"""
The ``modelchain`` module contains functions and classes that combine
many of the PV power modeling steps. These tools make it easy to
get started with pvlib and demonstrate standard ways to use the
library. With great power comes great responsibility: users should take
the time to read the source code for the module.
"""
from functools import partial
import warnings
import pandas as pd
from pvlib import (atmosphere, clearsky, inverter, pvsystem, solarposition,
temperature, tools)
from pvlib.tracking import SingleAxisTracker
import pvlib.irradiance # avoid name conflict with full import
from pvlib.pvsystem import _DC_MODEL_PARAMS
from pvlib._deprecation import pvlibDeprecationWarning
from pvlib.tools import _build_kwargs
# keys that are used to detect input data and assign data to appropriate
# ModelChain attribute
# for ModelChain.weather
WEATHER_KEYS = ('ghi', 'dhi', 'dni', 'wind_speed', 'temp_air',
'precipitable_water')
# for ModelChain.total_irrad
POA_KEYS = ('poa_global', 'poa_direct', 'poa_diffuse')
# Optional keys to communicate temperature data. If provided,
# 'cell_temperature' overrides ModelChain.temperature_model and sets
# ModelChain.cell_temperature to the data. If 'module_temperature' is provdied,
# overrides ModelChain.temperature_model with
# pvlib.temperature.sapm_celL_from_module
TEMPERATURE_KEYS = ('module_temperature', 'cell_temperature')
DATA_KEYS = WEATHER_KEYS + POA_KEYS + TEMPERATURE_KEYS
# these dictionaries contain the default configuration for following
# established modeling sequences. They can be used in combination with
# basic_chain and ModelChain. They are used by the ModelChain methods
# ModelChain.with_pvwatts, ModelChain.with_sapm, etc.
# pvwatts documentation states that it uses the following reference for
# a temperature model: Fuentes, M. K. (1987). A Simplified Thermal Model
# for Flat-Plate Photovoltaic Arrays. SAND85-0330. Albuquerque, NM:
# Sandia National Laboratories. Accessed September 3, 2013:
# http://prod.sandia.gov/techlib/access-control.cgi/1985/850330.pdf
# pvlib python does not implement that model, so use the SAPM instead.
PVWATTS_CONFIG = dict(
dc_model='pvwatts', ac_model='pvwatts', losses_model='pvwatts',
transposition_model='perez', aoi_model='physical',
spectral_model='no_loss', temperature_model='sapm'
)
SAPM_CONFIG = dict(
dc_model='sapm', ac_model='sandia', losses_model='no_loss',
aoi_model='sapm', spectral_model='sapm', temperature_model='sapm'
)
[docs]def basic_chain(times, latitude, longitude,
module_parameters, temperature_model_parameters,
inverter_parameters,
irradiance=None, weather=None,
surface_tilt=None, surface_azimuth=None,
orientation_strategy=None,
transposition_model='haydavies',
solar_position_method='nrel_numpy',
airmass_model='kastenyoung1989',
altitude=None, pressure=None,
**kwargs):
"""
An experimental function that computes all of the modeling steps
necessary for calculating power or energy for a PV system at a given
location.
Parameters
----------
times : DatetimeIndex
Times at which to evaluate the model.
latitude : float.
Positive is north of the equator.
Use decimal degrees notation.
longitude : float.
Positive is east of the prime meridian.
Use decimal degrees notation.
module_parameters : None, dict or Series
Module parameters as defined by the SAPM. See pvsystem.sapm for
details.
temperature_model_parameters : None, dict or Series.
Temperature model parameters as defined by the SAPM.
See temperature.sapm_cell for details.
inverter_parameters : None, dict or Series
Inverter parameters as defined by the CEC. See
:py:func:`inverter.sandia` for details.
irradiance : None or DataFrame, default None
If None, calculates clear sky data.
Columns must be 'dni', 'ghi', 'dhi'.
weather : None or DataFrame, default None
If None, assumes air temperature is 20 C and
wind speed is 0 m/s.
Columns must be 'wind_speed', 'temp_air'.
surface_tilt : None, float or Series, default None
Surface tilt angles in decimal degrees.
The tilt angle is defined as degrees from horizontal
(e.g. surface facing up = 0, surface facing horizon = 90)
surface_azimuth : None, float or Series, default None
Surface azimuth angles in decimal degrees.
The azimuth convention is defined
as degrees east of north
(North=0, South=180, East=90, West=270).
orientation_strategy : None or str, default None
The strategy for aligning the modules.
If not None, sets the ``surface_azimuth`` and ``surface_tilt``
properties of the ``system``. Allowed strategies include 'flat',
'south_at_latitude_tilt'. Ignored for SingleAxisTracker systems.
transposition_model : str, default 'haydavies'
Passed to system.get_irradiance.
solar_position_method : str, default 'nrel_numpy'
Passed to solarposition.get_solarposition.
airmass_model : str, default 'kastenyoung1989'
Passed to atmosphere.relativeairmass.
altitude : None or float, default None
If None, computed from pressure. Assumed to be 0 m
if pressure is also None.
pressure : None or float, default None
If None, computed from altitude. Assumed to be 101325 Pa
if altitude is also None.
