def get_perfect_voltage_for_a_day(start, freq): """This method is used to build a pandas serie with voltage values. This serie has DateTime index and contains a value for every "freq" seconds during 24 hours starting from "start" date. There are several assumptions: 1. Location is Munich 2. A battery is pointing to the south, amount of blocks is 20 3. Sandia Module database is used 4. pvlib library is heavily used :param start: datetime. First timestamp in result series :param freq: str. How often voltage should be sampled :return: voltage : Series """ surface_tilt = _munich_location.latitude surface_azimuth = 180 # pointing south date_range = pd.date_range(start=start, end=start + dt.timedelta( hours=23, minutes=59, seconds=59), freq=freq, tz=_munich_location.tz) clearsky_estimations = _munich_location.get_clearsky(date_range) dni_extra = irradiance.extraradiation(date_range) solar_position = solarposition.get_solarposition( date_range, _munich_location.latitude, _munich_location.longitude) airmass = atmosphere.relativeairmass(solar_position['apparent_zenith']) pressure = atmosphere.alt2pres(_munich_location.altitude) am_abs = atmosphere.absoluteairmass(airmass, pressure) total_irrad = irradiance.total_irrad(surface_tilt, surface_azimuth, solar_position['apparent_zenith'], solar_position['azimuth'], clearsky_estimations['dni'], clearsky_estimations['ghi'], clearsky_estimations['dhi'], dni_extra=dni_extra, model='haydavies') temps = pvsystem.sapm_celltemp(total_irrad['poa_global'], 0, 15) aoi = irradiance.aoi(surface_tilt, surface_azimuth, solar_position['apparent_zenith'], solar_position['azimuth']) # add 0.0001 to avoid np.log(0) and warnings about that effective_irradiance = pvsystem.sapm_effective_irradiance( total_irrad['poa_direct'], total_irrad['poa_diffuse'], am_abs, aoi, _sandia_module) + 0.0001 sapm = pvsystem.sapm(effective_irradiance, temps['temp_cell'], _sandia_module) return sapm['p_mp'] * _module_count
def get_airmass(self, times=None, solar_position=None, model='kastenyoung1989'): """ Calculate the relative and absolute airmass. Automatically chooses zenith or apparant zenith depending on the selected model. Parameters ---------- times : None or DatetimeIndex Only used if solar_position is not provided. solar_position : None or DataFrame DataFrame with with columns 'apparent_zenith', 'zenith'. model : str Relative airmass model Returns ------- airmass : DataFrame Columns are 'airmass_relative', 'airmass_absolute' """ if solar_position is None: solar_position = self.get_solarposition(times) if model in atmosphere.APPARENT_ZENITH_MODELS: zenith = solar_position['apparent_zenith'] elif model in atmosphere.TRUE_ZENITH_MODELS: zenith = solar_position['zenith'] else: raise ValueError('{} is not a valid airmass model'.format(model)) airmass_relative = atmosphere.relativeairmass(zenith, model) pressure = atmosphere.alt2pres(self.altitude) airmass_absolute = atmosphere.absoluteairmass(airmass_relative, pressure) airmass = pd.DataFrame() airmass['airmass_relative'] = airmass_relative airmass['airmass_absolute'] = airmass_absolute return airmass
def test_ineichen_series(): tus = Location(32.2, -111, 'US/Arizona', 700) times = pd.date_range(start='2014-06-24', end='2014-06-25', freq='3h') times_localized = times.tz_localize(tus.tz) ephem_data = solarposition.get_solarposition(times_localized, tus.latitude, tus.longitude) am = atmosphere.relativeairmass(ephem_data['apparent_zenith']) am = atmosphere.absoluteairmass(am, atmosphere.alt2pres(tus.altitude)) expected = pd.DataFrame( np.array([[0., 0., 0.], [0., 0., 0.], [91.12492792, 321.16092181, 51.17628184], [716.46580533, 888.90147035, 99.5050056], [1053.42066043, 953.24925854, 116.32868969], [863.54692781, 922.06124712, 106.95536561], [271.06382274, 655.44925241, 73.05968071], [0., 0., 0.], [0., 0., 0.]]), columns=['ghi', 'dni', 'dhi'], index=times_localized) out = clearsky.ineichen(ephem_data['apparent_zenith'], am, 3) assert_frame_equal(expected, out)
def test_ineichen_series(): tus = Location(32.2, -111, 'US/Arizona', 700) times = pd.date_range(start='2014-06-24', end='2014-06-25', freq='3h') times_localized = times.tz_localize(tus.tz) ephem_data = solarposition.get_solarposition(times_localized, tus.latitude, tus.longitude) am = atmosphere.relativeairmass(ephem_data['apparent_zenith']) am = atmosphere.absoluteairmass(am, atmosphere.alt2pres(tus.altitude)) expected = pd.DataFrame(np. array([[ 0. , 0. , 0. ], [ 0. , 0. , 0. ], [ 91.12492792, 321.16092181, 51.17628184], [ 716.46580533, 888.90147035, 99.5050056 ], [ 1053.42066043, 953.24925854, 116.32868969], [ 863.54692781, 922.06124712, 106.95536561], [ 271.06382274, 655.44925241, 73.05968071], [ 0. , 0. , 0. ], [ 0. , 0. , 0. ]]), columns=['ghi', 'dni', 'dhi'], index=times_localized) out = clearsky.ineichen(ephem_data['apparent_zenith'], am, 3) assert_frame_equal(expected, out)
def test_absoluteairmass(): relative_am = atmosphere.relativeairmass(ephem_data['zenith'], 'simple') atmosphere.absoluteairmass(relative_am) atmosphere.absoluteairmass(relative_am, pressure=100000)
