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