**kwargs
Arbitrary keyword arguments.
See code for details.
Returns
-------
output : (dc, ac)
Tuple of DC power (with SAPM parameters) (DataFrame) and AC
power (Series).
"""
# use surface_tilt and surface_azimuth if provided,
# otherwise set them using the orientation_strategy
if surface_tilt is not None and surface_azimuth is not None:
pass
elif orientation_strategy is not None:
surface_tilt, surface_azimuth = \
get_orientation(orientation_strategy, latitude=latitude)
else:
raise ValueError('orientation_strategy or surface_tilt and '
'surface_azimuth must be provided')
if altitude is None and pressure is None:
altitude = 0.
pressure = 101325.
elif altitude is None:
altitude = atmosphere.pres2alt(pressure)
elif pressure is None:
pressure = atmosphere.alt2pres(altitude)
solar_position = solarposition.get_solarposition(
times, latitude, longitude, altitude=altitude, pressure=pressure,
method=solar_position_method, **kwargs)
# possible error with using apparent zenith with some models
airmass = atmosphere.get_relative_airmass(
solar_position['apparent_zenith'], model=airmass_model)
airmass = atmosphere.get_absolute_airmass(airmass, pressure)
dni_extra = pvlib.irradiance.get_extra_radiation(solar_position.index)
aoi = pvlib.irradiance.aoi(surface_tilt, surface_azimuth,
solar_position['apparent_zenith'],
solar_position['azimuth'])
if irradiance is None:
linke_turbidity = clearsky.lookup_linke_turbidity(
solar_position.index, latitude, longitude)
irradiance = clearsky.ineichen(
solar_position['apparent_zenith'],
airmass,
linke_turbidity,
altitude=altitude,
dni_extra=dni_extra
)
total_irrad = pvlib.irradiance.get_total_irradiance(
surface_tilt,
surface_azimuth,
solar_position['apparent_zenith'],
solar_position['azimuth'],
irradiance['dni'],
irradiance['ghi'],
irradiance['dhi'],
model=transposition_model,
dni_extra=dni_extra)
if weather is None:
weather = {'wind_speed': 0, 'temp_air': 20}
cell_temperature = temperature.sapm_cell(
total_irrad['poa_global'], weather['temp_air'], weather['wind_speed'],
temperature_model_parameters['a'], temperature_model_parameters['b'],
temperature_model_parameters['deltaT'])
effective_irradiance = pvsystem.sapm_effective_irradiance(
total_irrad['poa_direct'], total_irrad['poa_diffuse'], airmass, aoi,
module_parameters)
dc = pvsystem.sapm(effective_irradiance, cell_temperature,
module_parameters)
ac = inverter.sandia(dc['v_mp'], dc['p_mp'], inverter_parameters)
return dc, ac
[docs]def get_orientation(strategy, **kwargs):
"""
Determine a PV system's surface tilt and surface azimuth
using a named strategy.
Parameters
----------
strategy: str
The orientation strategy.
Allowed strategies include 'flat', 'south_at_latitude_tilt'.
**kwargs:
Strategy-dependent keyword arguments. See code for details.
Returns
-------
surface_tilt, surface_azimuth
"""
if strategy == 'south_at_latitude_tilt':
surface_azimuth = 180
surface_tilt = kwargs['latitude']
elif strategy == 'flat':
surface_azimuth = 180
surface_tilt = 0
else:
raise ValueError('invalid orientation strategy. strategy must '
'be one of south_at_latitude, flat,')
return surface_tilt, surface_azimuth
[docs]class ModelChain:
"""
The ModelChain class to provides a standardized, high-level
interface for all of the modeling steps necessary for calculating PV
power from a time series of weather inputs.
See https://pvlib-python.readthedocs.io/en/stable/modelchain.html
for examples.
Parameters
----------
system : PVSystem
A :py:class:`~pvlib.pvsystem.PVSystem` object that represents
the connected set of modules, inverters, etc.
location : Location
A :py:class:`~pvlib.location.Location` object that represents
the physical location at which to evaluate the model.
orientation_strategy : None or str, default None
The strategy for aligning the modules. If not None, sets the
``surface_azimuth`` and ``surface_tilt`` properties of the
``system``. Allowed strategies include 'flat',
'south_at_latitude_tilt'. Ignored for SingleAxisTracker systems.
clearsky_model : str, default 'ineichen'
Passed to location.get_clearsky.
transposition_model : str, default 'haydavies'
Passed to system.get_irradiance.
solar_position_method : str, default 'nrel_numpy'
Passed to location.get_solarposition.
airmass_model : str, default 'kastenyoung1989'
Passed to location.get_airmass.
dc_model: None, str, or function, default None
If None, the model will be inferred from the contents of
system.module_parameters. Valid strings are 'sapm',
'desoto', 'cec', 'pvsyst', 'pvwatts'. The ModelChain instance will
be passed as the first argument to a user-defined function.
ac_model: None, str, or function, default None
If None, the model will be inferred from the contents of
system.inverter_parameters and system.module_parameters. Valid
strings are 'sandia', 'adr', 'pvwatts'. The
ModelChain instance will be passed as the first argument to a
user-defined function.
