_occupancy_sensible_convective
_occupancy.sensible_radiant_internal_gain = data['occupancy'][usage][1] * ch().comnet_occupancy_sensible_radiant
_occupancy.latent_internal_gain = data['occupancy'][usage][2]
if _occupancy.occupancy_density <= 0:
_usage_zone.mechanical_air_change = 0
else:
_usage_zone.mechanical_air_change = data['ventilation rate'][usage][0] / _occupancy.occupancy_density
_usage_zone.occupancy = _occupancy
_usage_zone.lighting = _lighting
_usage_zone.appliances = _appliances
return _usage_zone
def enrich_buildings(self):
"""
Returns the city with the usage parameters assigned to the buildings
:return:
"""
city = self._city
for building in city.buildings:
usage = GeometryHelper.usage_from_function(building.function)
archetype = self._search_archetype(UsageHelper.comnet_from_usage(usage))
if archetype is None:
sys.stderr.write(f'Building {building.name} has unknown archetype for building function:'
f' {building.function}, that assigns building usage as '
f'{GeometryHelper.usage_from_function(building.function)}\n')
continue
for internal_zone in building.internal_zones:
usage_zone = UsageZone()
usage_zone.usage = usage
self._assign_values(usage_zone, archetype, volume_per_area)
usage_zone.percentage = 1
internal_zone.usage_zones = [usage_zone]
def _search_archetype(self, building_usage):
for building_archetype in self._usage_archetypes:
if building_archetype.usage == building_usage:
return building_archetype
return None
@staticmethod
def _assign_values(usage_zone, archetype, volume_per_area):
# Due to the fact that python is not a typed language, the wrong object type is assigned to
# usage_zone.internal_gains when writing usage_zone.internal_gains = archetype.internal_gains.
# Therefore, this walk around has been done.
internal_gains = []
for archetype_internal_gain in archetype.internal_gains:
internal_gain = InternalGains()
internal_gain.type = archetype_internal_gain.type
internal_gain.average_internal_gain = archetype_internal_gain.average_internal_gain
internal_gain.convective_fraction = archetype_internal_gain.convective_fraction
internal_gain.radiative_fraction = archetype_internal_gain.radiative_fraction
internal_gain.latent_fraction = archetype_internal_gain.latent_fraction
internal_gains.append(internal_gain)
usage_zone.internal_gains = internal_gains
usage_zone.occupancy_density = archetype.occupancy_density * cte.METERS_TO_FEET**2
usage_zone.mechanical_air_change = archetype.mechanical_air_change * cte.METERS_TO_FEET**2 \
* cte.HOUR_TO_MINUTES / cte.METERS_TO_FEET**3 / volume_per_area