345 lines
16 KiB
Python
345 lines
16 KiB
Python
"""
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ComnetUsageParameters model the usage properties
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SPDX - License - Identifier: LGPL - 3.0 - or -later
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Copyright © 2022 Concordia CERC group
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Project Coder Pilar Monsalvete Alvarez de Uribarri pilar.monsalvete@concordia.ca
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"""
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import copy
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import sys
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from typing import Dict
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import pandas as pd
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import numpy
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import helpers.constants as cte
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from helpers.configuration_helper import ConfigurationHelper as ch
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from imports.geometry.helpers.geometry_helper import GeometryHelper
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from imports.usage.helpers.usage_helper import UsageHelper
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from imports.schedules.helpers.schedules_helper import SchedulesHelper
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from city_model_structure.building_demand.usage_zone import UsageZone
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from city_model_structure.building_demand.lighting import Lighting
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from city_model_structure.building_demand.occupancy import Occupancy
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from city_model_structure.building_demand.appliances import Appliances
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from city_model_structure.building_demand.thermal_control import ThermalControl
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from city_model_structure.attributes.schedule import Schedule
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from city_model_structure.building_demand.internal_gain import InternalGain
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class ComnetUsageParameters:
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"""
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ComnetUsageParameters class
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"""
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def __init__(self, city, base_path):
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self._city = city
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self._base_path = str(base_path / 'comnet_archetypes.xlsx')
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self._data = self._read_file()
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self._comnet_schedules_path = str(base_path / 'comnet_schedules_archetypes.xlsx')
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self._xls = pd.ExcelFile(self._comnet_schedules_path)
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def _read_file(self) -> Dict:
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"""
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reads xlsx files containing usage information into a dictionary
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:return : Dict
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"""
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number_usage_types = 33
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xl_file = pd.ExcelFile(self._base_path)
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file_data = pd.read_excel(xl_file, sheet_name="Modeling Data", skiprows=[0, 1, 2],
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nrows=number_usage_types, usecols="A:AB")
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lighting_data = {}
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plug_loads_data = {}
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occupancy_data = {}
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ventilation_rate = {}
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water_heating = {}
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process_data = {}
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schedules_key = {}
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for j in range(0, number_usage_types):
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usage_parameters = file_data.iloc[j]
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usage_type = usage_parameters[0]
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lighting_data[usage_type] = usage_parameters[1:6].values.tolist()
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plug_loads_data[usage_type] = usage_parameters[8:13].values.tolist()
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occupancy_data[usage_type] = usage_parameters[17:20].values.tolist()
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ventilation_rate[usage_type] = usage_parameters[20:21].values.tolist()
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water_heating[usage_type] = usage_parameters[23:24].values.tolist()
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process_data[usage_type] = usage_parameters[24:26].values.tolist()
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schedules_key[usage_type] = usage_parameters[27:28].values.tolist()
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return {'lighting': lighting_data,
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'plug loads': plug_loads_data,
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'occupancy': occupancy_data,
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'ventilation rate': ventilation_rate,
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'water heating': water_heating,
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'process': process_data,
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'schedules_key': schedules_key}
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@staticmethod
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def _parse_usage_type(comnet_usage, data, schedules_data):
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_usage_zone = UsageZone()
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# lighting
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_lighting = Lighting()
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_lighting.latent_fraction = ch().comnet_lighting_latent
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_lighting.convective_fraction = ch().comnet_lighting_convective
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_lighting.radiative_fraction = ch().comnet_lighting_radiant
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_lighting.density = data['lighting'][comnet_usage][4]
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# plug loads
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_appliances = None
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if data['plug loads'][comnet_usage][0] != 'n.a.':
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_appliances = Appliances()
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_appliances.latent_fraction = ch().comnet_plugs_latent
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_appliances.convective_fraction = ch().comnet_plugs_convective