aoi_model: None, str, or function, default None
If None, the model will be inferred from the contents of
system.module_parameters. Valid strings are 'physical',
'ashrae', 'sapm', 'martin_ruiz', 'no_loss'. The ModelChain instance
will be passed as the first argument to a user-defined function.
spectral_model: None, str, or function, default None
If None, the model will be inferred from the contents of
system.module_parameters. Valid strings are 'sapm',
'first_solar', 'no_loss'. The ModelChain instance will be passed
as the first argument to a user-defined function.
temperature_model: None, str or function, default None
Valid strings are 'sapm', 'pvsyst', and 'faiman'. The ModelChain
instance will be passed as the first argument to a user-defined
function.
losses_model: str or function, default 'no_loss'
Valid strings are 'pvwatts', 'no_loss'. The ModelChain instance
will be passed as the first argument to a user-defined function.
name: None or str, default None
Name of ModelChain instance.
"""
[docs] def __init__(self, system, location,
orientation_strategy=None,
clearsky_model='ineichen',
transposition_model='haydavies',
solar_position_method='nrel_numpy',
airmass_model='kastenyoung1989',
dc_model=None, ac_model=None, aoi_model=None,
spectral_model=None, temperature_model=None,
losses_model='no_loss', name=None, **kwargs):
self.name = name
self.system = system
self.location = location
self.clearsky_model = clearsky_model
self.transposition_model = transposition_model
self.solar_position_method = solar_position_method
self.airmass_model = airmass_model
# calls setters
self.dc_model = dc_model
self.ac_model = ac_model
self.aoi_model = aoi_model
self.spectral_model = spectral_model
self.temperature_model = temperature_model
self.losses_model = losses_model
self.orientation_strategy = orientation_strategy
self.weather = None
self.times = None
self.solar_position = None
if kwargs:
warnings.warn(
'Arbitrary ModelChain kwargs are deprecated and will be '
'removed in v0.9', pvlibDeprecationWarning
)
[docs] @classmethod
def with_pvwatts(cls, system, location,
orientation_strategy=None,
clearsky_model='ineichen',
airmass_model='kastenyoung1989',
name=None,
**kwargs):
"""
ModelChain that follows the PVWatts methods.
Parameters
----------
system : PVSystem
A :py:class:`~pvlib.pvsystem.PVSystem` object that represents
the connected set of modules, inverters, etc.
location : Location
A :py:class:`~pvlib.location.Location` object that represents
the physical location at which to evaluate the model.
orientation_strategy : None or str, default None
The strategy for aligning the modules. If not None, sets the
``surface_azimuth`` and ``surface_tilt`` properties of the
``system``. Allowed strategies include 'flat',
'south_at_latitude_tilt'. Ignored for SingleAxisTracker systems.
clearsky_model : str, default 'ineichen'
Passed to location.get_clearsky.
airmass_model : str, default 'kastenyoung1989'
Passed to location.get_airmass.
name: None or str, default None
Name of ModelChain instance.
**kwargs
Parameters supplied here are passed to the ModelChain
constructor and take precedence over the default
configuration.
Examples
--------
>>> module_parameters = dict(gamma_pdc=-0.003, pdc0=4500)
>>> inverter_parameters = dict(pac0=4000)
>>> tparams = TEMPERATURE_MODEL_PARAMETERS['sapm']['open_rack_glass_glass']
>>> system = PVSystem(surface_tilt=30, surface_azimuth=180,
... module_parameters=module_parameters,
... inverter_parameters=inverter_parameters,
... temperature_model_parameters=tparams)
>>> location = Location(32.2, -110.9)
>>> ModelChain.with_pvwatts(system, location)
ModelChain:
name: None
orientation_strategy: None
clearsky_model: ineichen
transposition_model: perez
solar_position_method: nrel_numpy
airmass_model: kastenyoung1989
dc_model: pvwatts_dc
ac_model: pvwatts_inverter
aoi_model: physical_aoi_loss
spectral_model: no_spectral_loss
temperature_model: sapm_temp
losses_model: pvwatts_losses
""" # noqa: E501
config = PVWATTS_CONFIG.copy()
config.update(kwargs)
return ModelChain(
system, location,
orientation_strategy=orientation_strategy,
clearsky_model=clearsky_model,
airmass_model=airmass_model,
name=name,
**config
)
[docs] @classmethod
def with_sapm(cls, system, location,
orientation_strategy=None,
clearsky_model='ineichen',
transposition_model='haydavies',
solar_position_method='nrel_numpy',
airmass_model='kastenyoung1989',
name=None,
**kwargs):
"""
ModelChain that follows the Sandia Array Performance Model
(SAPM) methods.
Parameters
----------
system : PVSystem
A :py:class:`~pvlib.pvsystem.PVSystem` object that represents
the connected set of modules, inverters, etc.
location : Location
A :py:class:`~pvlib.location.Location` object that represents
the physical location at which to evaluate the model.
orientation_strategy : None or str, default None
The strategy for aligning the modules. If not None, sets the
``surface_azimuth`` and ``surface_tilt`` properties of the
``system``. Allowed strategies include 'flat',
'south_at_latitude_tilt'. Ignored for SingleAxisTracker systems.
clearsky_model : str, default 'ineichen'
Passed to location.get_clearsky.
transposition_model : str, default 'haydavies'
Passed to system.get_irradiance.
solar_position_method : str, default 'nrel_numpy'
Passed to location.get_solarposition.
airmass_model : str, default 'kastenyoung1989'
Passed to location.get_airmass.
name: None or str, default None
Name of ModelChain instance.
