From 9a1da24918d501dbd9bcc6e98a484dc350a7be1e Mon Sep 17 00:00:00 2001 From: cwhanse Date: Wed, 22 Jun 2016 15:07:31 -0600 Subject: [PATCH 1/6] Add pvl_robustfit MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Added pvl_robustfit.m to provide an option for pvl_desoto_parameter_estimation and pvl_PVsyst_parameter_estimation that doesn’t rely on the Matlab Statistics Toolbox. Added py_rlm.py which is the Python code used if the Statistics Toolbox is not present. --- pvl_PVsyst_parameter_estimation.m | 34 +++++++++------ pvl_desoto_parameter_estimation.m | 23 ++++++---- pvl_rectify_IV_curve.m | 8 +++- pvl_robustfit.m | 70 +++++++++++++++++++++++++++++++ py_rlm.py | 33 +++++++++++++++ 5 files changed, 144 insertions(+), 24 deletions(-) create mode 100644 pvl_robustfit.m create mode 100644 py_rlm.py diff --git a/pvl_PVsyst_parameter_estimation.m b/pvl_PVsyst_parameter_estimation.m index 33c72fa..dcea0c6 100644 --- a/pvl_PVsyst_parameter_estimation.m +++ b/pvl_PVsyst_parameter_estimation.m @@ -322,7 +322,7 @@ T0K = Const.T0 + 273.15; % convert T0 to K X = Const.q/Const.k*(1/T0K - 1./TcK(u))./gamma(u); Y = log(Io(u))-3*log(TcK(u)/T0K); - beta = robustfit(X,Y); + beta = pvl_robustfit(X,Y,true); Io0 = exp(beta(1)); eG = beta(2); @@ -353,7 +353,7 @@ plot(Tc(u),(pIo-Io(u))./Io(u)*100,'x') xlabel('Cell temp. (C)') ylabel('Percent Deviation in I_O') - refline(0,0) +% line(mx, [0 0]); figure('Position',[1 1 600 300]) plot(Tc(u),Y + 3*(Tc(u)/Const.T0),'k.') @@ -379,14 +379,15 @@ % Estimate Iph0 X = (Tc(u)-Const.T0); Y = Iph(u).*(Const.E0./Ee(u)); - Iph0 = nanmean(Y-Specs.aIsc*X); + nans = isnan(Y-Specs.aIsc*X); % average over non-NaN values of Y and X + Iph0 = mean(Y(~nans)-Specs.aIsc*X(~nans)); if graphic % predict Iph pIph = (Ee(u)/Const.E0).*(Iph0+Specs.aIsc*(Tc(u)-Const.T0)); figure subplot(311) - plot(Ee(u), Y,'r+') + plot(Ee(u), pIph,'r+') hold all line([0 max(Ee(u))],[Iph0 Iph0]) xlabel('Irradiance (W/m^2)') @@ -406,7 +407,7 @@ plot(Ee(u),(pIph-Iph(u))./Iph(u)*100,'x') xlabel('Irradiance (W/m^2)') ylabel('Percent Deviation from I_{ L}') - refline(0,0) + line(xlim, [0 0]); figure plot(Tc(u),Iph(u),'b+'); @@ -428,14 +429,15 @@ % Estimate Rsh0, Rsh_ref and Rshexp % Initial guesses. Rsh0 is value at Ee=0. + nans = isnan(Rsh); if any(Ee<400) - gRsh0 = nanmean(Rsh(Ee<400)); + gRsh0 = mean(Rsh(~nans&Ee<400)); else gRsh0 = max(Rsh); end % Rsh_ref is value at Ee=1000 if any(Ee>400) - gRshref = nanmean(Rsh(Ee>400)); + gRshref = mean(Rsh(~nans&Ee>400)); else gRshref = min(Rsh); end @@ -445,10 +447,16 @@ % Here we use a nonlinear least squares technique. lsqnonlin minimizes the % sum of squares of the objective function (here, tf). x0 = [gRsh0 gRshref]; - tf = @(x) log10(estRsh(x,Rshexp,Ee(u),Const.E0)) - log10(Rsh(u)); - options = optimset('Display','off'); % Notify only on non-convergence - beta = lsqnonlin(tf,x0,[1 1],[1e7 1e6],options); - + tmp = which('lsqnonlin'); + if ~isempty(tmp) + tf = @(x) log10(estRsh(x,Rshexp,Ee(u),Const.E0)) - log10(Rsh(u)); + options = optimset('Display','off'); % Notify only on non-convergence + beta = lsqnonlin(tf,x0,[1 1],[1e7 1e6],options); + else + tf = @(x) sum((log10(estRsh(x,Rshexp,Ee(u),Const.E0)) - log10(Rsh(u))).