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_appliances.radiative_fraction = ch().comnet_plugs_radiant
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_appliances.density = data['plug loads'][comnet_usage][0]
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# occupancy
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_occupancy = Occupancy()
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value = data['occupancy'][comnet_usage][0]
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_occupancy.occupancy_density = 0
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if value != 0:
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_occupancy.occupancy_density = 1 / value
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_occupancy.sensible_convective_internal_gain = data['occupancy'][comnet_usage][1] \
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* ch().comnet_occupancy_sensible_convective
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_occupancy.sensible_radiative_internal_gain = data['occupancy'][comnet_usage][1] \
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* ch().comnet_occupancy_sensible_radiant
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_occupancy.latent_internal_gain = data['occupancy'][comnet_usage][2]
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_usage_zone.mechanical_air_change = data['ventilation rate'][comnet_usage][0]
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schedules_usage = UsageHelper.schedules_key(data['schedules_key'][comnet_usage][0])
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_extracted_data = pd.read_excel(schedules_data, sheet_name=schedules_usage,
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skiprows=[0, 1, 2, 3], nrows=39, usecols="A:AA")
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schedules = []
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number_of_schedule_types = 13
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schedules_per_schedule_type = 3
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day_types = dict({'week_day': 0, 'saturday': 1, 'sunday': 2})
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for schedule_types in range(0, number_of_schedule_types):
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name = ''
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data_type = ''
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for schedule_day in range(0, schedules_per_schedule_type):
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_schedule = Schedule()
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_schedule.time_step = cte.HOUR
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_schedule.time_range = cte.DAY
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row_cells = _extracted_data.iloc[schedules_per_schedule_type * schedule_types + schedule_day]
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if schedule_day == day_types['week_day']:
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name = row_cells[0]
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data_type = row_cells[1]
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_schedule.day_types = [cte.MONDAY, cte.TUESDAY, cte.WEDNESDAY, cte.THURSDAY, cte.FRIDAY]
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elif schedule_day == day_types['saturday']:
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_schedule.day_types = [cte.SATURDAY]
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else:
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_schedule.day_types = [cte.SUNDAY, cte.HOLIDAY]
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_schedule.type = name
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_schedule.data_type = SchedulesHelper.data_type_from_comnet(data_type)
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if _schedule.data_type == cte.TEMPERATURE:
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values = []
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for cell in row_cells[schedules_per_schedule_type:].to_numpy():
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values.append((float(cell) - 32.) * 5 / 9)
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_schedule.values = values
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else:
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_schedule.values = row_cells[schedules_per_schedule_type:].to_numpy()
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schedules.append(_schedule)
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schedules_types = dict({'Occupancy': 0, 'Lights': 3, 'Receptacle': 6, 'Infiltration': 9, 'HVAC Avail': 12,
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'ClgSetPt': 15, 'HtgSetPt': 18})
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_schedules = []
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for pointer in range(0, 3):
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_schedules.append(schedules[schedules_types['Occupancy']+pointer])
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_occupancy.occupancy_schedules = _schedules
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_schedules = []
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for pointer in range(0, 3):
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_schedules.append(schedules[schedules_types['Lights']+pointer])
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_lighting.schedules = _schedules
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_schedules = []
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for pointer in range(0, 3):
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_schedules.append(schedules[schedules_types['Receptacle']+pointer])
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_appliances.schedules = _schedules
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_usage_zone.occupancy = _occupancy
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_usage_zone.lighting = _lighting
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_usage_zone.appliances = _appliances
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_control = ThermalControl()
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_schedules = []
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for pointer in range(0, 3):
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_schedules.append(schedules[schedules_types['HtgSetPt']+pointer])
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_control.heating_set_point_schedules = _schedules
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_schedules = []
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for pointer in range(0, 3):
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_schedules.append(schedules[schedules_types['ClgSetPt']+pointer])
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_control.cooling_set_point_schedules = _schedules
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_schedules = []
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for pointer in range(0, 3):
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_schedules.append(schedules[schedules_types['HVAC Avail']+pointer])
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_control.hvac_availability_schedules = _schedules
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_usage_zone.thermal_control = _control
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return _usage_zone
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def _search_archetypes(self, libs_usage):