**kwargs
Parameters supplied here are passed to the ModelChain
constructor and take precedence over the default
configuration.
Examples
--------
>>> mods = pvlib.pvsystem.retrieve_sam('sandiamod')
>>> invs = pvlib.pvsystem.retrieve_sam('cecinverter')
>>> module_parameters = mods['Canadian_Solar_CS5P_220M___2009_']
>>> inverter_parameters = invs['ABB__MICRO_0_25_I_OUTD_US_240__240V_']
>>> tparams = TEMPERATURE_MODEL_PARAMETERS['sapm']['open_rack_glass_glass']
>>> system = PVSystem(surface_tilt=30, surface_azimuth=180,
... module_parameters=module_parameters,
... inverter_parameters=inverter_parameters,
... temperature_model_parameters=tparams)
>>> location = Location(32.2, -110.9)
>>> ModelChain.with_sapm(system, location)
ModelChain:
name: None
orientation_strategy: None
clearsky_model: ineichen
transposition_model: haydavies
solar_position_method: nrel_numpy
airmass_model: kastenyoung1989
dc_model: sapm
ac_model: snlinverter
aoi_model: sapm_aoi_loss
spectral_model: sapm_spectral_loss
temperature_model: sapm_temp
losses_model: no_extra_losses
""" # noqa: E501
config = SAPM_CONFIG.copy()
config.update(kwargs)
return ModelChain(
system, location,
orientation_strategy=orientation_strategy,
clearsky_model=clearsky_model,
transposition_model=transposition_model,
solar_position_method=solar_position_method,
airmass_model=airmass_model,
name=name,
**config
)
def __repr__(self):
attrs = [
'name', 'orientation_strategy', 'clearsky_model',
'transposition_model', 'solar_position_method',
'airmass_model', 'dc_model', 'ac_model', 'aoi_model',
'spectral_model', 'temperature_model', 'losses_model'
]
def getmcattr(self, attr):
"""needed to avoid recursion in property lookups"""
out = getattr(self, attr)
try:
out = out.__name__
except AttributeError:
pass
return out
return ('ModelChain: \n ' + '\n '.join(
f'{attr}: {getmcattr(self, attr)}' for attr in attrs))
@property
def orientation_strategy(self):
return self._orientation_strategy
@orientation_strategy.setter
def orientation_strategy(self, strategy):
if strategy == 'None':
strategy = None
if strategy is not None:
self.system.surface_tilt, self.system.surface_azimuth = \
get_orientation(strategy, latitude=self.location.latitude)
self._orientation_strategy = strategy
@property
def dc_model(self):
return self._dc_model
@dc_model.setter
def dc_model(self, model):
# guess at model if None
if model is None:
self._dc_model, model = self.infer_dc_model()
# Set model and validate parameters
if isinstance(model, str):
model = model.lower()
if model in _DC_MODEL_PARAMS.keys():
# validate module parameters
missing_params = (_DC_MODEL_PARAMS[model]
- set(self.system.module_parameters.keys()))
if missing_params: # some parameters are not in module.keys()
raise ValueError(model + ' selected for the DC model but '
'one or more required parameters are '
'missing : ' + str(missing_params))
if model == 'sapm':
self._dc_model = self.sapm
elif model == 'desoto':
self._dc_model = self.desoto
elif model == 'cec':
self._dc_model = self.cec
elif model == 'pvsyst':
self._dc_model = self.pvsyst
elif model == 'pvwatts':
self._dc_model = self.pvwatts_dc
else:
raise ValueError(model + ' is not a valid DC power model')
else:
self._dc_model = partial(model, self)
[docs] def infer_dc_model(self):
"""Infer DC power model from system attributes."""
params = set(self.system.module_parameters.keys())
if {'A0', 'A1', 'C7'} <= params:
return self.sapm, 'sapm'
elif {'a_ref', 'I_L_ref', 'I_o_ref', 'R_sh_ref', 'R_s',
'Adjust'} <= params:
return self.cec, 'cec'
elif {'a_ref', 'I_L_ref', 'I_o_ref', 'R_sh_ref', 'R_s'} <= params:
return self.desoto, 'desoto'
elif {'gamma_ref', 'mu_gamma', 'I_L_ref', 'I_o_ref', 'R_sh_ref',
'R_sh_0', 'R_sh_exp', 'R_s'} <= params:
return self.pvsyst, 'pvsyst'
elif {'pdc0', 'gamma_pdc'} <= params:
return self.pvwatts_dc, 'pvwatts'
else:
raise ValueError('could not infer DC model from '
'system.module_parameters. Check '
'system.module_parameters or explicitly '
'set the model with the dc_model kwarg.')