^2); + beta = fminsearch(tf,x0); + end + % Extract PVsyst parameter values Rsh0 = beta(1); Rshref = beta(2); @@ -471,7 +479,7 @@ plot(Ee(u),(log10(pRsh(u)) - log10(Rsh(u)))./log10(Rsh(u))*100,'x') xlabel('Irradiance (W/m^2)') ylabel('Percent Deviation in log_{10}(R_{sh})') - refline(0,0) + line(xlim, [0 0]); % ylim([-35 15]) figure @@ -507,7 +515,7 @@ plot(Ee(u),(Rs0-Rs(u))./Rs(u)*100,'x') xlabel('Irradiance (W/m^2)') ylabel('Percent Deviation in R_S') - refline(0,0) + line(xlim, [0 0]); figure plot(Ee(u&v),Rs(u&v),'b.') diff --git a/pvl_desoto_parameter_estimation.m b/pvl_desoto_parameter_estimation.m index 96359aa..064280d 100644 --- a/pvl_desoto_parameter_estimation.m +++ b/pvl_desoto_parameter_estimation.m @@ -3,7 +3,7 @@ % module performance model % % Syntax -% [Model Iph Io Rsh Rs u] = pvl_desoto_parameter_estimation(IVCurves, Specs, Const, maxiter, eps1, graphic, n) +% [Model] = pvl_desoto_parameter_estimation(IVCurves, Specs, Const, maxiter, eps1, graphic, n) % % Description % pvl_desoto_parameter_estimation estimates parameters for the De Soto module @@ -110,7 +110,7 @@ % See [2], Step 2 or [3] Step 2. X = Specs.Ns*Vth.*log(Ee/Const.E0); Y = Voc - Specs.bVoc*(Tc-Const.T0); - beta = robustfit(X,Y); + beta = pvl_robustfit(X,Y,true); Voc0 = beta(1); n = beta(2); @@ -280,7 +280,7 @@ T0K = Const.T0 + 273.15; % convert T0 to K X = 1/Const.keV*(1/T0K - 1./TcK(u) + Const.dEgdT*(TcK(u)-T0K)./TcK(u)); Y = log(Io(u))-3*log(TcK(u)/T0K); -beta = robustfit(X,Y); +beta = pvl_robustfit(X,Y,true); Io0 = exp(beta(1)); Eg0 = beta(2); @@ -312,7 +312,8 @@ plot(Tc(u),(pIo-Io(u))./Io(u)*100,'x') xlabel('Cell temp. (C)') ylabel('Percent Deviation in I_O') - refline(0,0) + [mx, Mx] = xlim; + line([mx Mx],[0 0]); figure('Position',[1 1 600 300]) plot(Tc(u),Y + 3*(Tc(u)/Const.T0),'k.') @@ -336,7 +337,7 @@ % Estimate Iph0 X = (Tc(u)-Const.T0); Y = Iph(u).*(Const.E0./Ee(u)); -beta = robustfit(X,Y); +beta = pvl_robustfit(X,Y,true); Iph0 = beta(1); if graphic @@ -365,7 +366,8 @@ plot(Ee(u),(pIph-Iph(u))./Iph(u)*100,'x') xlabel('Irradiance (W/m^2)') ylabel('Percent Deviation from I_{ L}') - refline(0,0) + mx = xlim; + line(mx,[0 0]); end % Additional filter for Rsh and Rs; restrict effective irradiance to be @@ -376,7 +378,8 @@ % Estimate Rsh0 Y = Rsh(u&v); X = Const.E0./Ee(u&v); -beta = robustfit(X,Y,'bisquare',4.685,'off'); % const is 'off' to omit the intercept. +beta = pvl_robustfit(X,Y,false); +%beta = pvl_robustfit(X,Y,'bisquare',4.685,'off'); % const is 'off' to omit the intercept. Rsh0 = beta(1); if graphic @@ -397,7 +400,8 @@ plot(Ee(u),(log10(pRsh) - log10(Rsh(u)))./log10(Rsh(u))*100,'x') xlabel('Irradiance (W/m^2)') ylabel('Percent Deviation in log_{10}(R_{sh})') - refline(0,0) + mx = xlim; + line(mx,[0 0]); ylim([-35 15]) end @@ -420,7 +424,8 @@ plot(Ee(u),(Rs0-Rs(u))./Rs(u)*100,'x') xlabel('Irradiance (W/m^2)') ylabel('Percent Deviation in R_S') - refline(0,0) + mx = xlim; + line(mx,[0 0]) end % Set diode factor diff --git a/pvl_rectify_IV_curve.m b/pvl_rectify_IV_curve.m index a5ef113..dc0e69a 100644 --- a/pvl_rectify_IV_curve.m +++ b/pvl_rectify_IV_curve.m @@ -1,4 +1,4 @@ -function [I V] = pvl_rectify_IV_curve(tI, tV, Voc, Isc) +function [I, V] = pvl_rectify_IV_curve(tI, tV, Voc, Isc) % PVL_RECTIFY_IV_CURVE ensures that Isc and Voc are included in a IV curve % and removes duplicate voltage and current points % @@ -25,6 +25,10 @@ % I, V - vectors of equal length containing current and voltage, % respectively +if isnan(Isc) || isnan(Voc) + return +end + % Filter out negative voltage and current values u=tV0 & tI>0; @@ -33,7 +37,7 @@ I=[Isc; tI(u); 0]; % remove duplicate Voltage points -[C,ia,ic] = unique(V); +[~,ia,ic] = unique(V); c = hist(ic,1:length(ia)); cfil = c>1; ind = find(cfil); diff --git a/pvl_robustfit.m b/pvl_robustfit.m new file mode 100644 index 0000000..c5570f3 --- /dev/null +++ b/pvl_robustfit.m @@ -0,0 +1,70 @@ +function beta = pvl_robustfit(X, Y, intercept) + +% PVL_ROBUSTFIT Regress Y onto X