def bird(zenith, airmass_relative, aod380, aod500, precipitable_water, ozone=0.3, pressure=101325., dni_extra=1364., asymmetry=0.85, albedo=0.2): """ Bird Simple Clear Sky Broadband Solar Radiation Model Based on NREL Excel implementation by Daryl R. Myers [1, 2]. Bird and Hulstrom define the zenith as the "angle between a line to the sun and the local zenith". There is no distinction in the paper between solar zenith and apparent (or refracted) zenith, but the relative airmass is defined using the Kasten 1966 expression, which requires apparent zenith. Although the formulation for calculated zenith is never explicitly defined in the report, since the purpose was to compare existing clear sky models with "rigorous radiative transfer models" (RTM) it is possible that apparent zenith was obtained as output from the RTM. However, the implentation presented in PVLIB is tested against the NREL Excel implementation by Daryl Myers which uses an analytical expression for solar zenith instead of apparent zenith. Parameters ---------- zenith : numeric Solar or apparent zenith angle in degrees - see note above airmass_relative : numeric Relative airmass aod380 : numeric Aerosol optical depth [cm] measured at 380[nm] aod500 : numeric Aerosol optical depth [cm] measured at 500[nm] precipitable_water : numeric Precipitable water [cm] ozone : numeric Atmospheric ozone [cm], defaults to 0.3[cm] pressure : numeric Ambient pressure [Pa], defaults to 101325[Pa] dni_extra : numeric Extraterrestrial radiation [W/m^2], defaults to 1364[W/m^2] asymmetry : numeric Asymmetry factor, defaults to 0.85 albedo : numeric Albedo, defaults to 0.2 Returns ------- clearsky : DataFrame (if Series input) or OrderedDict of arrays DataFrame/OrderedDict contains the columns/keys ``'dhi', 'dni', 'ghi', 'direct_horizontal'`` in [W/m^2]. See also -------- pvlib.atmosphere.bird_hulstrom80_aod_bb pvlib.atmosphere.relativeairmass References ---------- [1] R. E. Bird and R. L Hulstrom, "A Simplified Clear Sky model for Direct and Diffuse Insolation on Horizontal Surfaces" SERI Technical Report SERI/TR-642-761, Feb 1981. Solar Energy Research Institute, Golden, CO. [2] Daryl R. Myers, "Solar Radiation: Practical Modeling for Renewable Energy Applications", pp. 46-51 CRC Press (2013) `NREL Bird Clear Sky Model <http://rredc.nrel.gov/solar/models/clearsky/>`_ `SERI/TR-642-761 <http://rredc.nrel.gov/solar/pubs/pdfs/tr-642-761.pdf>`_ `Error Reports <http://rredc.nrel.gov/solar/models/clearsky/error_reports.html>`_ """ etr = dni_extra # extraradiation ze_rad = np.deg2rad(zenith) # zenith in radians airmass = airmass_relative # Bird clear sky model am_press = atmosphere.absoluteairmass(airmass, pressure) t_rayleigh = (np.exp(-0.0903 * am_press**0.84 * (1.0 + am_press - am_press**1.01))) am_o3 = ozone * airmass t_ozone = (1.0 - 0.1611 * am_o3 * (1.0 + 139.48 * am_o3)**-0.3034 - 0.002715 * am_o3 / (1.0 + 0.044 * am_o3 + 0.0003 * am_o3**2.0)) t_gases = np.exp(-0.0127 * am_press**0.26) am_h2o = airmass * precipitable_water t_water = (1.0 - 2.4959 * am_h2o / ((1.0 + 79.034 * am_h2o)**0.6828 + 6.385 * am_h2o)) bird_huldstrom = atmosphere.bird_hulstrom80_aod_bb(aod380, aod500) t_aerosol = np.exp(-(bird_huldstrom**0.873) * (1.0 + bird_huldstrom - bird_huldstrom**0.7088) * airmass**0.9108) taa = 1.0 - 0.1 * (1.0 - airmass + airmass**1.06) * (1.0 - t_aerosol) rs = 0.0685 + (1.0 - asymmetry) * (1.0 - t_aerosol / taa) id_ = 0.9662 * etr * t_aerosol * t_water * t_gases * t_ozone * t_rayleigh ze_cos = np.where(zenith < 90, np.cos(ze_rad), 0.0) id_nh = id_ * ze_cos ias = (etr * ze_cos * 0.79 * t_ozone * t_gases * t_water * taa * (0.5 * (1.0 - t_rayleigh) + asymmetry * (1.0 - (t_aerosol / taa))) / (1.0 - airmass + airmass**1.02)) gh = (id_nh + ias) / (1.0 - albedo * rs) diffuse_horiz = gh - id_nh # TODO: be DRY, use decorator to wrap methods that need to return either # OrderedDict or DataFrame instead of repeating this boilerplate code irrads = OrderedDict() irrads['direct_horizontal'] = id_nh irrads['ghi'] = gh irrads['dni'] = id_ irrads['dhi'] = diffuse_horiz if isinstance(irrads['dni'], pd.Series): irrads = pd.DataFrame.from_dict(irrads) return irrads
def ineichen(time, location, linke_turbidity=None, solarposition_method='pyephem', zenith_data=None, airmass_model='young1994', airmass_data=None, interp_turbidity=True): ''' Determine clear sky GHI, DNI, and DHI from Ineichen/Perez model Implements the Ineichen and Perez clear sky model for global horizontal irradiance (GHI), direct normal irradiance (DNI), and calculates the clear-sky diffuse horizontal (DHI) component as the difference between GHI and DNI*cos(zenith) as presented in [1, 2]. A report on clear sky models found the Ineichen/Perez model to have excellent performance with a minimal input data set [3]. Default values for montly Linke turbidity provided by SoDa [4, 5]. Parameters ----------- time : pandas.DatetimeIndex location : pvlib.Location linke_turbidity : None or float If None, uses ``LinkeTurbidities.mat`` lookup table. solarposition_method : string Sets the solar position algorithm. See solarposition.get_solarposition() zenith_data : None or Series If None, ephemeris data will be calculated using ``solarposition_method``. airmass_model : string See pvlib.airmass.relativeairmass(). airmass_data : None or Series If None, absolute air mass data will be calculated using ``airmass_model`` and location.alitude. interp_turbidity : bool If ``True``, interpolates the monthly Linke turbidity values found in ``LinkeTurbidities.mat`` to daily values. Returns -------- DataFrame with the following columns: ``ghi, dni, dhi``. Notes ----- If you are using this function in a loop, it may be faster to load LinkeTurbidities.mat outside of the loop and feed it in as a keyword argument, rather than having the function open and process the file each time it is called. References ---------- [1] P. Ineichen and R. Perez, "A New airmass independent formulation for the Linke turbidity coefficient", Solar Energy, vol 73, pp. 151-157, 2002. [2] R. Perez et. al., "A