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load_weather.py
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load_weather.py
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import pandas as pd
import datetime
from scipy import interpolate
import numpy as np
import matplotlib.pyplot as plt
import time
def get_StartTime(bld_name):
loads_list = pd.read_csv(bld_name + '.csv', sep = ',')
first_time = loads_list.iloc[0, 0]
return first_time
def ReadWeather():
weather_year = 'weather/seattle'
weather_list = pd.read_csv(weather_year + '.csv', sep = ',')
N_lines = len(weather_list.index)
DewPoint = np.zeros((N_lines))
DHI = np.zeros((N_lines))
DNI = np.zeros((N_lines))
Temp = np.zeros((N_lines))
Pressure = np.zeros((N_lines))
Humidity = np.zeros((N_lines))
WindSpeed = np.zeros((N_lines))
times = np.zeros((N_lines))
timestamp = []
for i in range(N_lines):
year = int(weather_list.iloc[i, 0])
month = int(weather_list.iloc[i, 1])
day = int(weather_list.iloc[i, 2])
hour = int(weather_list.iloc[i, 3])
minute = int(weather_list.iloc[i, 4])
d = datetime.datetime(year, month, day, hour, minute, 0)
timestamp.append(d)
times[i] = int(time.mktime(d.timetuple()))
DewPoint[i] = float(weather_list.iloc[i, 5])
DHI[i] = float(weather_list.iloc[i, 6])
DNI[i] = float(weather_list.iloc[i, 7])
Temp[i] = float(weather_list.iloc[i, 8])
Pressure[i] = float(weather_list.iloc[i, 9])
Humidity[i] = float(weather_list.iloc[i, 10])
WindSpeed[i] = float(weather_list.iloc[i, 11])
return(times, DewPoint, DHI, DNI, Temp, Pressure, Humidity, WindSpeed)
def Interpolate(bld_name, times, DewPoint, DHI, DNI, Temp, Pressure, Humidity, WindSpeed):
fTemp = interpolate.interp1d(times, Temp)
fDewPoint = interpolate.interp1d(times, DewPoint)
fDHI = interpolate.interp1d(times, DHI)
fDNI = interpolate.interp1d(times, DNI)
fPressure = interpolate.interp1d(times, Pressure)
fHumidity = interpolate.interp1d(times, Humidity)
fWindSpeed = interpolate.interp1d(times, WindSpeed)
start_time = get_StartTime(bld_name)
end_time = '2012-07-06 00:00:00'
start_t = datetime.datetime.strptime(start_time, '%Y-%m-%d %H:%M:%S')
start_t = int(time.mktime(start_t.timetuple()))
end_t = datetime.datetime.strptime(end_time, '%Y-%m-%d %H:%M:%S')
end_t = int(time.mktime(end_t.timetuple()))
vlen = int((end_t - start_t) / 900)
new_time = np.linspace(start_t, end_t - 900, vlen)
nTemp = fTemp(new_time)
nDewPoint = fDewPoint(new_time)
nDHI = fDHI(new_time)
nDNI = fDNI(new_time)
nPressure = fPressure(new_time)
nHumidity = fHumidity(new_time)
nWindSpeed = fWindSpeed(new_time)
return(nTemp, nDewPoint, nDHI, nDNI, nPressure, nHumidity, nWindSpeed)
def CalCorrelation(bld_name, nTemp, nDewPoint, nDHI, nDNI, nPressure, nHumidity, nWindSpeed):
data = pd.read_csv(bld_name + '.csv')
timestamp = data['date-time'].values
load = data['load'].values
load_len = load.size
load_weekday = []
Temp_weekday = []
DewPoint_weekday = []
DHI_weekday = []
DNI_weekday = []
Pressure_weekday = []
Huminity_weekday = []
WindSpeed_weekday = []
t = 0
while t < load_len:
d = datetime.datetime.strptime(timestamp[t], '%Y-%m-%d %H:%M:%S')
# check whether the date is weekday
if d.isoweekday() in range(1, 6):
load_weekday.extend(load[t:t+96])
Temp_weekday.extend(nTemp[t:t+96])
DewPoint_weekday.extend(nDewPoint[t:t+96])
DHI_weekday.extend(nDHI[t:t+96])
DNI_weekday.extend(nDNI[t:t+96])