using iteratively reweighted least squares +% +% Syntax +% beta = pvl_robustfit(X, Y, intercept) +% +% Description +% PVL_ROBUSTFIT uses the Matlab function robustfit if the Statistics +% toolbox is available. Otherwise it uses the RLM function in the Python +% statsmodels package, which requires a Python installation. +% +% Inputs +% X - must be a N x M matrix, where M is the number of predictors and N is +% the number of observations +% Y - must be N x 1 vector +% intercept - an integer, 0 indicates no intercept, any other value +% indicates that the regression has an intercept term +% +% Output +% beta - a vector of coefficients, M x 1 if no intercept is specified, +% (M+1) x 1 if an intercept is specified. The coefficient for the +% intercept (if present) is beta(1). + +% See if robustfit is available (requires statistics toolbox) +tmp = which('robustfit'); + +if isempty(tmp) + % need python code + msg = 'Statistics toolbox not found'; + disp(msg); + + % check for python installation + tmp2 = dos('python --version'); + + if tmp2==0 + % found python + msg = 'Found Python installation; using statsmodels package'; + disp(msg); + + loc = which('py_rlm.py'); % find python script + + tmpdir = getenv('TEMP'); + + f1 = fopen([tmpdir '\temp_rlm_config.txt'],'w'); + fprintf(f1,'%i\n',intercept); + fclose(f1); + + csvwrite([tmpdir '\temp_rlm_input.csv'],[X Y]) + + dos(['python ' loc]); + + beta = csvread([tmpdir '\temp_rlm_output.csv']); + + % clean up temporary files + delete([tmpdir '\temp_rlm_config.txt'], [tmpdir '\temp_rlm_input.csv'], [tmpdir '\temp_rlm_output.csv']); + + else + msg = 'Python installation not found'; + disp(msg); + beta = NaN; + end + +else + if intercept + beta = robustfit(X,Y); + else + beta = robustfit(X,Y,'bisquare',4.685,'off'); % const is 'off' to omit the intercept. + end +end diff --git a/py_rlm.py b/py_rlm.py new file mode 100644 index 0000000..1b56a3a --- /dev/null +++ b/py_rlm.py @@ -0,0 +1,33 @@ +def rlm(y,x,inter): + + import statsmodels.api as sm + import numpy as np + + if inter: + x = sm.add_constant(x) + + rlmod = sm.RLM(y,x,M= sm.robust.norms.TukeyBiweight()) + res = rlmod.fit() + return res.params + + +import numpy as np +import csv +import os + +wdir = os.getenv('TEMP') + +os.chdir(wdir) + +f1 = open('temp_rlm_config.txt') +intercept = int(f1.readline()); # 0 if no intercept is to be used +f1.close() + +dat = np.loadtxt('temp_rlm_input.csv',delimiter=',') + +X = dat[:,0:-1] +Y = dat[:,-1] + +pp = rlm(Y,X,intercept) + +np.savetxt('temp_rlm_output.csv', pp) From e55a02fbd2a179ef386eb84f4afb3c8c681ca1dd Mon Sep 17 00:00:00 2001 From: cwhanse Date: Wed, 22 Jun 2016 15:15:25 -0600 Subject: [PATCH 2/6] Huld model parameter estimation Add new function --- pvl_huld_parameter_estimation.m | 38 +++++++++++++++++++++++++++++++++ 1 file changed, 38 insertions(+) create mode 100644 pvl_huld_parameter_estimation.m diff --git a/pvl_huld_parameter_estimation.m b/pvl_huld_parameter_estimation.m new file mode 100644 index 0000000..daa720d --- /dev/null +++ b/pvl_huld_parameter_estimation.m @@ -0,0 +1,38 @@ +function [Model] = pvl_huld_parameter_estimation(Pmp, Ee, Tm) +% PVL_HULD_PARAMETER_ESTIMATION estimates parameters for the Huld module performance model +% +% Syntax +% [Model] = pvl_huld_parameter_estimation(Pmp, Ee, Tm) +% +% Description +% pvl_huld_parameter_estimation estimates parameters for the Huld module +% performance model [1]. The estimation uses robust regression to fit the +% Huld model, a polynomial in Tm and log(Ee), to Pmp. +% +% Input: +% Pmp - a N x 1 vector of power (W) at the maximum