New Operational Model for Satellite-Derived Irradiances: Description and Validation", Solar Energy, vol 73, pp. 307-317, 2002. [3] M. Reno, C. Hansen, and J. Stein, "Global Horizontal Irradiance Clear Sky Models: Implementation and Analysis", Sandia National Laboratories, SAND2012-2389, 2012. [4] http://www.soda-is.com/eng/services/climat_free_eng.php#c5 (obtained July 17, 2012). [5] J. Remund, et. al., "Worldwide Linke Turbidity Information", Proc. ISES Solar World Congress, June 2003. Goteborg, Sweden. ''' # Initial implementation of this algorithm by Matthew Reno. # Ported to python by Rob Andrews # Added functionality by Will Holmgren (@wholmgren) I0 = irradiance.extraradiation(time.dayofyear) if zenith_data is None: ephem_data = solarposition.get_solarposition( time, location, method=solarposition_method) time = ephem_data.index # fixes issue with time possibly not being tz-aware try: ApparentZenith = ephem_data['apparent_zenith'] except KeyError: ApparentZenith = ephem_data['zenith'] logger.warning('could not find apparent_zenith. using zenith') else: ApparentZenith = zenith_data #ApparentZenith[ApparentZenith >= 90] = 90 # can cause problems in edge cases if linke_turbidity is None: TL = lookup_linke_turbidity(time, location.latitude, location.longitude, interp_turbidity=interp_turbidity) else: TL = linke_turbidity # Get the absolute airmass assuming standard local pressure (per # alt2pres) using Kasten and Young's 1989 formula for airmass. if airmass_data is None: AMabsolute = atmosphere.absoluteairmass( airmass_relative=atmosphere.relativeairmass( ApparentZenith, airmass_model), pressure=atmosphere.alt2pres(location.altitude)) else: AMabsolute = airmass_data fh1 = np.exp(-location.altitude / 8000.) fh2 = np.exp(-location.altitude / 1250.) cg1 = 5.09e-05 * location.altitude + 0.868 cg2 = 3.92e-05 * location.altitude + 0.0387 logger.debug('fh1=%s, fh2=%s, cg1=%s, cg2=%s', fh1, fh2, cg1, cg2) # Dan's note on the TL correction: By my reading of the publication on # pages 151-157, Ineichen and Perez introduce (among other things) three # things. 1) Beam model in eqn. 8, 2) new turbidity factor in eqn 9 and # appendix A, and 3) Global horizontal model in eqn. 11. They do NOT appear # to use the new turbidity factor (item 2 above) in either the beam or GHI # models. The phrasing of appendix A seems as if there are two separate # corrections, the first correction is used to correct the beam/GHI models, # and the second correction is used to correct the revised turibidity # factor. In my estimation, there is no need to correct the turbidity # factor used in the beam/GHI models. # Create the corrected TL for TL < 2 # TLcorr = TL; # TLcorr(TL < 2) = TLcorr(TL < 2) - 0.25 .* (2-TLcorr(TL < 2)) .^ (0.5); # This equation is found in Solar Energy 73, pg 311. # Full ref: Perez et. al., Vol. 73, pp. 307-317 (2002). # It is slightly different than the equation given in Solar Energy 73, pg 156. # We used the equation from pg 311 because of the existence of known typos # in the pg 156 publication (notably the fh2-(TL-1) should be fh2 * (TL-1)). cos_zenith = tools.cosd(ApparentZenith) clearsky_GHI = (cg1 * I0 * cos_zenith * np.exp(-cg2 * AMabsolute * (fh1 + fh2 * (TL - 1))) * np.exp(0.01 * AMabsolute**1.8)) clearsky_GHI[clearsky_GHI < 0] = 0 # BncI == "normal beam clear sky radiation" b = 0.664 + 0.163 / fh1 BncI = b * I0 * np.exp(-0.09 * AMabsolute * (TL - 1)) logger.debug('b=%s', b) # "empirical correction" SE 73, 157 & SE 73, 312. BncI_2 = (clearsky_GHI * (1 - (0.1 - 0.2 * np.exp(-TL)) / (0.1 + 0.882 / fh1)) / cos_zenith) clearsky_DNI = np.minimum(BncI, BncI_2) clearsky_DHI = clearsky_GHI - clearsky_DNI * cos_zenith df_out = pd.DataFrame({ 'ghi': clearsky_GHI, 'dni': clearsky_DNI, 'dhi': clearsky_DHI }) df_out.fillna(0, inplace=True) return df_out
def test_absoluteairmass_nan(): np.testing.assert_equal(np.nan, atmosphere.absoluteairmass(np.nan))
def basic_chain(times, latitude, longitude, module_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. inverter_parameters : None, dict or Series Inverter parameters as defined by the CEC. irradiance : None or DataFrame If None, calculates clear sky data. Columns must be 'dni', 'ghi', 'dhi'. weather : None or DataFrame If None, assumes air temperature is 20 C and wind speed is 0 m/s. Columns must be 'wind_speed', 'temp_air'. surface_tilt : float or Series 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 : float or Series 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 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 Passed to system.get_irradiance. solar_position_method : str Passed to location.get_solarposition. airmass_model : str Passed to location.get_airmass. altitude : None or float If None, computed from pressure. Assumed to be 0 m if pressure is also None. pressure : None or float 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') times = times 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, **kwargs) # possible error with using apparent zenith with some models airmass = atmosphere.relativeairmass(solar_position['apparent_zenith'], model=airmass_model) airmass = atmosphere.absoluteairmass(airmass, pressure) dni_extra = pvlib.irradiance.extraradiation(solar_position.index) dni_extra = pd.Series(dni_extra, index=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.total_irrad( 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} temps = pvsystem.sapm_celltemp(total_irrad['poa_global'], weather['wind_speed'], weather['temp_air']) effective_irradiance = pvsystem.sapm_effective_irradiance( total_irrad['poa_direct'], total_irrad['poa_diffuse'], airmass, aoi, module_parameters) dc = pvsystem.sapm(effective_irradiance, temps['temp_cell'], module_parameters) ac = pvsystem.snlinverter(dc['v_mp'], dc['p_mp'], inverter_parameters) return dc, ac