[docs] def sapm(self):
self.dc = self.system.sapm(self.effective_irradiance,
self.cell_temperature)
self.dc = self.system.scale_voltage_current_power(self.dc)
return self
def _singlediode(self, calcparams_model_function):
(photocurrent, saturation_current, resistance_series,
resistance_shunt, nNsVth) = (
calcparams_model_function(self.effective_irradiance,
self.cell_temperature))
self.diode_params = pd.DataFrame({'I_L': photocurrent,
'I_o': saturation_current,
'R_s': resistance_series,
'R_sh': resistance_shunt,
'nNsVth': nNsVth})
self.dc = self.system.singlediode(
photocurrent, saturation_current, resistance_series,
resistance_shunt, nNsVth)
self.dc = self.system.scale_voltage_current_power(self.dc).fillna(0)
return self
[docs] def desoto(self):
return self._singlediode(self.system.calcparams_desoto)
[docs] def cec(self):
return self._singlediode(self.system.calcparams_cec)
[docs] def pvsyst(self):
return self._singlediode(self.system.calcparams_pvsyst)
[docs] def pvwatts_dc(self):
self.dc = self.system.pvwatts_dc(self.effective_irradiance,
self.cell_temperature)
return self
@property
def ac_model(self):
return self._ac_model
@ac_model.setter
def ac_model(self, model):
if model is None:
self._ac_model = self.infer_ac_model()
elif isinstance(model, str):
model = model.lower()
# TODO in v0.9: remove 'snlinverter', 'adrinverter'
if model in ['sandia', 'snlinverter']:
if model == 'snlinverter':
warnings.warn("ac_model = 'snlinverter' is deprecated and"
" will be removed in v0.9; use"
" ac_model = 'sandia' instead.",
pvlibDeprecationWarning)
self._ac_model = self.snlinverter
elif model in ['adr', 'adrinverter']:
if model == 'adrinverter':
warnings.warn("ac_model = 'adrinverter' is deprecated and"
" will be removed in v0.9; use"
" ac_model = 'adr' instead.",
pvlibDeprecationWarning)
self._ac_model = self.adrinverter
elif model == 'pvwatts':
self._ac_model = self.pvwatts_inverter
else:
raise ValueError(model + ' is not a valid AC power model')
else:
self._ac_model = partial(model, self)
[docs] def infer_ac_model(self):
"""Infer AC power model from system attributes."""
inverter_params = set(self.system.inverter_parameters.keys())
if {'C0', 'C1', 'C2'} <= inverter_params:
return self.snlinverter
elif {'ADRCoefficients'} <= inverter_params:
return self.adrinverter
elif {'pdc0'} <= inverter_params:
return self.pvwatts_inverter
else:
raise ValueError('could not infer AC model from '
'system.inverter_parameters. Check '
'system.inverter_parameters or explicitly '
'set the model with the ac_model kwarg.')
[docs] def snlinverter(self):
self.ac = self.system.snlinverter(self.dc['v_mp'], self.dc['p_mp'])
return self
[docs] def adrinverter(self):
self.ac = self.system.adrinverter(self.dc['v_mp'], self.dc['p_mp'])
return self
[docs] def pvwatts_inverter(self):
self.ac = self.system.pvwatts_ac(self.dc).fillna(0)
return self
@property
def aoi_model(self):
return self._aoi_model
@aoi_model.setter
def aoi_model(self, model):
if model is None:
self._aoi_model = self.infer_aoi_model()
elif isinstance(model, str):
model = model.lower()
if model == 'ashrae':
self._aoi_model = self.ashrae_aoi_loss
elif model == 'physical':
self._aoi_model = self.physical_aoi_loss
elif model == 'sapm':
self._aoi_model = self.sapm_aoi_loss
elif model == 'martin_ruiz':
self._aoi_model = self.martin_ruiz_aoi_loss
elif model == 'no_loss':
self._aoi_model = self.no_aoi_loss
else:
raise ValueError(model + ' is not a valid aoi loss model')
else:
self._aoi_model = partial(model, self)
[docs] def infer_aoi_model(self):
params = set(self.system.module_parameters.keys())
if {'K', 'L', 'n'} <= params:
return self.physical_aoi_loss
elif {'B5', 'B4', 'B3', 'B2', 'B1', 'B0'} <= params:
return self.sapm_aoi_loss
elif {'b'} <= params:
return self.ashrae_aoi_loss
elif {'a_r'} <= params:
return self.martin_ruiz_aoi_loss
else:
raise ValueError('could not infer AOI model from '
'system.module_parameters. Check that the '
'system.module_parameters contain parameters for '
'the physical, aoi, ashrae or martin_ruiz model; '
'explicitly set the model with the aoi_model '
'kwarg; or set aoi_model="no_loss".')
