def now(self, timestamp=None, weather_data=None, model='STC'):
"""Preditive power output"""
if timestamp is None:
timestamp = datetime.datetime.now() - \
datetime.timedelta(hours=self.tz)
if model != 'STC' and weather_data == None:
weather_data = forecast.data(self.place)['currently']
if model == 'CC':
record = irradiation.blave(timestamp, self.place, self.tilt, \
self.azimuth, cloudCover=weather_data['cloudCover'])
else:
record = irradiation.blave(timestamp, self.place, self.tilt, \
self.azimuth)
irradiance = irradiation.irradiation(record, self.place, None,\
t=self.tilt, array_azimuth=self.azimuth, model='p9')
if hasattr(self, 'hourly_shade'):
irradiance = irradiance * self.hourly_shade.shade(timestamp)
if model == 'STC':
return self.p_ac(irradiance)
else:
t_cell = irradiation.moduleTemp(irradiance, weather_data)
return self.p_ac(irradiance, t_cell)
def now(self, timestamp=None, weather_data=None, model='STC'):
"""Preditive power output"""
if timestamp is None:
timestamp = datetime.datetime.now() - \
datetime.timedelta(hours=self.tz)
if model != 'STC' and weather_data == None:
weather_data = forecast.data(self.place)['currently']
if model == 'CC':
record = irradiation.blave(timestamp, self.place, self.tilt, \
self.azimuth, cloudCover=weather_data['cloudCover'])
else:
record = irradiation.blave(timestamp, self.place, self.tilt, \
self.azimuth)
irradiance = irradiation.irradiation(record, self.place, None,\
t=self.tilt, array_azimuth=self.azimuth, model='p9')
if hasattr(self, 'hourly_shade'):
irradiance = irradiance * self.hourly_shade.shade(timestamp)
if model == 'STC':
return self.p_ac(irradiance)
else:
t_cell = irradiation.moduleTemp(irradiance, weather_data)
return self.p_ac(irradiance, t_cell)
def _calc(record):
"""internal fuction for multiprocessing hack"""
properties, record = record
horizon = None
insolation = irradiation.irradiation(record, properties['place'], \
horizon, properties['tilt'], properties['azimuth'], \
properties['model_name'])
year = 2000
timestamp = tmy3.normalize_date(record['datetime'], year)
return timestamp, insolation, record
def _calc(record):
"""internal fuction for multiprocessing hack"""
properties, record = record
horizon = None
insolation = irradiation.irradiation(record, properties['place'], \
horizon, properties['tilt'], properties['azimuth'], \
properties['model_name'])
year = 2000
timestamp = tmy3.normalize_date(record['datetime'], year)
return timestamp, insolation, record
record['utc_datetime'] = strptime(_sd, self.tz)
record['datetime'] = strptime(_sd)
return record
def __del__(self):
self.csvfile.close()
def total(usaf, field='GHI (W/m^2)'):
"""total annual insolation, defaults to GHI"""
running_total = 0
usafdata = data(usaf)
for record in usafdata:
running_total += float(record[field])
return running_total/1000.
if __name__ == "__main__":
from solpy import geo
TILT = 32.0
#import matplotlib.pyplot as plt
#place = zipToCoordinates('17601) #Lancaster
PLACE = geo.zip_coordinates('19113') #Philadelphia
DUMMY, USAF = geo.closest_usaf(PLACE)
TOTAL_INS = 0
for i in data(USAF):
output = irradiation.irradiation(i, PLACE, t=TILT)
TOTAL_INS += output
print TOTAL_INS/1000
print TOTAL_INS/(1000*365.0)
print total(USAF)
# cloud_cover = ccddatabase[str(m_s)+str(d_s)]
# else:
# cloud_cover = 0.0
for setting in settings:
if setting['Name'] not in res:
res[setting['Name']] = {}
e = str(m_s) + str(d_s)
if e not in res[setting['Name']]:
res[setting['Name']][e] = []
#-- Global synthetic irradiation from Solpy
global_irradiation_rec = irradiation.blave(dt, place, 0, 180)
#-- Adjust it for the tilt. The value is now in W/m^2
irrValue = irradiation.irradiation(global_irradiation_rec,
place, None,
setting['Tilt'],
setting['Azimuth'], 'p9')
horr_irrValue = irradiation.irradiation(
global_irradiation_rec, place, None, 0, 180, 'p9')
#-- Workaround to keep the data aligned
d_ = datetime.date(2013, 1, 1)
t_ = datetime.time(hour, minute)
dt_ = datetime.datetime.combine(d_, t_)
res[setting['Name']][e].append([dt_, irrValue, horr_irrValue])
import scipy
import os
#-- Fix LaTeX for macOS 10.12
os.environ['PATH'] = os.environ['PATH'] + ':/Library/TeX/texbin'
import matplotlib as mpl
mpl.use('TkAgg')
def forecast_output(self, daylightSavings=False, source=None, hours=24):
"""forecast output of system"""
#todo: model and forecast should use consistant units; Wh or KWh
#default is forecast with solpy.blave
#this is ugly code... sorry
#todo: refactor
d = datetime.date.today()
endTimeUTC = datetime.datetime(d.year, d.month, d.day)\
+ datetime.timedelta(hours=hours-self.tz)
if source == 'noaa':