def ineichen(time, latitude, longitude, altitude=0, linke_turbidity=None, solarposition_method='nrel_numpy', zenith_data=None, airmass_model='young1994', airmass_data=None, interp_turbidity=True): ''' Determine clear sky GHI, DNI, and DHI from Ineichen/Perez model Implements the Ineichen and Perez clear sky model for global horizontal irradiance (GHI), direct normal irradiance (DNI), and calculates the clear-sky diffuse horizontal (DHI) component as the difference between GHI and DNI*cos(zenith) as presented in [1, 2]. A report on clear sky models found the Ineichen/Perez model to have excellent performance with a minimal input data set [3]. Default values for montly Linke turbidity provided by SoDa [4, 5]. Parameters ----------- time : pandas.DatetimeIndex latitude : float longitude : float altitude : float linke_turbidity : None or float If None, uses ``LinkeTurbidities.mat`` lookup table. solarposition_method : string Sets the solar position algorithm. See solarposition.get_solarposition() zenith_data : None or Series If None, ephemeris data will be calculated using ``solarposition_method``. airmass_model : string See pvlib.airmass.relativeairmass(). airmass_data : None or Series If None, absolute air mass data will be calculated using ``airmass_model`` and location.alitude. interp_turbidity : bool If ``True``, interpolates the monthly Linke turbidity values found in ``LinkeTurbidities.mat`` to daily values. Returns -------- DataFrame with the following columns: ``ghi, dni, dhi``. Notes ----- If you are using this function in a loop, it may be faster to load LinkeTurbidities.mat outside of the loop and feed it in as a keyword argument, rather than having the function open and process the file each time it is called. References ---------- [1] P. Ineichen and R. Perez, "A New airmass independent formulation for the Linke turbidity coefficient", Solar Energy, vol 73, pp. 151-157, 2002. [2] R. Perez et. al., "A New Operational Model for Satellite-Derived Irradiances: Description and Validation", Solar Energy, vol 73, pp. 307-317, 2002. [3] M. Reno, C. Hansen, and J. Stein, "Global Horizontal Irradiance Clear Sky Models: Implementation and Analysis", Sandia National Laboratories, SAND2012-2389, 2012. [4] http://www.soda-is.com/eng/services/climat_free_eng.php#c5 (obtained July 17, 2012). [5] J. Remund, et. al., "Worldwide Linke Turbidity Information", Proc. ISES Solar World Congress, June 2003. Goteborg, Sweden. ''' # Initial implementation of this algorithm by Matthew Reno. # Ported to python by Rob Andrews # Added functionality by Will Holmgren (@wholmgren) I0 = irradiance.extraradiation(time.dayofyear) if zenith_data is None: ephem_data = solarposition.get_solarposition(time, latitude=latitude, longitude=longitude, altitude=altitude, method=solarposition_method) time = ephem_data.index # fixes issue with time possibly not being tz-aware try: ApparentZenith = ephem_data['apparent_zenith'] except KeyError: ApparentZenith = ephem_data['zenith'] logger.warning('could not find apparent_zenith. using zenith') else: ApparentZenith = zenith_data #ApparentZenith[ApparentZenith >= 90] = 90 # can cause problems in edge cases if linke_turbidity is None: TL = lookup_linke_turbidity(time, latitude, longitude, interp_turbidity=interp_turbidity) else: TL = linke_turbidity # Get the absolute airmass assuming standard local pressure (per # alt2pres) using Kasten and Young's 1989 formula for airmass. if airmass_data is None: AMabsolute = atmosphere.absoluteairmass(airmass_relative=atmosphere.relativeairmass(ApparentZenith, airmass_model), pressure=atmosphere.alt2pres(altitude)) else: AMabsolute = airmass_data fh1 = np.exp(-altitude/8000.) fh2 = np.exp(-altitude/1250.) cg1 = 5.09e-05 * altitude + 0.868 cg2 = 3.92e-05 * altitude + 0.0387 logger.debug('fh1=%s, fh2=%s, cg1=%s, cg2=%s', fh1, fh2, cg1, cg2) # Dan's note on the TL correction: By my reading of the publication on # pages 151-157, Ineichen and Perez introduce (among other things) three # things. 1) Beam model in eqn. 8, 2) new turbidity factor in eqn 9 and # appendix A, and 3) Global horizontal model in eqn. 11. They do NOT appear # to use the new turbidity factor (item 2 above) in either the beam or GHI # models. The phrasing of appendix A seems as if there are two separate # corrections, the first correction is used to correct the beam/GHI models, # and the second correction is used to correct the revised turibidity # factor. In my estimation, there is no need to correct the turbidity # factor used in the beam/GHI models. # Create the corrected TL for TL < 2 # TLcorr = TL; # TLcorr(TL < 2) = TLcorr(TL < 2) - 0.25 .* (2-TLcorr(TL < 2)) .^ (0.5); # This equation is found in Solar Energy 73, pg 311. # Full ref: Perez et. al., Vol. 73, pp. 307-317 (2002). # It is slightly different than the equation given in Solar Energy 73, pg 156. # We used the equation from pg 311 because of the existence of known typos # in the pg 156 publication (notably the fh2-(TL-1) should be fh2 * (TL-1)). cos_zenith = tools.cosd(ApparentZenith) clearsky_GHI = ( cg1 * I0 * cos_zenith * np.exp(-cg2*AMabsolute*(fh1 + fh2*(TL - 1))) * np.exp(0.01*AMabsolute**1.8) ) clearsky_GHI[clearsky_GHI < 0] = 0 # BncI == "normal beam clear sky radiation" b = 0.664 + 0.163/fh1 BncI = b * I0 * np.exp( -0.09 * AMabsolute * (TL - 1) ) logger.debug('b=%s', b) # "empirical correction" SE 73, 157 & SE 73, 312. BncI_2 = ( clearsky_GHI * ( 1 - (0.1 - 0.2*np.exp(-TL))/(0.1 + 0.882/fh1) ) / cos_zenith ) clearsky_DNI = np.minimum(BncI, BncI_2) clearsky_DHI = clearsky_GHI - clearsky_DNI*cos_zenith df_out = pd.DataFrame({'ghi':clearsky_GHI, 'dni':clearsky_DNI, 'dhi':clearsky_DHI}) df_out.fillna(0, inplace=True) return df_out