[docs] def ashrae_aoi_loss(self):
self.aoi_modifier = self.system.get_iam(self.aoi, iam_model='ashrae')
return self
[docs] def physical_aoi_loss(self):
self.aoi_modifier = self.system.get_iam(self.aoi, iam_model='physical')
return self
[docs] def sapm_aoi_loss(self):
self.aoi_modifier = self.system.get_iam(self.aoi, iam_model='sapm')
return self
def martin_ruiz_aoi_loss(self):
self.aoi_modifier = self.system.get_iam(self.aoi,
iam_model='martin_ruiz')
return self
[docs] def no_aoi_loss(self):
self.aoi_modifier = 1.0
return self
@property
def spectral_model(self):
return self._spectral_model
@spectral_model.setter
def spectral_model(self, model):
if model is None:
self._spectral_model = self.infer_spectral_model()
elif isinstance(model, str):
model = model.lower()
if model == 'first_solar':
self._spectral_model = self.first_solar_spectral_loss
elif model == 'sapm':
self._spectral_model = self.sapm_spectral_loss
elif model == 'no_loss':
self._spectral_model = self.no_spectral_loss
else:
raise ValueError(model + ' is not a valid spectral loss model')
else:
self._spectral_model = partial(model, self)
[docs] def infer_spectral_model(self):
"""Infer spectral model from system attributes."""
params = set(self.system.module_parameters.keys())
if {'A4', 'A3', 'A2', 'A1', 'A0'} <= params:
return self.sapm_spectral_loss
elif ((('Technology' in params or
'Material' in params) and
(self.system._infer_cell_type() is not None)) or
'first_solar_spectral_coefficients' in params):
return self.first_solar_spectral_loss
else:
raise ValueError('could not infer spectral model from '
'system.module_parameters. Check that the '
'system.module_parameters contain valid '
'first_solar_spectral_coefficients, a valid '
'Material or Technology value, or set '
'spectral_model="no_loss".')
[docs] def first_solar_spectral_loss(self):
self.spectral_modifier = self.system.first_solar_spectral_loss(
self.weather['precipitable_water'],
self.airmass['airmass_absolute'])
return self
[docs] def sapm_spectral_loss(self):
self.spectral_modifier = self.system.sapm_spectral_loss(
self.airmass['airmass_absolute'])
return self
[docs] def no_spectral_loss(self):
self.spectral_modifier = 1
return self
@property
def temperature_model(self):
return self._temperature_model
@temperature_model.setter
def temperature_model(self, model):
if model is None:
self._temperature_model = self.infer_temperature_model()
elif isinstance(model, str):
model = model.lower()
if model == 'sapm':
self._temperature_model = self.sapm_temp
elif model == 'pvsyst':
self._temperature_model = self.pvsyst_temp
elif model == 'faiman':
self._temperature_model = self.faiman_temp
else:
raise ValueError(model + ' is not a valid temperature model')
# check system.temperature_model_parameters for consistency
name_from_params = self.infer_temperature_model().__name__
if self._temperature_model.__name__ != name_from_params:
raise ValueError(
'Temperature model {} is inconsistent with '
'PVsystem.temperature_model_parameters {}'.format(
self._temperature_model.__name__,
self.system.temperature_model_parameters))
else:
self._temperature_model = partial(model, self)
[docs] def infer_temperature_model(self):
"""Infer temperature model from system attributes."""
params = set(self.system.temperature_model_parameters.keys())
# remove or statement in v0.9
if {'a', 'b', 'deltaT'} <= params or (
not params and self.system.racking_model is None
and self.system.module_type is None):
return self.sapm_temp
elif {'u_c', 'u_v'} <= params:
return self.pvsyst_temp
elif {'u0', 'u1'} <= params:
return self.faiman_temp
else:
raise ValueError('could not infer temperature model from '
'system.temperature_module_parameters {}.'
.format(self.system.temperature_model_parameters))
[docs] def sapm_temp(self):
self.cell_temperature = self.system.sapm_celltemp(
self.total_irrad['poa_global'], self.weather['temp_air'],
self.weather['wind_speed'])
return self
[docs] def pvsyst_temp(self):
self.cell_temperature = self.system.pvsyst_celltemp(
self.total_irrad['poa_global'], self.weather['temp_air'],
self.weather['wind_speed'])
return self
[docs] def faiman_temp(self):
self.cell_temperature = self.system.faiman_celltemp(
self.total_irrad['poa_global'], self.weather['temp_air'],
self.weather['wind_speed'])
return self
@property
def losses_model(self):
return self._losses_model
@losses_model.setter
def losses_model(self, model):
if model is None:
self._losses_model = self.infer_losses_model()
elif isinstance(model, str):
model = model.lower()
if model == 'pvwatts':
self._losses_model = self.pvwatts_losses
elif model == 'no_loss':
self._losses_model = self.no_extra_losses
else:
raise ValueError(model + ' is not a valid losses model')
else:
self._losses_model = partial(model, self)
[docs] def infer_losses_model(self):
raise NotImplementedError
[docs] def pvwatts_losses(self):
self.losses = (100 - self.system.pvwatts_losses()) / 100.
self.dc *= self.losses
return self
[docs] def effective_irradiance_model(self):
fd = self.system.module_parameters.get('FD', 1.)
self.effective_irradiance = self.spectral_modifier * (
self.total_irrad['poa_direct']*self.aoi_modifier +
fd*self.total_irrad['poa_diffuse'])
return self
[docs] def complete_irradiance(self, weather):
"""
Determine the missing irradiation columns. Only two of the
following data columns (dni, ghi, dhi) are needed to calculate
the missing data.