Pressure_weekday.extend(nPressure[t:t+96])
Huminity_weekday.extend(nHumidity[t:t+96])
WindSpeed_weekday.extend(nWindSpeed[t:t+96])
t += 96
load_weekday = np.asarray(load_weekday)
Temp_weekday = np.asarray(Temp_weekday)
DewPoint_weekday = np.asarray(DewPoint_weekday)
DHI_weekday = np.asarray(DHI_weekday)
DNI_weekday = np.asarray(DNI_weekday)
Pressure_weekday = np.asarray(Pressure_weekday)
Huminity_weekday = np.asarray(Huminity_weekday)
WindSpeed_weekday = np.asarray(WindSpeed_weekday)
Temp_coef = np.corrcoef(load_weekday, Temp_weekday)[0,1]
DewPoint_coef = np.corrcoef(load_weekday, DewPoint_weekday)[0, 1]
DHI_coef = np.corrcoef(load_weekday, DHI_weekday)[0,1]
DNI_coef = np.corrcoef(load_weekday, DNI_weekday)[0,1]
Pressure_coef = np.corrcoef(load_weekday, Pressure_weekday)[0,1]
Huminity_coef = np.corrcoef(load_weekday, Huminity_weekday)[0,1]
WindSpeed_coef = np.corrcoef(load_weekday, WindSpeed_weekday)[0,1]
#
weekday_len = load_weekday.size
day_period = np.zeros((weekday_len))
week_period = np.zeros((weekday_len))
Td = 96
Tw = 96 * 5
for i in range(weekday_len):
day_period[i] = np.cos(2 * np.pi * i / Td)
week_period[i] = np.cos(2 * np.pi * i / Tw)
Day_coef = np.corrcoef(load_weekday, day_period)[0,1]
Week_coef = np.corrcoef(load_weekday, week_period)[0,1]
return(Temp_coef, DewPoint_coef, DHI_coef, DNI_coef, Pressure_coef, Huminity_coef, WindSpeed_coef, Day_coef, Week_coef)
def getWeatherData(bld_name):
(times, DewPoint, DHI, DNI, Temp, Pressure, Humidity, WindSpeed) = ReadWeather()
(nTemp, nDewPoint, nDHI, nDNI, nPressure, nHumidity, nWindSpeed) = Interpolate(bld_name, times, DewPoint, DHI, DNI, Temp, Pressure, Humidity, WindSpeed)
data = pd.read_csv(bld_name + '.csv')
timestamp = data['date-time'].values
load = data['load'].values
load_len = load.size
Temp_weekday = []
Huminity_weekday = []
WindSpeed_weekday = []
t = 0
while t < load_len:
d = datetime.datetime.strptime(timestamp[t], '%Y-%m-%d %H:%M:%S')
# check whether the date is weekday
if d.isoweekday() in range(1, 6):
Temp_weekday.extend(nTemp[t:t+96])
Huminity_weekday.extend(nHumidity[t:t+96])
WindSpeed_weekday.extend(nWindSpeed[t:t+96])
t += 96
Temp_weekday = np.asarray(Temp_weekday)
Huminity_weekday = np.asarray(Huminity_weekday)
WindSpeed_weekday = np.asarray(WindSpeed_weekday)
weekday_len = Temp_weekday.size
Day_period = np.zeros((weekday_len))
Td = 96
for i in range(weekday_len):
Day_period[i] = np.cos(2 * np.pi * i / Td)
return(Temp_weekday, Huminity_weekday, WindSpeed_weekday, Day_period)
if __name__ == "__main__":
bld_name = 'combined/1008_EE_CSE'
# building start and end times
start_time = get_StartTime(bld_name)
end_time = '2012-07-06 00:00:00'
'''
# read weather data
(times, DewPoint, DHI, DNI, Temp, Pressure, Humidity, WindSpeed) = ReadWeather()
# interpolate
(nTemp, nDewPoint, nDHI, nDNI, nPressure, nHumidity, nWindSpeed) = Interpolate(times, DewPoint, DHI, DNI, Temp, Pressure, Humidity, WindSpeed)
# calculate the correlation
(Temp_coef, DewPoint_coef, DHI_coef, DNI_coef, Pressure_coef, Huminity_coef, WindSpeed_coef, Day_coef, Week_coef) = CalCorrelation(bld_name, nTemp, nDewPoint, nDHI, nDNI, nPressure, nHumidity, nWindSpeed)
print(Temp_coef, DewPoint_coef, DHI_coef, DNI_coef, Pressure_coef, Huminity_coef, WindSpeed_coef, Day_coef, Week_coef)
'''
(Temp_weekday, Huminity_weekday, WindSpeed_weekday, Day_period) = getWeatherData(bld_name)