power point. +% Ee - a N x 1 vector of effective irradiance (suns). +% Tm - a N x 1 vector of module (not cell) temperature (C). +% +% Output: +% Model - a structure containing the model parameters +% Model.Pmp0 - estimated Pmp at STC. +% Model.k - a vector of length 6 containing the coefficients k1 through k6. +% +% Sources: +% [1] A power-rating model for crystalline silicon PV modules, T. Huld, +% G. Friesen, A. Skoczek, R. Kenny, T. Sample, M. Field, E. Dunlop, Solar +% Energy Materials and Solar Cells 95(2011), pp 3359-3369. + + +Y = Pmp./(Ee); + +x1 = log(Ee); +x2 = Tm - 25; + +beta = pvl_robustfit([x1 x1.^2 x2 x2.*x1 x2.*x1.^2 x2.^2],Y,true); + +Model.Pmp0 = beta(1); +Model.k = beta(2:7); + + From e7de52b3d998a345249dd8b2cd5fbddfbe55fe7c Mon Sep 17 00:00:00 2001 From: cwhanse Date: Wed, 22 Jun 2016 15:30:00 -0600 Subject: [PATCH 3/6] Correction to pvl_snlinverter change ACPower(ACPowerPac0) = Pac0; % Inverter clipping at maximum rated AC Power -ACPower(ACPower Date: Fri, 24 Jun 2016 12:04:45 -0600 Subject: [PATCH 4/6] Clear sky detection algorithm and example data Initial upload --- Example Data/PSEL_GHI_2012.mat | Bin 0 -> 2007266 bytes html/pvl_detect_clear_times_help.m | 84 ++++++++++++ pvl_detect_clear_times.m | 208 +++++++++++++++++++++++++++++ 3 files changed, 292 insertions(+) create mode 100644 Example Data/PSEL_GHI_2012.mat create mode 100644 html/pvl_detect_clear_times_help.m create mode 100644 pvl_detect_clear_times.m 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100644 index 0000000..9cf41fb --- /dev/null +++ b/html/pvl_detect_clear_times_help.m @@ -0,0 +1,84 @@ +%% pvl_detect_clear_times +% Identify times with GHI consistent with clear sky conditions. +% +%% Syntax +% |[clearSamples, csGHI, alpha] = pvl_detect_clear_times(GHI, Time, +% UTCoffset, Location, win_length, sample_interval)| +% +%% Description +% Detects clear sky times by comparing statistics for a regular GHI +% time series to the Ineichen clear sky model. Statistics are calculated +% using a sliding time window (e.g., 10 minutes). An iterative algorithm +% identifies clear periods, uses the identified periods to estimate bias +% in the clear sky model and/or GHI data, adjusts the clear sky model and +% repeats. Code handles GHI data with some irregularities, i.e., missing +% values or unequal data spacing. Code execution can be made +% significantly faster if equally spaced and complete data can be +% assumed. +% Clear times are identified by meeting 5 criteria, thresholds +% for which are hardcoded in this version. Values for these thresholds +% are appropriate for 10 minute windows of 1 minute GHI data. +% +% +%% Inputs: +% +% * *|GHI|* - a Nx1 vector of GHI values (W/m^2) +% * *|Time|* - a Nx1 vector of datenum with equal time spacing +% * *|UTCoffset|* - scalar UTC offset (e.g. EST = -5) +% * *|Location|* - a struct with the following elements, note that all +% elements must be scalars in this application. +% * *|Location.latitude|* - scalar latitude in decimal degrees (positive is +% northern hemisphere) +% * *|Location.longitude|* - scalar longitude in decimal degrees (positive is +% east of prime meridian) +% * *|Location.altitude|* - scalar height above sea level in meters. +% While altitude is optional in many uses, it is required in this +% model implementation. +% * *|win_length|* - length of sliding time window in minutes, integer +% * *|sample_interval|* - nominal minutes between each GHI sample time, integer +% +%% Output: +% * * |clearSamples|* - column vector of logical values with True indicating +% a clear sample