This function is not safe at the moment. Results can be too high
or negative. Please contribute and help to improve this function
on https://github.com/pvlib/pvlib-python
Parameters
----------
weather : DataFrame
Column names must be ``'dni'``, ``'ghi'``, ``'dhi'``,
``'wind_speed'``, ``'temp_air'``. All irradiance components
are required. Air temperature of 20 C and wind speed
of 0 m/s will be added to the DataFrame if not provided.
Returns
-------
self
Notes
-----
Assigns attributes: ``weather``
Examples
--------
This example does not work until the parameters `my_system`,
`my_location`, and `my_weather` are defined but shows the basic idea
how this method can be used.
>>> from pvlib.modelchain import ModelChain
>>> # my_weather containing 'dhi' and 'ghi'.
>>> mc = ModelChain(my_system, my_location) # doctest: +SKIP
>>> mc.complete_irradiance(my_weather) # doctest: +SKIP
>>> mc.run_model(mc.weather) # doctest: +SKIP
>>> # my_weather containing 'dhi', 'ghi' and 'dni'.
>>> mc = ModelChain(my_system, my_location) # doctest: +SKIP
>>> mc.run_model(my_weather) # doctest: +SKIP
"""
self.weather = weather
self.solar_position = self.location.get_solarposition(
self.weather.index, method=self.solar_position_method)
icolumns = set(self.weather.columns)
wrn_txt = ("This function is not safe at the moment.\n" +
"Results can be too high or negative.\n" +
"Help to improve this function on github:\n" +
"https://github.com/pvlib/pvlib-python \n")
if {'ghi', 'dhi'} <= icolumns and 'dni' not in icolumns:
clearsky = self.location.get_clearsky(
self.weather.index, solar_position=self.solar_position)
self.weather.loc[:, 'dni'] = pvlib.irradiance.dni(
self.weather.loc[:, 'ghi'], self.weather.loc[:, 'dhi'],
self.solar_position.zenith,
clearsky_dni=clearsky['dni'],
clearsky_tolerance=1.1)
elif {'dni', 'dhi'} <= icolumns and 'ghi' not in icolumns:
warnings.warn(wrn_txt, UserWarning)
self.weather.loc[:, 'ghi'] = (
self.weather.dni * tools.cosd(self.solar_position.zenith) +
self.weather.dhi)
elif {'dni', 'ghi'} <= icolumns and 'dhi' not in icolumns:
warnings.warn(wrn_txt, UserWarning)
self.weather.loc[:, 'dhi'] = (
self.weather.ghi - self.weather.dni *
tools.cosd(self.solar_position.zenith))
return self
def _prep_inputs_solar_pos(self, kwargs={}):
"""
Assign solar position
"""
self.solar_position = self.location.get_solarposition(
self.weather.index, method=self.solar_position_method,
**kwargs)
return self
def _prep_inputs_airmass(self):
"""
Assign airmass
"""
self.airmass = self.location.get_airmass(
solar_position=self.solar_position, model=self.airmass_model)
return self
def _prep_inputs_tracking(self):
"""
Calculate tracker position and AOI
"""
self.tracking = self.system.singleaxis(
self.solar_position['apparent_zenith'],
self.solar_position['azimuth'])
self.tracking['surface_tilt'] = (
self.tracking['surface_tilt']
.fillna(self.system.axis_tilt))
self.tracking['surface_azimuth'] = (
self.tracking['surface_azimuth']
.fillna(self.system.axis_azimuth))
self.aoi = self.tracking['aoi']
return self
def _prep_inputs_fixed(self):
"""
Calculate AOI for fixed tilt system
"""
self.aoi = self.system.get_aoi(self.solar_position['apparent_zenith'],
self.solar_position['azimuth'])
return self
def _verify_df(self, data, required):
""" Checks data for column names in required
Parameters
----------
data : Dataframe
required : List of str
Raises
------
ValueError if any of required are not in data.columns.
"""
if not set(required) <= set(data.columns):
raise ValueError(
f"Incomplete input data. Data needs to contain {required}. "
f"Detected data contains: {list(data.columns)}")
return
def _assign_weather(self, data):
key_list = [k for k in WEATHER_KEYS if k in data]
self.weather = data[key_list].copy()
if self.weather.get('wind_speed') is None:
self.weather['wind_speed'] = 0
if self.weather.get('temp_air') is None:
self.weather['temp_air'] = 20
return self
def _assign_total_irrad(self, data):
key_list = [k for k in POA_KEYS if k in data]
self.total_irrad = data[key_list].copy()
return self
def _prepare_temperature(self, data=None):
"""
Sets cell_temperature using inputs in data and the specified
temperature model.
If 'data' contains 'cell_temperature', these values are assigned to
attribute ``cell_temperature``. If 'data' contains 'module_temperature`
and `temperature_model' is 'sapm', cell temperature is calculated using
:py:func:`pvlib.temperature.sapm_celL_from_module`. Otherwise, cell
temperature is calculated by 'temperature_model'.
Parameters
----------
data : DataFrame, default None
May contain columns ``'cell_temperature'`` or
``'module_temperaure'``.
Returns
-------
self
Assigns attribute ``cell_temperature``.