wseries = noaa.herp_derp_interp(self.place)
ts = []
irr = []
for i in wseries:
if i['utc_datetime'] > endTimeUTC:
break
rec = irradiation.blave(i['utc_datetime'], self.place, \
self.tilt, self.azimuth, cloudCover=i['cloudCover'])
irradiance = irradiation.irradiation(rec, self.place,\
t=self.tilt, array_azimuth=self.azimuth, model='p90')
t_cell = irradiation.moduleTemp(irradiance, i)
irr.append(self.p_ac(irradiance, t_cell))
ts.append(i['utc_datetime'])
resultset = ResultSet()
resultset.values = irr
resultset.timeseries = ts
return resultset
if source == 'forecast':
wseries = forecast.hourly(self.place)
ts = []
irr = []
for i in wseries:
if i['utc_datetime'] > endTimeUTC:
break
irradiance = irradiation.irradiation(i, self.place,\
t=self.tilt, array_azimuth=self.azimuth, model='p90')
t_cell = irradiation.moduleTemp(irradiance, i)
irr.append(self.p_ac(irradiance, t_cell))
ts.append(i['utc_datetime'])
resultset = ResultSet()
resultset.values = irr
resultset.timeseries = ts
return resultset
if source == 'blave':
wseries = forecast.hourly(self.place)
ts = []
irr = []
for i in wseries:
if i['utc_datetime'] > endTimeUTC:
break
rec = irradiation.blave(i['utc_datetime'], self.place, \
self.tilt, self.azimuth, cloudCover=i['cloudCover'])
irradiance = irradiation.irradiation(rec, self.place,\
t=self.tilt, array_azimuth=self.azimuth, model='p90')
t_cell = irradiation.moduleTemp(irradiance, i)
irr.append(self.p_ac(irradiance, t_cell))
ts.append(i['utc_datetime'])
rs = ResultSet()
rs.values = irr
rs.timeseries = ts
return rs
else:
#blave
stime = datetime.datetime.today().timetuple()
tz_off = datetime.timedelta(hours=self.tz)
if daylightSavings:
tz_off += datetime.timedelta(hours=1)
else:
tz_off += datetime.timedelta(hours=0)
initTime = datetime.datetime(stime[0], stime[1], stime[2]) - tz_off
timeseries = []
values = []
ts = initTime
while ts < datetime.datetime.now()-tz_off:
ts += datetime.timedelta(minutes=5)
currentPower = self.now(ts)
values.append(currentPower)
timeseries.append(ts)
rs = ResultSet()
rs.values = values
rs.timeseries = timeseries
return rs
def yearly_total_irr(place, az, tr): #, interval=30, ccd=None
"""Function which estimates the total irradiation.
Input: location (lat, lon),
az (azimuth in degrees, south is at 180 degrees),
tr (tilt of the roof in degrees, flat roof is 0),
#interval (what is the precision of the integration in minutes),
#cloud cover data (dictionary with floats from 0 to 1, for each day of the year in mmdd format (e.g. '1231');
get it from your local weather station).
Returns total yearly irradiation for the tilted and oriented surface in kWh/m^2.
"""
#-- Old method with KNMI data
#-- Counter for the yearly irradiation in kWh/m^2
# yearly_sum = 0
# #-- Compute for all dates and times
# for month in range(1, 13):
# for day in range(1, 32):
# #-- Skip these dates
# if (day == 29 or day == 30) and month == 2:
# continue
# if day == 31 and month in (2, 4, 6, 9, 11):
# continue
# #-- Daily value reset
# daily_rads = 0
# #-- Tweaking to get the proper key values for dates
# if month < 10:
# m_s = '0' + str(month)
# else:
# m_s = str(month)
# if day < 10:
# d_s = '0' + str(day)
# else:
# d_s = str(day)
# d = datetime.date(2013, month, day)
# #-- These are UTC times. The program is not smart enough to use sunrise and sunset times, but this works too
# for hour in range(3, 20):
# for minute in range(0, 60, interval):
# #-- Datetime
# t = datetime.time(hour, minute)
# dt = datetime.datetime.combine(d, t)
# #-- Get the historic cloud cover for that day
# if ccd:
# cloud_cover = ccd[str(m_s)+str(d_s)]
# else:
# cloud_cover = 0.0
# #-- Global synthetic irradiation from Solpy
# global_irradiation_rec = irradiation.blave(dt, place, 0, 180, cloud_cover)
# #-- Adjust it for the tilt. The value is now in W/m^2
# irrValue = irradiation.irradiation(global_irradiation_rec, place, None, tr, az, 'p9')
# #-- Integrate the value over the time interval (get Wh/m^2) and convert it to kWh/m^2 and add it to the daily summed value
# daily_rads += (irrValue * (float(interval)/60.0)) / 1000.0
# #-- When finished with the day, add the estimated value to the yearly sum
# yearly_sum += daily_rads
#-- EPW Weather data
STATION_CODE = '062400' # '062400' for Amsterdam
#-- Fetch the dataset thanks to the caelum library
records = eere.EPWdata(STATION_CODE)
#-- Get the global yearly irradiance (Wh/m^2/year)
TOTAL = sum([irradiation.irradiation(record=rec, location=place, horizon=None, t=tr, array_azimuth=az, model='p9') for rec in records])
#-- Divide it by 1000 to get the value in kWh/m^2/year
yearly_sum = TOTAL/1000.
