140 lines
5.4 KiB
Python
140 lines
5.4 KiB
Python
"""
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monthly_to_hourly_demand module
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SPDX - License - Identifier: LGPL - 3.0 - or -later
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Copyright © 2020 Project Author Pilar Monsalvete pilar_monsalvete@yahoo.es
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"""
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import pandas as pd
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from city_model_structure.attributes.occupants import Occupants
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import calendar as cal
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import helpers.constants as cte
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class MonthlyToHourlyDemand:
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"""
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MonthlyToHourlyDemand class
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"""
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def __init__(self, building, conditioning_seasons):
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self._hourly_heating = pd.DataFrame()
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self._hourly_cooling = pd.DataFrame()
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self._building = building
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self._conditioning_seasons = conditioning_seasons
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def hourly_heating(self, key):
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"""
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hourly distribution of the monthly heating of a building
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:param key: string
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:return: [hourly_heating]
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"""
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# todo: this method and the insel model have to be reviewed for more than one thermal zone
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external_temp = self._building.external_temperature[cte.HOUR]
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# todo: review index depending on how the schedules are defined, either 8760 or 24 hours
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for usage_zone in self._building.usage_zones:
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temp_set = float(usage_zone.heating_setpoint)-3
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temp_back = float(usage_zone.heating_setback)-3
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# todo: if these are data frames, then they should be called as (Occupancy should be in low case):
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# usage_zone.schedules.Occupancy
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# self._conditioning_seasons.heating
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occupancy = Occupants().get_complete_year_schedule(usage_zone.schedules['Occupancy'])
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heating_schedule = self._conditioning_seasons['heating']
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hourly_heating = []
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i = 0
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j = 0
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temp_grad_day = []
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for month in range(1, 13):
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temp_grad_month = 0
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month_range = cal.monthrange(2015, month)[1]
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for day in range(1, month_range[1]+1):
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external_temp_med = 0
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for hour in range(0, 24):
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external_temp_med += external_temp[key][i]/24
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for hour in range(0, 24):
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if external_temp_med < temp_set and heating_schedule[month-1] == 1:
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if occupancy[hour] > 0:
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hdd = temp_set - external_temp[key][i]
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if hdd < 0:
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hdd = 0
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temp_grad_day.append(hdd)
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else:
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hdd = temp_back - external_temp[key][i]
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if hdd < 0:
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hdd = 0
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temp_grad_day.append(hdd)
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else:
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temp_grad_day.append(0)
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temp_grad_month += temp_grad_day[i]
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i += 1
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for day in range(1, month_range[1] + 1):
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for hour in range(0, 24):
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# monthly_demand = self._building.heating[cte.MONTH]['INSEL'][month-1] or maybe:
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# monthly_demand = self._building.heating[cte.MONTH].INSEL[month-1]
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monthly_demand = self._building.heating[cte.MONTH][month-1]
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if monthly_demand == 'NaN':
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monthly_demand = 0
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if temp_grad_month == 0:
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hourly_demand = 0
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else:
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hourly_demand = float(monthly_demand)*float(temp_grad_day[j])/float(temp_grad_month)
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hourly_heating.append(hourly_demand)
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j += 1
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self._hourly_heating = pd.DataFrame(data=hourly_heating, columns=['monthly to hourly'])
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return self._hourly_heating
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def hourly_cooling(self, key):
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"""
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hourly distribution of the monthly cooling of a building
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:param key: string
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:return: [hourly_cooling]
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"""
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# todo: this method and the insel model have to be reviewed for more than one thermal zone
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external_temp = self._building.external_temperature[cte.HOUR]
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# todo: review index depending on how the schedules are defined, either 8760 or 24 hours
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for usage_zone in self._building.usage_zones:
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temp_set = float(usage_zone.cooling_setpoint)
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temp_back = 100
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occupancy = Occupants().get_complete_year_schedule(usage_zone.schedules['Occupancy'])
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cooling_schedule = self._conditioning_seasons['cooling']
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hourly_cooling = []
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i = 0
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j = 0
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temp_grad_day = []
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for month in range(1, 13):
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temp_grad_month = 0
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month_range = cal.monthrange(2015, month)[1]
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for day in range(1, month_range[1] + 1):
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for hour in range(0, 24):
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if external_temp[key][i] > temp_set and cooling_schedule[month - 1] == 1:
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if occupancy[hour] > 0:
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cdd = external_temp[key][i] - temp_set
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if cdd < 0:
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cdd = 0
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temp_grad_day.append(cdd)
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else:
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cdd = external_temp[key][i] - temp_back
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if cdd < 0:
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cdd = 0
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temp_grad_day.append(cdd)
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else:
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temp_grad_day.append(0)
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temp_grad_month += temp_grad_day[i]
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i += 1
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for day in range(1, month_range[1] + 1):
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for hour in range(0, 24):
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# monthly_demand = self._building.heating[cte.MONTH]['INSEL'][month-1]
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monthly_demand = self._building.cooling[cte.MONTH][month - 1]
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if monthly_demand == 'NaN':
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monthly_demand = 0
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if temp_grad_month == 0:
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hourly_demand = 0
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else:
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hourly_demand = float(monthly_demand) * float(temp_grad_day[j]) / float(temp_grad_month)
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hourly_cooling.append(hourly_demand)
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j += 1
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self._hourly_cooling = pd.DataFrame(data=hourly_cooling, columns=['monthly to hourly'])
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return self._hourly_cooling
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