def bird(zenith, airmass_relative, aod380, aod500, precipitable_water, ozone=0.3, pressure=101325., dni_extra=1364., asymmetry=0.85, albedo=0.2): """ Bird Simple Clear Sky Broadband Solar Radiation Model Based on NREL Excel implementation by Daryl R. Myers [1, 2]. Bird and Hulstrom define the zenith as the "angle between a line to the sun and the local zenith". There is no distinction in the paper between solar zenith and apparent (or refracted) zenith, but the relative airmass is defined using the Kasten 1966 expression, which requires apparent zenith. Although the formulation for calculated zenith is never explicitly defined in the report, since the purpose was to compare existing clear sky models with "rigorous radiative transfer models" (RTM) it is possible that apparent zenith was obtained as output from the RTM. However, the implentation presented in PVLIB is tested against the NREL Excel implementation by Daryl Myers which uses an analytical expression for solar zenith instead of apparent zenith. Parameters ---------- zenith : numeric Solar or apparent zenith angle in degrees - see note above airmass_relative : numeric Relative airmass aod380 : numeric Aerosol optical depth [cm] measured at 380[nm] aod500 : numeric Aerosol optical depth [cm] measured at 500[nm] precipitable_water : numeric Precipitable water [cm] ozone : numeric Atmospheric ozone [cm], defaults to 0.3[cm] pressure : numeric Ambient pressure [Pa], defaults to 101325[Pa] dni_extra : numeric Extraterrestrial radiation [W/m^2], defaults to 1364[W/m^2] asymmetry : numeric Asymmetry factor, defaults to 0.85 albedo : numeric Albedo, defaults to 0.2 Returns ------- clearsky : DataFrame (if Series input) or OrderedDict of arrays DataFrame/OrderedDict contains the columns/keys ``'dhi', 'dni', 'ghi', 'direct_horizontal'`` in [W/m^2]. See also -------- pvlib.atmosphere.bird_hulstrom80_aod_bb pvlib.atmosphere.relativeairmass References ---------- [1] R. E. Bird and R. L Hulstrom, "A Simplified Clear Sky model for Direct and Diffuse Insolation on Horizontal Surfaces" SERI Technical Report SERI/TR-642-761, Feb 1981. Solar Energy Research Institute, Golden, CO. [2] Daryl R. Myers, "Solar Radiation: Practical Modeling for Renewable Energy Applications", pp. 46-51 CRC Press (2013) `NREL Bird Clear Sky Model <http://rredc.nrel.gov/solar/models/clearsky/>`_ `SERI/TR-642-761 <http://rredc.nrel.gov/solar/pubs/pdfs/tr-642-761.pdf>`_ `Error Reports <http://rredc.nrel.gov/solar/models/clearsky/error_reports.html>`_ """ etr = dni_extra # extraradiation ze_rad = np.deg2rad(zenith) # zenith in radians airmass = airmass_relative # Bird clear sky model am_press = atmosphere.absoluteairmass(airmass, pressure) t_rayleigh = ( np.exp(-0.0903 * am_press ** 0.84 * ( 1.0 + am_press - am_press ** 1.01 )) ) am_o3 = ozone*airmass t_ozone = ( 1.0 - 0.1611 * am_o3 * (1.0 + 139.48 * am_o3) ** -0.3034 - 0.002715 * am_o3 / (1.0 + 0.044 * am_o3 + 0.0003 * am_o3 ** 2.0) ) t_gases = np.exp(-0.0127 * am_press ** 0.26) am_h2o = airmass * precipitable_water t_water = ( 1.0 - 2.4959 * am_h2o / ( (1.0 + 79.034 * am_h2o) ** 0.6828 + 6.385 * am_h2o ) ) bird_huldstrom = atmosphere.bird_hulstrom80_aod_bb(aod380, aod500) t_aerosol = np.exp( -(bird_huldstrom ** 0.873) * (1.0 + bird_huldstrom - bird_huldstrom ** 0.7088) * airmass ** 0.9108 ) taa = 1.0 - 0.1 * (1.0 - airmass + airmass ** 1.06) * (1.0 - t_aerosol) rs = 0.0685 + (1.0 - asymmetry) * (1.0 - t_aerosol / taa) id_ = 0.9662 * etr * t_aerosol * t_water * t_gases * t_ozone * t_rayleigh ze_cos = np.where(zenith < 90, np.cos(ze_rad), 0.0) id_nh = id_ * ze_cos ias = ( etr * ze_cos * 0.79 * t_ozone * t_gases * t_water * taa * (0.5 * (1.0 - t_rayleigh) + asymmetry * (1.0 - (t_aerosol / taa))) / ( 1.0 - airmass + airmass ** 1.02 ) ) gh = (id_nh + ias) / (1.0 - albedo * rs) diffuse_horiz = gh - id_nh # TODO: be DRY, use decorator to wrap methods that need to return either # OrderedDict or DataFrame instead of repeating this boilerplate code irrads = OrderedDict() irrads['direct_horizontal'] = id_nh irrads['ghi'] = gh irrads['dni'] = id_ irrads['dhi'] = diffuse_horiz if isinstance(irrads['dni'], pd.Series): irrads = pd.DataFrame.from_dict(irrads) return irrads
def test_deprecated_07(): with pytest.warns(pvlibDeprecationWarning): atmosphere.relativeairmass(2) with pytest.warns(pvlibDeprecationWarning): atmosphere.absoluteairmass(2)
def test_absoluteairmass_numeric(): atmosphere.absoluteairmass(2)