in the input GHI time series +% * * |csGHI|* - column vector with scaled clear sky GHI derived from the +% Ineichen model +% * * |alpha|* - scaling factor applied to Ineichen model to obtain output csGHI +% +%% References +% [1] Reno, M.J. and C.W. Hansen, "Identification of periods of clear sky +% irradiance in time series of GHI measurements" Renewable Energy, v90, +% p. 520-531, 2016. +% +%% Notes: +% Initial implementation by Matthew Reno. Modifications for computational +% efficiency by Joshua Patrick and Curtis Martin. +% +%% Example + +% load data for Albuquerque, Time in UTC and GHI in W/m2 +load 'PSEL_GHI_2012.mat'; +dv = datevec(Time); +u = dv(:,2) == 4; % Select April 2012 +Time = Time(u); +GHI = GHI(u); + +% set up Location +Location.latitude = 35.04; +Location.longitude = -106.62; +Location.altitude = 1619; +UTCoffset = 0; % because example data are in UTC time + +win_length = 10; % consider 10 minute intervals +sample_interval = 1; % GHI data is nominally at 1 minute intervals + +[clearSamples, csGHI, alpha] = pvl_detect_clear_times(GHI, Time, UTCoffset, Location, win_length, sample_interval); + +figure +hold all +plot(Time, GHI, 'b.') +plot(Time(clearSamples), GHI(clearSamples), 'r.') +xlabel('Time (UTC)') +ylabel('GHI (W/m^2)') +datetick('x','mm/dd','KeepTicks') +legend('GHI data','Clear times') + diff --git a/pvl_detect_clear_times.m b/pvl_detect_clear_times.m new file mode 100644 index 0000000..2a03083 --- /dev/null +++ b/pvl_detect_clear_times.m @@ -0,0 +1,208 @@ +function [clearSamples, csGHI, alpha] = pvl_detect_clear_times(GHI, Time, UTCoffset, Location, win_length, sample_interval) +% PVL_DETECT_CLEAR_TIMES identifies times with irradiance consistent with +% clear sky conditions in a time series of GHI. +% +% Syntax +% [clearSamples, csGHI, alpha] = pvl_detect_clear_times(GHI, Time, UTCoffset, Location, win_length, sample_interval) +% +% Description +% Detects clear sky times by comparing statistics for a regular GHI +% time series to the Ineichen clear sky model. Statistics are calculated +% using a sliding time window (e.g., 10 minutes). An iterative algorithm +% identifies clear periods, uses the identified periods to estimate bias +% in the clear sky model and/or GHI data, adjusts the clear sky model and +% repeats. Code handles GHI data with some irregularities, i.e., missing +% values or unequal data spacing. Code execution can be made +% significantly faster if equally spaced and complete data can be +% assumed. +% Clear times are identified by meeting 5 criteria, thresholds +% for which are hardcoded in this version. Values for these thresholds +% are appropriate for 10 minute windows of 1 minute GHI data. +% +% Inputs: +% GHI - a Nx1 vector of GHI values (W/m2) +% Time - a Nx1 vector of datenum with equal time spacing +% UTCoffset - scalar UTC offset (e.g. EST = -5) +% Location - a struct with the following elements, note that all +% elements must be scalars in this application. +% Location.latitude - scalar latitude in decimal degrees (positive is +% northern hemisphere) +% Location.longitude - scalar longitude in decimal degrees (positive is +% east of prime meridian) +% Location.altitude - scalar height above sea level in meters. +% While altitude is optional in many uses, it is required in this +% model implementation. +% win_length - length of sliding time window in minutes, integer +% sample_interval - nominal minutes between each GHI sample time, integer +% +% Output: +% clearSamples - column vector of logical values with True indicating +% a clear sample in the input GHI time series +% csGHI - column vector with scaled clear sky GHI derived from the +% Ineichen model +% alpha - scaling factor applied to Ineichen model to obtain output csGHI +% +% References +% [1] Reno, M.J. and C.W. Hansen, "Identification of periods of clear sky +% irradiance in time series of GHI measurements" Renewable Energy, v90, +% p. 520-531, 2016. +% +% Notes: +% Initial implementation by Matthew Reno. Modifications for computational +% efficiency by Joshua Patrick and Curtis Martin. +% + + +% Threshold values for each criterion. Values are appropriate for 10 +% minute windows with 1 minute intervals between GHI data. + +% MeanDiff: threshold value in W/m2 for first criterion, i.e., agreement between mean +% values of GHI in each interval, see Eq. 6 in [1] +MeanDiff = 75; + +% MaxDiff: threshold value in W/m2 for second criterion, i.e., agreement +% between maxima of GHI values in each interval, see Eq. 7 in [1] +MaxDiff = 75; + +% LineLengthLower and LineLengthUpper: threshold values (unit is weird) for +% third criterion, i.e., agreement between line lengths of GHI data and +% clear sky GHI, see Eq. 8 in [1]. Criterion is satisfied when line length +% LL meets LineLengthLower <= LL <= LineLengthUpper +LineLengthLower = -5; +LineLengthUpper = 10; + +% VarDiff: threshold value in 1/seconds for the fourth criterion, i.e., +% agreement between normalized standard deviations of rate of change in +% irradiance, see Eq. 9 through Eq. 11 in [1] +VarDiff = 0.005; + +% SlopeDev: threshold value in W/m2 for the fifth criterion, i.e., +% agreement between largest magnitude of change in successive GHI values, +% see Eq. 12 through 14 in [1] +SlopeDev = 8; + +dv = datevec(Time); +dates = datenum(dv(:,1:3)); +days = unique(dates); +ivec = (1:length(Time))'; + +% Get standard Ineichen clear sky GHI +csGHI0 = pvl_clearsky_ineichen(pvl_maketimestruct(Time,UTCoffset),Location); +% replace NaNs with 0 +csNaNs = isnan(csGHI0); +csGHI0(csNaNs) = 0; + +% Create index matrix for windows + +max_samples_per_window = 2 * win_length / sample_interval; % upper bound on number of data samples in an interval +min_samples_per_window = floor(0.8 * win_length / sample_interval) ; % lower bound on number of data samples in an interval + +% initialize some arrays +clearMean = NaN(size(GHI)); +clearMax = clearMean; +clearMaxSlope = clearMean; +timeDiff = zeros(max_samples_per_window, length(clearMean)); +clearGHIDiff = timeDiff; + +measuredMean = clearMean; +measuredMax = clearMean; +measuredMaxSlope = clearMean; +measuredSqSlope = clearMean; +measuredStdSlope = clearMean; +measuredLineLength = clearMean; + + +k = 0; % counts number of intervals meeting data size criteria, i.e., samples between min_samples_per_window and max_samples_per_window +for j = 1:length(Time) + tu = Time>=Time(j) & Time<(Time(j)+ (win_length + 1)/1440); + if sum(tu) >= min_samples_per_window && sum(tu) <= max_samples_per_window % at least 8 measurements within a window but not more than 20 + k = k + 1; + tidx = ivec(tu); + w = diff(Time(tidx)*1440); % time intervals in minutes between samples + + % Calculate parameters (all but line length) for alpha=1 case + wx = [w; 0]/sum(w); % leave last point out of mean + clearMean(j) = sum(wx.*csGHI0(tidx)); + clearMax(j) = max(csGHI0(tidx)); + clearMaxSlope(j) = max(abs(diff(csGHI0(tidx),1,1)./w)); + tmp_diffGHI = diff(csGHI0(tidx)); + clearGHIDiff(1:length(tmp_diffGHI),j) = tmp_diffGHI; + timeDiff(1:length(tmp_diffGHI),j) = w; + + % Calculate parameters for measured GHI + measuredMean(j) = sum(wx.