#-- Yearly irradiation in kWh/m^2/year
return yearly_sum
def forecast_output(self, daylightSavings=False, source=None, hours=24):
"""forecast output of system"""
#todo: model and forecast should use consistant units; Wh or KWh
#default is forecast with solpy.blave
#this is ugly code... sorry
#todo: refactor
d = datetime.date.today()
endTimeUTC = datetime.datetime(d.year, d.month, d.day)\
+ datetime.timedelta(hours=hours-self.tz)
if source == 'noaa':
wseries = noaa.herp_derp_interp(self.place)
ts = []
irr = []
for i in wseries:
if i['utc_datetime'] > endTimeUTC:
break
rec = irradiation.blave(i['utc_datetime'], self.place, \
self.tilt, self.azimuth, cloudCover=i['cloudCover'])
irradiance = irradiation.irradiation(rec, self.place,\
t=self.tilt, array_azimuth=self.azimuth, model='p90')
t_cell = irradiation.moduleTemp(irradiance, i)
irr.append(self.p_ac(irradiance, t_cell))
ts.append(i['utc_datetime'])
resultset = ResultSet()
resultset.values = irr
resultset.timeseries = ts
return resultset
if source == 'forecast':
wseries = forecast.hourly(self.place)
ts = []
irr = []
for i in wseries:
if i['utc_datetime'] > endTimeUTC:
break
irradiance = irradiation.irradiation(i, self.place,\
t=self.tilt, array_azimuth=self.azimuth, model='p90')
t_cell = irradiation.moduleTemp(irradiance, i)
irr.append(self.p_ac(irradiance, t_cell))
ts.append(i['utc_datetime'])
resultset = ResultSet()
resultset.values = irr
resultset.timeseries = ts
return resultset
if source == 'blave':
wseries = forecast.hourly(self.place)
ts = []
irr = []
for i in wseries:
if i['utc_datetime'] > endTimeUTC:
break
rec = irradiation.blave(i['utc_datetime'], self.place, \
self.tilt, self.azimuth, cloudCover=i['cloudCover'])
irradiance = irradiation.irradiation(rec, self.place,\
t=self.tilt, array_azimuth=self.azimuth, model='p90')
t_cell = irradiation.moduleTemp(irradiance, i)
irr.append(self.p_ac(irradiance, t_cell))
ts.append(i['utc_datetime'])
rs = ResultSet()
rs.values = irr
rs.timeseries = ts
return rs
else:
#blave
stime = datetime.datetime.today().timetuple()
tz_off = datetime.timedelta(hours=self.tz)
if daylightSavings:
tz_off += datetime.timedelta(hours=1)
else:
tz_off += datetime.timedelta(hours=0)
initTime = datetime.datetime(stime[0], stime[1], stime[2]) - tz_off
timeseries = []
values = []
ts = initTime
while ts < datetime.datetime.now() - tz_off:
ts += datetime.timedelta(minutes=5)
currentPower = self.now(ts)
values.append(currentPower)
timeseries.append(ts)
rs = ResultSet()
rs.values = values
rs.timeseries = timeseries
return rs
# if ccddatabase:
# cloud_cover = ccddatabase[str(m_s)+str(d_s)]
# else:
# cloud_cover = 0.0
for setting in settings:
if setting['Name'] not in res:
res[setting['Name']] = {}
e = str(m_s)+str(d_s)
if e not in res[setting['Name']]:
res[setting['Name']][e] = []
#-- Global synthetic irradiation from Solpy
global_irradiation_rec = irradiation.blave(dt, place, 0, 180)
#-- Adjust it for the tilt. The value is now in W/m^2
irrValue = irradiation.irradiation(global_irradiation_rec, place, None, setting['Tilt'], setting['Azimuth'], 'p9')
horr_irrValue = irradiation.irradiation(global_irradiation_rec, place, None, 0, 180, 'p9')
#-- Workaround to keep the data aligned
d_ = datetime.date(2013, 1, 1)
t_ = datetime.time(hour, minute)
dt_ = datetime.datetime.combine(d_, t_)
res[setting['Name']][e].append([dt_, irrValue, horr_irrValue])
import scipy
import matplotlib as mpl
import matplotlib.pyplot as plt
# plt.rc('text', usetex=True)
# plt.rc('font', family='serif')
fig = plt.figure(1)