def ineichen( time, location, linke_turbidity=None, solarposition_method="pyephem", zenith_data=None, airmass_model="young1994", airmass_data=None, interp_turbidity=True, ): """ Determine clear sky GHI, DNI, and DHI from Ineichen/Perez model Implements the Ineichen and Perez clear sky model for global horizontal irradiance (GHI), direct normal irradiance (DNI), and calculates the clear-sky diffuse horizontal (DHI) component as the difference between GHI and DNI*cos(zenith) as presented in [1, 2]. A report on clear sky models found the Ineichen/Perez model to have excellent performance with a minimal input data set [3]. Default values for montly Linke turbidity provided by SoDa [4, 5]. Parameters ----------- time : pandas.DatetimeIndex location : pvlib.Location linke_turbidity : None or float If None, uses ``LinkeTurbidities.mat`` lookup table. solarposition_method : string Sets the solar position algorithm. See solarposition.get_solarposition() zenith_data : None or pandas.Series If None, ephemeris data will be calculated using ``solarposition_method``. airmass_model : string See pvlib.airmass.relativeairmass(). airmass_data : None or pandas.Series If None, absolute air mass data will be calculated using ``airmass_model`` and location.alitude. interp_turbidity : bool If ``True``, interpolates the monthly Linke turbidity values found in ``LinkeTurbidities.mat`` to daily values. Returns -------- DataFrame with the following columns: ``GHI, DNI, DHI``. Notes ----- If you are using this function in a loop, it may be faster to load LinkeTurbidities.mat outside of the loop and feed it in as a variable, rather than having the function open the file each time it is called. References ---------- [1] P. Ineichen and R. Perez, "A New airmass independent formulation for the Linke turbidity coefficient", Solar Energy, vol 73, pp. 151-157, 2002. [2] R. Perez et. al., "A New Operational Model for Satellite-Derived Irradiances: Description and Validation", Solar Energy, vol 73, pp. 307-317, 2002. [3] M. Reno, C. Hansen, and J. Stein, "Global Horizontal Irradiance Clear Sky Models: Implementation and Analysis", Sandia National Laboratories, SAND2012-2389, 2012. [4] http://www.soda-is.com/eng/services/climat_free_eng.php#c5 (obtained July 17, 2012). [5] J. Remund, et. al., "Worldwide Linke Turbidity Information", Proc. ISES Solar World Congress, June 2003. Goteborg, Sweden. """ # Initial implementation of this algorithm by Matthew Reno. # Ported to python by Rob Andrews # Added functionality by Will Holmgren I0 = irradiance.extraradiation(time.dayofyear) if zenith_data is None: ephem_data = solarposition.get_solarposition(time, location, method=solarposition_method) time = ephem_data.index # fixes issue with time possibly not being tz-aware try: ApparentZenith = ephem_data["apparent_zenith"] except KeyError: ApparentZenith = ephem_data["zenith"] logger.warning("could not find apparent_zenith. using zenith") else: ApparentZenith = zenith_data # ApparentZenith[ApparentZenith >= 90] = 90 # can cause problems in edge cases if linke_turbidity is None: # The .mat file 'LinkeTurbidities.mat' contains a single 2160 x 4320 x 12 # matrix of type uint8 called 'LinkeTurbidity'. The rows represent global # latitudes from 90 to -90 degrees; the columns represent global longitudes # from -180 to 180; and the depth (third dimension) represents months of # the year from January (1) to December (12). To determine the Linke # turbidity for a position on the Earth's surface for a given month do the # following: LT = LinkeTurbidity(LatitudeIndex, LongitudeIndex, month). # Note that the numbers within the matrix are 20 * Linke Turbidity, # so divide the number from the file by 20 to get the # turbidity. try: import scipy.io except ImportError: raise ImportError( "The Linke turbidity lookup table requires scipy. " + "You can still use clearsky.ineichen if you " + "supply your own turbidities." ) # consider putting this code at module level this_path = os.path.dirname(os.path.abspath(__file__)) logger.debug("this_path={}".format(this_path)) mat = scipy.io.loadmat(os.path.join(this_path, "data", "LinkeTurbidities.mat")) linke_turbidity = mat["LinkeTurbidity"] LatitudeIndex = np.round_(_linearly_scale(location.latitude, 90, -90, 1, 2160)) LongitudeIndex = np.round_(_linearly_scale(location.longitude, -180, 180, 1, 4320)) g = linke_turbidity[LatitudeIndex][LongitudeIndex] if interp_turbidity: logger.info("interpolating turbidity to the day") g2 = np.concatenate([[g[-1]], g, [g[0]]]) # wrap ends around days = np.linspace(-15, 380, num=14) # map day of year onto month (approximate) LT = pd.Series(np.interp(time.dayofyear, days, g2), index=time) else: logger.info("using monthly turbidity") ApplyMonth = lambda x: g[x[0] - 1] LT = pd.DataFrame(time.month, index=time) LT = LT.apply(ApplyMonth, axis=1) TL = LT / 20.0 logger.info("using TL=\n{}".format(TL)) else: TL = linke_turbidity # Get the absolute airmass assuming standard local pressure (per # alt2pres) using Kasten and Young's 1989 formula for airmass. if airmass_data is None: AMabsolute = atmosphere.absoluteairmass( AMrelative=atmosphere.relativeairmass(ApparentZenith, airmass_model), pressure=atmosphere.alt2pres(location.altitude), ) else: AMabsolute = airmass_data fh1 = np.exp(-location.altitude / 8000.0) fh2 = np.exp(-location.altitude / 1250.0) cg1 = 5.09e-05 * location.altitude + 0.868 cg2 = 3.92e-05 * location.altitude + 0.0387 logger.debug("fh1={}, fh2={}, cg1={}, cg2={}".format(fh1, fh2, cg1, cg2)) # Dan's note on the TL correction: By my reading of the publication on # pages 151-157, Ineichen and Perez introduce (among other things) three # things. 1) Beam model in eqn. 8, 2) new turbidity factor in eqn 9 and # appendix A, and 3) Global horizontal model in eqn. 11. They do NOT appear # to use the new turbidity factor (item 2 above) in either the beam or GHI # models. The phrasing of appendix A seems as if there are two separate # corrections, the first correction is used to correct the beam/GHI models, # and the second correction is used to correct the revised turibidity # factor. In my estimation, there is no need to correct the turbidity # factor used in the beam/GHI models. # Create the corrected TL for TL < 2 # TLcorr = TL; # TLcorr(TL < 2) = TLcorr(TL < 2) - 0.25 .