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for item in self._data['lighting']:
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comnet_usage = UsageHelper.comnet_from_libs_usage(libs_usage)
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if comnet_usage == item:
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usage_archetype = self._parse_usage_type(comnet_usage, self._data, self._xls)
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return usage_archetype
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return None
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def enrich_buildings(self):
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"""
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Returns the city with the usage parameters assigned to the buildings
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:return:
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"""
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city = self._city
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for building in city.buildings:
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usage = GeometryHelper.libs_usage_from_libs_function(building.function)
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try:
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archetype_usage = self._search_archetypes(usage)
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except KeyError:
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sys.stderr.write(f'Building {building.name} has unknown archetype for building function:'
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f' {building.function}, that assigns building usage as '
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f'{GeometryHelper.libs_usage_from_libs_function(building.function)}\n')
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return
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for internal_zone in building.internal_zones:
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if internal_zone.area is None:
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raise Exception('Internal zone area not defined, ACH cannot be calculated')
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if internal_zone.volume is None:
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raise Exception('Internal zone volume not defined, ACH cannot be calculated')
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if internal_zone.area <= 0:
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raise Exception('Internal zone area is zero, ACH cannot be calculated')
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if internal_zone.volume <= 0:
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raise Exception('Internal zone volume is zero, ACH cannot be calculated')
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volume_per_area = internal_zone.volume / internal_zone.area
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usage_zone = UsageZone()
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usage_zone.usage = usage
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self._assign_values_usage_zone(usage_zone, archetype_usage, volume_per_area)
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usage_zone.percentage = 1
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self._calculate_reduced_values_from_extended_library(usage_zone, archetype_usage)
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internal_zone.usage_zones = [usage_zone]
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@staticmethod
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def _assign_values_usage_zone(usage_zone, archetype, volume_per_area):
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# Due to the fact that python is not a typed language, the wrong object type is assigned to
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# usage_zone.occupancy when writing usage_zone.occupancy = archetype.occupancy.
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# Same happens for lighting and appliances. Therefore, this walk around has been done.
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usage_zone.mechanical_air_change = archetype.mechanical_air_change * cte.METERS_TO_FEET ** 2 \
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* cte.HOUR_TO_MINUTES / cte.METERS_TO_FEET ** 3 / volume_per_area
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_occupancy = Occupancy()
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_occupancy.occupancy_density = archetype.occupancy.occupancy_density / cte.METERS_TO_FEET**2
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_occupancy.sensible_radiative_internal_gain = archetype.occupancy.sensible_radiative_internal_gain \
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* archetype.occupancy.occupancy_density / cte.METERS_TO_FEET**2 \
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* cte.BTU_H_TO_WATTS
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_occupancy.latent_internal_gain = archetype.occupancy.latent_internal_gain \
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* archetype.occupancy.occupancy_density / cte.METERS_TO_FEET**2 \
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* cte.BTU_H_TO_WATTS
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_occupancy.sensible_convective_internal_gain = archetype.occupancy.sensible_convective_internal_gain \
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* archetype.occupancy.occupancy_density / cte.METERS_TO_FEET**2 \
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* cte.BTU_H_TO_WATTS
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_occupancy.occupancy_schedules = archetype.occupancy.occupancy_schedules
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usage_zone.occupancy = _occupancy
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_lighting = Lighting()
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_lighting.density = archetype.lighting.density / cte.METERS_TO_FEET ** 2
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_lighting.convective_fraction = archetype.lighting.convective_fraction
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_lighting.radiative_fraction = archetype.lighting.radiative_fraction
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_lighting.latent_fraction = archetype.lighting.latent_fraction
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_lighting.schedules = archetype.lighting.schedules
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usage_zone.lighting = _lighting
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_appliances = Appliances()
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_appliances.density = archetype.appliances.density / cte.METERS_TO_FEET ** 2
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_appliances.convective_fraction = archetype.appliances.convective_fraction
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_appliances.radiative_fraction = archetype.appliances.radiative_fraction
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_appliances.latent_fraction = archetype.appliances.latent_fraction
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_appliances.schedules = archetype.appliances.schedules
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usage_zone.appliances = _appliances
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_control = ThermalControl()
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_control.cooling_set_point_schedules = archetype.thermal_control.cooling_set_point_schedules
