102 lines
3.4 KiB
Python
102 lines
3.4 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 math
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import hub.helpers.constants as cte
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import pandas as pd
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import calendar as cal
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import numpy as np
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class Weather:
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"""
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Weather class
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"""
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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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ambient_temperature_fahrenheit = []
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average_temperature = 0
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maximum_temperature = -1000
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minimum_temperature = 1000
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for temperature in ambient_temperature:
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value = temperature * 9 / 5 + 32
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ambient_temperature_fahrenheit.append(value)
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average_temperature += value / 8760
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if value > maximum_temperature:
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maximum_temperature = value
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if value < minimum_temperature:
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minimum_temperature = value
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delta_temperature = maximum_temperature - minimum_temperature
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ratio = 0.4 + 0.01 * (average_temperature - 44)
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lag = 35 - 1 * (average_temperature - 44)
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cold_temperature = []
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for temperature in ambient_temperature_fahrenheit:
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radians = (0.986 * (temperature-15-lag) - 90) * math.pi / 180
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cold_temperature.append((average_temperature + 6 + ratio * (delta_temperature/2) * math.sin(radians) - 32) * 5/9)
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return pd.DataFrame(cold_temperature, columns=['epw'])
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def get_monthly_mean_values(self, values):
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out = None
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if values is not None:
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if 'month' not in values.columns:
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values = pd.concat([self.month_hour, pd.DataFrame(values)], axis=1)
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out = values.groupby('month', as_index=False).mean()
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del out['month']
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return out
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@staticmethod
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def get_yearly_mean_values(values):
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return values.mean()
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def get_total_month(self, values):
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out = None
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if values is not None:
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if 'month' not in values.columns:
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values = pd.concat([self.month_hour, pd.DataFrame(values)], axis=1)
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out = pd.DataFrame(values).groupby('month', as_index=False).sum()
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del out['month']
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return out
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@property
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def month_hour(self):
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"""
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returns a DataFrame that has x values of the month number (January = 1, February = 2...),
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being x the number of hours of the corresponding month
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:return: DataFrame(int)
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"""
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array = []
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for i in range(0, 12):
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days_of_month = cal.monthrange(2015, i+1)[1]
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total_hours = days_of_month * 24
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array = np.concatenate((array, np.full(total_hours, i + 1)))
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return pd.DataFrame(array, columns=['month'])
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