* (2-TLcorr(TL < 2)) .^ (0.5); # This equation is found in Solar Energy 73, pg 311. # Full ref: Perez et. al., Vol. 73, pp. 307-317 (2002). # It is slightly different than the equation given in Solar Energy 73, pg 156. # We used the equation from pg 311 because of the existence of known typos # in the pg 156 publication (notably the fh2-(TL-1) should be fh2 * (TL-1)). cos_zenith = tools.cosd(ApparentZenith) clearsky_GHI = ( cg1 * I0 * cos_zenith * np.exp(-cg2 * AMabsolute * (fh1 + fh2 * (TL - 1))) * np.exp(0.01 * AMabsolute ** 1.8) ) clearsky_GHI[clearsky_GHI < 0] = 0 # BncI == "normal beam clear sky radiation" b = 0.664 + 0.163 / fh1 BncI = b * I0 * np.exp(-0.09 * AMabsolute * (TL - 1)) logger.debug("b={}".format(b)) # "empirical correction" SE 73, 157 & SE 73, 312. BncI_2 = clearsky_GHI * (1 - (0.1 - 0.2 * np.exp(-TL)) / (0.1 + 0.882 / fh1)) / cos_zenith # return BncI, BncI_2 clearsky_DNI = np.minimum(BncI, BncI_2) # Will H: use np.minimum explicitly clearsky_DHI = clearsky_GHI - clearsky_DNI * cos_zenith df_out = pd.DataFrame({"GHI": clearsky_GHI, "DNI": clearsky_DNI, "DHI": clearsky_DHI}) df_out.fillna(0, inplace=True) # df_out['BncI'] = BncI # df_out['BncI_2'] = BncI return df_out
def ineichen(time, location, linke_turbidity=None, solarposition_method='pyephem', zenith_data=None, airmass_model='young1994', airmass_data=None, interp_turbidity=True): ''' Determine clear sky GHI, DNI, and DHI from Ineichen/Perez model Implements the Ineichen and Perez clear sky model for global horizontal irradiance (GHI), direct normal irradiance (DNI), and calculates the clear-sky diffuse horizontal (DHI) component as the difference between GHI and DNI*cos(zenith) as presented in [1, 2]. A report on clear sky models found the Ineichen/Perez model to have excellent performance with a minimal input data set [3]. Default values for montly Linke turbidity provided by SoDa [4, 5]. Parameters ----------- time : pandas.DatetimeIndex location : pvlib.Location linke_turbidity : None or float If None, uses ``LinkeTurbidities.mat`` lookup table. solarposition_method : string Sets the solar position algorithm. See solarposition.get_solarposition() zenith_data : None or pandas.Series If None, ephemeris data will be calculated using ``solarposition_method``. airmass_model : string See pvlib.airmass.relativeairmass(). airmass_data : None or pandas.Series If None, absolute air mass data will be calculated using ``airmass_model`` and location.alitude. interp_turbidity : bool If ``True``, interpolates the monthly Linke turbidity values found in ``LinkeTurbidities.mat`` to daily values. Returns -------- DataFrame with the following columns: ``GHI, DNI, DHI``. Notes ----- If you are using this function in a loop, it may be faster to load LinkeTurbidities.mat outside of the loop and feed it in as a variable, rather than having the function open the file each time it is called. References ---------- [1] P. Ineichen and R. Perez, "A New airmass independent formulation for the Linke turbidity coefficient", Solar Energy, vol 73, pp. 151-157, 2002. [2] R. Perez et. al., "A New Operational Model for Satellite-Derived Irradiances: Description and Validation", Solar Energy, vol 73, pp. 307-317, 2002. [3] M. Reno, C. Hansen, and J. Stein, "Global Horizontal Irradiance Clear Sky Models: Implementation and Analysis", Sandia National Laboratories, SAND2012-2389, 2012. [4] http://www.soda-is.com/eng/services/climat_free_eng.php#c5 (obtained July 17, 2012). [5] J. Remund, et. al., "Worldwide Linke Turbidity Information", Proc. ISES Solar World Congress, June 2003. Goteborg, Sweden. ''' # Initial implementation of this algorithm by Matthew Reno. # Ported to python by Rob Andrews # Added functionality by Will Holmgren I0 = irradiance.extraradiation(time.dayofyear) if zenith_data is None: ephem_data = solarposition.get_solarposition( time, location, method=solarposition_method) time = ephem_data.index # fixes issue with time possibly not being tz-aware try: ApparentZenith = ephem_data['apparent_zenith'] except KeyError: ApparentZenith = ephem_data['zenith'] logger.warning('could not find apparent_zenith. using zenith') else: ApparentZenith = zenith_data #ApparentZenith[ApparentZenith >= 90] = 90 # can cause problems in edge cases if linke_turbidity is None: # The .mat file 'LinkeTurbidities.mat' contains a single 2160 x 4320 x 12 # matrix of type uint8 called 'LinkeTurbidity'. The rows represent global # latitudes from 90 to -90 degrees; the columns represent global longitudes # from -180 to 180; and the depth (third dimension) represents months of # the year from January (1) to December (12). To determine the Linke # turbidity for a position on the Earth's surface for a given month do the # following: LT = LinkeTurbidity(LatitudeIndex, LongitudeIndex, month). # Note that the numbers within the matrix are 20 * Linke Turbidity, # so divide the number from the file by 20 to get the # turbidity. try: import scipy.io except ImportError: raise ImportError( 'The Linke turbidity lookup table requires scipy. ' + 'You can still use clearsky.ineichen if you ' + 'supply your own turbidities.') # consider putting this code at module level this_path = os.path.dirname(os.path.abspath(__file__)) logger.debug('this_path={}'.format(this_path)) mat = scipy.io.loadmat( os.path.join(this_path, 'data', 'LinkeTurbidities.mat')) linke_turbidity = mat['LinkeTurbidity'] LatitudeIndex = np.round_( _linearly_scale(location.latitude, 90, -90, 1, 2160)) LongitudeIndex = np.round_( _linearly_scale(location.longitude, -180, 