"""
if 'cell_temperature' in data:
self.cell_temperature = data['cell_temperature']
return self
# cell_temperature is not in input. Calculate cell_temperature using
# a temperature_model.
# If module_temperature is in input data we can use the SAPM cell
# temperature model.
if (('module_temperature' in data) and
(self.temperature_model.__name__ == 'sapm_temp')):
# use SAPM cell temperature model only
self.cell_temperature = pvlib.temperature.sapm_cell_from_module(
module_temperature=data['module_temperature'],
poa_global=self.total_irrad['poa_global'],
deltaT=self.system.temperature_model_parameters['deltaT'])
return self
# Calculate cell temperature from weather data. Cell temperature models
# expect total_irrad['poa_global'].
self.temperature_model()
return self
[docs] def run_model(self, weather):
"""
Run the model chain starting with broadband global, diffuse and/or
direct irradiance.
Parameters
----------
weather : DataFrame
Irradiance column names must include ``'dni'``, ``'ghi'``, and
``'dhi'``. If optional columns ``'temp_air'`` and ``'wind_speed'``
are not provided, air temperature of 20 C and wind speed of 0 m/s
are added to the DataFrame. If optional column
``'cell_temperature'`` is provided, these values are used instead
of `temperature_model`. If optional column `module_temperature`
is provided, `temperature_model` must be ``'sapm'``.
Returns
-------
self
Notes
-----
Assigns attributes: ``solar_position``, ``airmass``, ``weather``,
``total_irrad``, ``aoi``, ``aoi_modifier``, ``spectral_modifier``,
and ``effective_irradiance``, ``cell_temperature``, ``dc``, ``ac``,
``losses``, ``diode_params`` (if dc_model is a single diode model).
See also
--------
pvlib.modelchain.ModelChain.run_model_from_poa
pvlib.modelchain.ModelChain.run_model_from_effective_irradiance
"""
self.prepare_inputs(weather)
self.aoi_model()
self.spectral_model()
self.effective_irradiance_model()
self._run_from_effective_irrad(weather)
return self
[docs] def run_model_from_poa(self, data):
"""
Run the model starting with broadband irradiance in the plane of array.
Data must include direct, diffuse and total irradiance (W/m2) in the
plane of array. Reflections and spectral adjustments are made to
calculate effective irradiance (W/m2).
Parameters
----------
data : DataFrame
Required column names include ``'poa_global'``,
``'poa_direct'`` and ``'poa_diffuse'``. If optional columns
``'temp_air'`` and ``'wind_speed'`` are not provided, air
temperature of 20 C and wind speed of 0 m/s are assumed.
If optional column ``'cell_temperature'`` is provided, these values
are used instead of `temperature_model`. If optional column
``'module_temperature'`` is provided, `temperature_model` must be
``'sapm'``.
Returns
-------
self
Notes
-----
Assigns attributes: ``solar_position``, ``airmass``, ``weather``,
``total_irrad``, ``aoi``, ``aoi_modifier``, ``spectral_modifier``,
and ``effective_irradiance``, ``cell_temperature``, ``dc``, ``ac``,
``losses``, ``diode_params`` (if dc_model is a single diode model).
See also
--------
pvlib.modelchain.ModelChain.run_model
pvlib.modelchain.ModelChain.run_model_from_effective_irradiance
"""
self.prepare_inputs_from_poa(data)
self.aoi_model()
self.spectral_model()
self.effective_irradiance_model()
self._run_from_effective_irrad(data)
return self
def _run_from_effective_irrad(self, data=None):
"""
Executes the temperature, DC, losses and AC models.
Parameters
----------
data : DataFrame, default None
If optional column ``'cell_temperature'`` is provided, these values
are used instead of `temperature_model`. If optional column
`module_temperature` is provided, `temperature_model` must be
``'sapm'``.
Returns
-------
self
Notes
-----
Assigns attributes:``cell_temperature``, ``dc``, ``ac``, ``losses``,
``diode_params`` (if dc_model is a single diode model).
"""
self._prepare_temperature(data)
self.dc_model()
self.losses_model()
self.ac_model()
return self
[docs] def run_model_from_effective_irradiance(self, data=None):
"""
Run the model starting with effective irradiance in the plane of array.
Effective irradiance is irradiance in the plane-of-array after any
adjustments for soiling, reflections and spectrum.
Parameters
----------
data : DataFrame, default None
Required column is ``'effective_irradiance'``.
If optional column ``'cell_temperature'`` is provided, these values
are used instead of `temperature_model`. If optional column
``'module_temperature'`` is provided, `temperature_model` must be
``'sapm'``.
Returns
-------
self
Notes
-----
Assigns attributes: ``weather``, ``total_irrad``,
``effective_irradiance``, ``cell_temperature``, ``dc``, ``ac``,
``losses``, ``diode_params`` (if dc_model is a single diode model).
See also
--------
pvlib.modelchain.ModelChain.run_model_from
pvlib.modelchain.ModelChain.run_model_from_poa
"""
self._assign_weather(data)
self._assign_total_irrad(data)
self.effective_irradiance = data['effective_irradiance']
self._run_from_effective_irrad(data)
return self