82 lines
4.2 KiB
Python
82 lines
4.2 KiB
Python
"""
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weather helper
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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 logging
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import math
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import hub.helpers.constants as cte
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from datetime import datetime, timedelta
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class Weather:
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"""
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Weather class
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"""
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_epw_file = {
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'CA.02.5935': 'https://energyplus-weather.s3.amazonaws.com/north_and_central_america_wmo_region_4/CAN/BC/CAN_BC_Summerland.717680_CWEC/CAN_BC_Summerland.717680_CWEC.epw',
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'CA.10.06': 'https://energyplus-weather.s3.amazonaws.com/north_and_central_america_wmo_region_4/CAN/PQ/CAN_PQ_Montreal.Intl.AP.716270_CWEC/CAN_PQ_Montreal.Intl.AP.716270_CWEC.epw',
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'CA.10.13': 'https://energyplus-weather.s3.amazonaws.com/north_and_central_america_wmo_region_4/CAN/PQ/CAN_PQ_Montreal.Intl.AP.716270_CWEC/CAN_PQ_Montreal.Intl.AP.716270_CWEC.epw',
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'CA.10.14': 'https://energyplus-weather.s3.amazonaws.com/north_and_central_america_wmo_region_4/CAN/PQ/CAN_PQ_Montreal.Intl.AP.716270_CWEC/CAN_PQ_Montreal.Intl.AP.716270_CWEC.epw',
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'CA.10.16': 'https://energyplus-weather.s3.amazonaws.com/north_and_central_america_wmo_region_4/CAN/PQ/CAN_PQ_Montreal.Intl.AP.716270_CWEC/CAN_PQ_Montreal.Intl.AP.716270_CWEC.epw',
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'DE.01.082': 'https://energyplus-weather.s3.amazonaws.com/europe_wmo_region_6/DEU/DEU_Stuttgart.107380_IWEC/DEU_Stuttgart.107380_IWEC.epw',
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'US.NY.047': 'https://energyplus-weather.s3.amazonaws.com/north_and_central_america_wmo_region_4/USA/NY/USA_NY_New.York.City-Central.Park.94728_TMY/USA_NY_New.York.City-Central.Park.94728_TMY.epw',
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'CA.10.12': 'https://energyplus-weather.s3.amazonaws.com/north_and_central_america_wmo_region_4/CAN/PQ/CAN_PQ_Quebec.717140_CWEC/CAN_PQ_Quebec.717140_CWEC.epw',
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'IL.01.': 'https://energyplus-weather.s3.amazonaws.com/europe_wmo_region_6/ISR/ISR_Eilat.401990_MSI/ISR_Eilat.401990_MSI.epw',
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'ES.07.PM': 'https://energyplus-weather.s3.amazonaws.com/europe_wmo_region_6/ESP/ESP_Palma.083060_SWEC/ESP_Palma.083060_SWEC.epw'
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}
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# todo: this dictionary need to be completed, a data science student task?
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@staticmethod
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def sky_temperature(ambient_temperature):
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"""
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Get sky temperature from ambient temperature in Celsius
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:return: List[float]
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"""
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# Swinbank - Source sky model approximation(1963) based on cloudiness statistics(32 %) in the United States
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# ambient temperatures( in °C)
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# sky temperatures( in °C)
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values = []
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for temperature in ambient_temperature:
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value = 0.037536 * math.pow((temperature + cte.KELVIN), 1.5) \
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+ 0.32 * (temperature + cte.KELVIN) - cte.KELVIN
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values.append(value)
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return values
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@staticmethod
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def cold_water_temperature(ambient_temperature):
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"""
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Get cold water temperature from ambient temperature in Celsius
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:return: dict
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"""
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# Equation from "TOWARDS DEVELOPMENT OF AN ALGORITHM FOR MAINS WATER TEMPERATURE", 2004, Jay Burch
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# and Craig Christensen, National Renewable Energy Laboratory
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# ambient temperatures( in °C)
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# cold water temperatures( in °C)
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t_out_fahrenheit = [1.8 * t_out + 32 for t_out in ambient_temperature]
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t_out_average = sum(t_out_fahrenheit) / len(t_out_fahrenheit)
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max_difference = max(t_out_fahrenheit) - min(t_out_fahrenheit)
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ratio = 0.4 + 0.01 * (t_out_average - 44)
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lag = 35 - (t_out_average - 35)
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number_of_day = [a for a in range(1, 366)]
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day_of_year = [day for day in number_of_day for _ in range(24)]
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cold_temperature_fahrenheit = []
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cold_temperature = []
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for i in range(len(ambient_temperature)):
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cold_temperature_fahrenheit.append(t_out_average + 6 + ratio * (max_difference / 2) *
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math.sin(math.radians(0.986 * (day_of_year[i] - 15 - lag) - 90)))
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cold_temperature.append((cold_temperature_fahrenheit[i] - 32) / 1.8)
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return cold_temperature
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def epw_file(self, region_code):
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"""
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returns the url for the weather file for the given location or default (Montreal data)
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:return: str
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"""
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if region_code not in self._epw_file:
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logging.warning('Specific weather data unknown for %s using Montreal data instead', region_code)
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return self._epw_file['CA.10.06']
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return self._epw_file[region_code]
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