180, 1, 4320)) g = linke_turbidity[LatitudeIndex][LongitudeIndex] if interp_turbidity: logger.info('interpolating turbidity to the day') g2 = np.concatenate([[g[-1]], g, [g[0]]]) # wrap ends around days = np.linspace( -15, 380, num=14) # map day of year onto month (approximate) LT = pd.Series(np.interp(time.dayofyear, days, g2), index=time) else: logger.info('using monthly turbidity') ApplyMonth = lambda x: g[x[0] - 1] LT = pd.DataFrame(time.month, index=time) LT = LT.apply(ApplyMonth, axis=1) TL = LT / 20. logger.info('using TL=\n{}'.format(TL)) else: TL = linke_turbidity # Get the absolute airmass assuming standard local pressure (per # alt2pres) using Kasten and Young's 1989 formula for airmass. if airmass_data is None: AMabsolute = atmosphere.absoluteairmass( AMrelative=atmosphere.relativeairmass(ApparentZenith, airmass_model), pressure=atmosphere.alt2pres(location.altitude)) else: AMabsolute = airmass_data fh1 = np.exp(-location.altitude / 8000.) fh2 = np.exp(-location.altitude / 1250.) cg1 = 5.09e-05 * location.altitude + 0.868 cg2 = 3.92e-05 * location.altitude + 0.0387 logger.debug('fh1={}, fh2={}, cg1={}, cg2={}'.format(fh1, fh2, cg1, cg2)) # Dan's note on the TL correction: By my reading of the publication on # pages 151-157, Ineichen and Perez introduce (among other things) three # things. 1) Beam model in eqn. 8, 2) new turbidity factor in eqn 9 and # appendix A, and 3) Global horizontal model in eqn. 11. They do NOT appear # to use the new turbidity factor (item 2 above) in either the beam or GHI # models. The phrasing of appendix A seems as if there are two separate # corrections, the first correction is used to correct the beam/GHI models, # and the second correction is used to correct the revised turibidity # factor. In my estimation, there is no need to correct the turbidity # factor used in the beam/GHI models. # Create the corrected TL for TL < 2 # TLcorr = TL; # TLcorr(TL < 2) = TLcorr(TL < 2) - 0.25 .* (2-TLcorr(TL < 2)) .^ (0.5); # This equation is found in Solar Energy 73, pg 311. # Full ref: Perez et. al., Vol. 73, pp. 307-317 (2002). # It is slightly different than the equation given in Solar Energy 73, pg 156. # We used the equation from pg 311 because of the existence of known typos # in the pg 156 publication (notably the fh2-(TL-1) should be fh2 * (TL-1)). cos_zenith = tools.cosd(ApparentZenith) clearsky_GHI = cg1 * I0 * cos_zenith * np.exp( -cg2 * AMabsolute * (fh1 + fh2 * (TL - 1))) * np.exp(0.01 * AMabsolute**1.8) clearsky_GHI[clearsky_GHI < 0] = 0 # BncI == "normal beam clear sky radiation" b = 0.664 + 0.163 / fh1 BncI = b * I0 * np.exp(-0.09 * AMabsolute * (TL - 1)) logger.debug('b={}'.format(b)) # "empirical correction" SE 73, 157 & SE 73, 312. BncI_2 = clearsky_GHI * (1 - (0.1 - 0.2 * np.exp(-TL)) / (0.1 + 0.882 / fh1)) / cos_zenith #return BncI, BncI_2 clearsky_DNI = np.minimum(BncI, BncI_2) # Will H: use np.minimum explicitly clearsky_DHI = clearsky_GHI - clearsky_DNI * cos_zenith df_out = pd.DataFrame({ 'GHI': clearsky_GHI, 'DNI': clearsky_DNI, 'DHI': clearsky_DHI }) df_out.fillna(0, inplace=True) #df_out['BncI'] = BncI #df_out['BncI_2'] = BncI return df_out
def basic_chain(times, latitude, longitude, module_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. inverter_parameters : None, dict or Series Inverter parameters as defined by the CEC. irradiance : None or DataFrame If None, calculates clear sky data. Columns must be 'dni', 'ghi', 'dhi'. weather : None or DataFrame If None, assumes air temperature is 20 C and wind speed is 0 m/s. Columns must be 'wind_speed', 'temp_air'. surface_tilt : float or Series 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 : float or Series 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 The strategy for aligning the modules. If not None, sets the ``surface_azimuth`` and ``surface_tilt`` properties of the ``system``. transposition_model : str Passed to system.get_irradiance. solar_position_method : str Passed to location.get_solarposition. airmass_model : str Passed to location.get_airmass. altitude : None or float If None, computed from pressure. Assumed to be 0 m if pressure is also None. pressure : None or float 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') times = times 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, **kwargs) # possible error with using apparent zenith with some models airmass = atmosphere.relativeairmass(solar_position['apparent_zenith'], model=airmass_model) airmass = atmosphere.absoluteairmass(airmass, pressure) dni_extra = pvlib.irradiance.extraradiation(solar_position.index) dni_extra = pd.Series(dni_extra, index=solar_position.index) aoi = pvlib.irradiance.aoi(surface_tilt, surface_azimuth, solar_position['apparent_zenith'], solar_position['azimuth']) if irradiance is None: irradiance = clearsky.ineichen( solar_position.index, latitude, longitude, zenith_data=solar_position['apparent_zenith'], airmass_data=airmass, altitude=altitude) total_irrad = pvlib.irradiance.total_irrad( 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} temps = pvsystem.sapm_celltemp(total_irrad['poa_global'], weather['wind_speed'], weather['temp_air']) dc = pvsystem.sapm(module_parameters, total_irrad['poa_direct'], total_irrad['poa_diffuse'], temps['temp_cell'], airmass, aoi) ac = pvsystem.snlinverter(inverter_parameters, dc['v_mp'], dc['p_mp']) return dc, ac
def test_absoluteairmass(): relative_am = atmosphere.relativeairmass(ephem_data["zenith"], "simple") atmosphere.absoluteairmass(relative_am) atmosphere.absoluteairmass(relative_am, pressure=100000)