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_control.heating_set_point_schedules = archetype.thermal_control.heating_set_point_schedules
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_control.hvac_availability_schedules = archetype.thermal_control.hvac_availability_schedules
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usage_zone.thermal_control = _control
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@staticmethod
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def _calculate_reduced_values_from_extended_library(usage_zone, archetype):
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number_of_days_per_type = {'WD': 251, 'Sat': 52, 'Sun': 62}
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total = 0
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for schedule in archetype.thermal_control.hvac_availability_schedules:
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if schedule.day_types[0] == cte.SATURDAY:
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for value in schedule.values:
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total += value * number_of_days_per_type['Sat']
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elif schedule.day_types[0] == cte.SUNDAY:
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for value in schedule.values:
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total += value * number_of_days_per_type['Sun']
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else:
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for value in schedule.values:
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total += value * number_of_days_per_type['WD']
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usage_zone.hours_day = total / 365
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usage_zone.days_year = 365
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@staticmethod
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def _calculate_internal_gains(archetype):
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_DAYS = [cte.MONDAY, cte.TUESDAY, cte.WEDNESDAY, cte.THURSDAY, cte.FRIDAY, cte.SATURDAY, cte.SUNDAY, cte.HOLIDAY]
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_number_of_days_per_type = [51, 50, 50, 50, 50, 52, 52, 10]
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_mean_internal_gain = InternalGain()
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_mean_internal_gain.type = 'mean_value_of_internal_gains'
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_base_schedule = Schedule()
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_base_schedule.type = cte.INTERNAL_GAINS
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_base_schedule.time_range = cte.DAY
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_base_schedule.time_step = cte.HOUR
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_base_schedule.data_type = cte.FRACTION
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_latent_heat_gain = archetype.occupancy.latent_internal_gain
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_convective_heat_gain = archetype.occupancy.sensible_convective_internal_gain
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_radiative_heat_gain = archetype.occupancy.sensible_radiative_internal_gain
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_total_heat_gain = (_latent_heat_gain + _convective_heat_gain + _radiative_heat_gain)
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_schedule_values = numpy.zeros(24, 7)
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_sum = 0
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for day, _schedule in enumerate(archetype.occupancy.schedules):
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for v, value in enumerate(_schedule.values):
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_schedule_values[v, day] = value * _total_heat_gain
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_sum += value * _total_heat_gain * _number_of_days_per_type[day]
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_total_heat_gain += archetype.lighting.density + archetype.appliances.density
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_latent_heat_gain += archetype.lighting.latent_fraction * archetype.lighting.density\
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+ archetype.appliances.latent_fraction * archetype.appliances.density
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_radiative_heat_gain = archetype.lighting.radiative_fraction * archetype.lighting.density \
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+ archetype.appliances.radiative_fraction * archetype.appliances.density
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_convective_heat_gain = archetype.lighting.convective_fraction * archetype.lighting.density \
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+ archetype.appliances.convective_fraction * archetype.appliances.density
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for day, _schedule in enumerate(archetype.lighting.schedules):
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for v, value in enumerate(_schedule.values):
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_schedule_values[v, day] += value * archetype.lighting.density
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_sum += value * archetype.lighting.density * _number_of_days_per_type[day]
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for day, _schedule in enumerate(archetype.appliances.schedules):
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for v, value in enumerate(_schedule.values):
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_schedule_values[v, day] += value * archetype.appliances.density
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_sum += value * archetype.appliances.density * _number_of_days_per_type[day]
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_latent_fraction = _latent_heat_gain / _total_heat_gain
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_radiative_fraction = _radiative_heat_gain / _total_heat_gain
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_convective_fraction = _convective_heat_gain / _total_heat_gain
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_average_internal_gain = _sum / _total_heat_gain
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_schedules = []
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for day in range(0, len(_DAYS)):
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_schedule = copy.deepcopy(_base_schedule)
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_schedule.day_types = [_DAYS[day]]
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_schedule.values = _schedule_values[:day]
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_schedules.append(_schedule)
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_mean_internal_gain.average_internal_gain = _average_internal_gain
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_mean_internal_gain.latent_fraction = _latent_fraction
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_mean_internal_gain.convective_fraction = _convective_fraction
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_mean_internal_gain.radiative_fraction = _radiative_fraction
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_mean_internal_gain.schedules = _schedules
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return [_mean_internal_gain]
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