*GHI(tidx)); + measuredMax(j) = max(GHI(tidx)); + tmpSlope = diff(GHI(tidx),1,1)./w; + measuredMaxSlope(j) = max(abs(tmpSlope)); + measuredSqSlope(j) = sum(tmpSlope.^2); + measuredStdSlope(j) = std(tmpSlope); + measuredLineLength(j) = sum(sqrt(diff(GHI(tidx),1,1).^2 + w.^2)); + else + % too much missing data or too many data samples + disp(['Data problem between ' datestr(min(Time(tu))) ' and ' datestr(max(Time(tu))) ... + ' have ' num2str(sum(tu)) ' values']); + end +end + +% Normalized variance of measured GHI < 0.005 criterion doesn't depend on +% clear sky GHI +c4 = measuredStdSlope ./ measuredMean < VarDiff; + +% initialize state for iterative process +alpha = 1; %start off with Ineichen clear sky model + +% Start Iterative process of finding clear days, fitting clear sky model, finding clear days, .... +% Continue iterations until not finding any more clear days or 20 iterations +for ii=1:20 + + % Update clear sky model by scaling Ineichen + csGHI = alpha * csGHI0; + + % Adjust Clear Sky Model parameters for scaling by alpha + clearLineLength = sum(sqrt((alpha*clearGHIDiff).^2 + timeDiff.^2 )); + clearLineLength = clearLineLength(:); + + % Evaluate comparison criteria + c1 = abs(measuredMean - alpha * clearMean) < MeanDiff; + c2 = abs(measuredMax - alpha * clearMax) < MaxDiff; + c3 = (LineLengthLower < measuredLineLength - clearLineLength) & (measuredLineLength - clearLineLength < LineLengthUpper) ; + c5 = (measuredMaxSlope - alpha * clearMaxSlope) < SlopeDev; + c6 = clearMean ~= 0 & ~isnan(clearMean); % window includes some daylight + + % Identify windows meeting all five (six) criteria + clearWindows = c1 & c2 & c3 & c4 & c5 & c6; + + % Daily clearness is proportion of samples that are clear + dailyClearness = histcounts(Time(clearWindows),[days]) ./ ... + histcounts(Time(csGHI>25),[days]); + + % Save current state + previousAlpha = alpha; + + % Get new alpha by adjusting clear sky model to match clear times + % Need to minimize RMSE between GHI and alpha * csGHI0 + + clearSamples = false(size(GHI)); + + for j = 1:length(Time) + if clearWindows(j) + tu = Time>=Time(j) & Time<(Time(j)+win_length/1440); + clearSamples(tu) = true; + end + end + + RMSE = @(x)sqrt(mean((GHI(clearSamples) - x*csGHI0(clearSamples)).^2)); + alpha = fminsearch(RMSE, alpha); % update scaling factor on clear sky model + + if round(alpha*10000)==round(previousAlpha*10000) + previousAlpha = alpha; %#ok + break + end + +end % for loop + +csGHI = alpha * csGHI0; + +end % function + + From 6af3b34a07b1feaad483f468b4b171f9c1efabb0 Mon Sep 17 00:00:00 2001 From: cwhanse Date: Mon, 27 Jun 2016 13:28:12 -0600 Subject: [PATCH 5/6] Add code for equally spaced data Execution is much faster with GHI data at equally spaced time stamps --- html/pvl_detect_clear_times_help.m | 33 +++++- pvl_detect_clear_times.m | 164 +++++++++++++++++++---------- 2 files changed, 140 insertions(+), 57 deletions(-) diff --git a/html/pvl_detect_clear_times_help.m b/html/pvl_detect_clear_times_help.m index 9cf41fb..b36c50b 100644 --- a/html/pvl_detect_clear_times_help.m +++ b/html/pvl_detect_clear_times_help.m @@ -69,8 +69,10 @@ UTCoffset = 0; % because example data are in UTC time win_length = 10; % consider 10 minute intervals -sample_interval = 1; % GHI data is nominally at 1 minute intervals - +% GHI data is nominally at 1 minute intervals, but in our example there are +% missing samples and data aren't exactly at 1 minute intervals +sample_interval = 1; +% Demonstrate algorithm for unequally spaced data [clearSamples, csGHI, alpha] = pvl_detect_clear_times(GHI, Time, UTCoffset, Location, win_length, sample_interval); figure @@ -82,3 +84,30 @@ datetick('x','mm/dd','KeepTicks') legend('GHI data','Clear times') +%% make data equally spaced by rounding, fill in missing data by linear +% interpolation + +% create full time vector sTime +sivec = 0:round(max(Time)*1440) - round(Time(1)*1440); +sTime = (round(Time(1)*1440) + sivec)/1440; +sTime = sTime(:); + +sGHI = NaN(size(sTime)); +sGHI(round(Time*1440) - round(Time(1)*1440) + 1) = GHI; +% interpolate GHI to fill in NaNs in daylight GHI. Leave NaNs in for nighttime data +[~, SunEl, ~, ~] = pvl_ephemeris(pvl_maketimestruct(sTime,UTCoffset), Location); +daylight = SunEl > 1; +u = isnan(sGHI) & daylight; +sGHI(u) = interp1(sTime(daylight & ~u), sGHI(daylight & ~u), sTime(u)); + +% Demonstrate algorithm for equally spaced data +[clearSamples, csGHI, alpha] = pvl_detect_clear_times(sGHI, sTime, UTCoffset, Location, win_length, sample_interval); + +figure +hold all +plot(sTime, sGHI, 'b.') +plot(sTime(clearSamples), sGHI(clearSamples), 'r.') +xlabel('Time (UTC)') +ylabel('GHI (W/m^2)') +datetick('x','mm/dd','KeepTicks') +legend('GHI data','Clear times') diff --git a/pvl_detect_clear_times.m b/pvl_detect_clear_times.m index 2a03083..e963bdf 100644 --- a/pvl_detect_clear_times.m +++ b/pvl_detect_clear_times.m @@ -12,9 +12,12 @@ % identifies clear periods, uses the identified periods to estimate bias % in the clear sky model and/or GHI data, adjusts the clear sky model and % repeats. Code handles GHI data with some irregularities, i.e., missing -% values or unequal data spacing. Code execution can be made -% significantly faster if equally spaced and complete data can be -% assumed. +% values or unequal data spacing, but execution is significantly faster +% if data are equally spaced and without missing values. +% +% If non-daylight times are included the corresponding GHI values should +% be NaNs. +% % Clear times are identified by meeting 5 criteria, thresholds % for which are hardcoded in this version. Values for these thresholds % are appropriate for 10 minute windows of 1 minute GHI data. @@ -86,6 +89,21 @@ days = unique(dates); ivec = (1:length(Time))'; +% check for equally spaced data, code execution is much faster with equal +% time spacing + +sTime = diff(round(Time*1440)); % minutes between samples +if length(unique(sTime)) == 1 && mod(win_length, sample_interval)==0 + % equally spaced data, none missing + equal_spacing = true; +else + equal_spacing = false; +end + +% get sun angles for determining sunrise/sunset +[~, SunEl, ~, ~] = pvl_ephemeris(pvl_maketimestruct(Time,UTCoffset), Location); +daylight = SunEl > 1; + % Get standard Ineichen clear sky GHI csGHI0 = pvl_clearsky_ineichen(pvl_maketimestruct(Time,UTCoffset),Location); % replace NaNs with 0 @@ -97,56 +115,81 @@ max_samples_per_window = 2 * win_length / sample_interval; % upper bound on number of data samples in an interval min_samples_per_window = floor(0.8 * win_length / sample_interval) ; % lower bound on number of data samples in an interval -% initialize some arrays -clearMean = NaN(size(GHI)); -clearMax = clearMean; -clearMaxSlope = clearMean; -timeDiff = zeros(max_samples_per_window, length(clearMean)); -clearGHIDiff = timeDiff; - -measuredMean = clearMean; -measuredMax = clearMean; -measuredMaxSlope = clearMean; -measuredSqSlope = clearMean; -measuredStdSlope = clearMean; -measuredLineLength = clearMean; - - -k = 0; % counts number of intervals meeting data size criteria, i.e., samples between min_samples_per_window and max_samples_per_window -for j = 1:length(Time) - tu = Time>=Time(j) & Time<(Time(j)+ (win_length + 1)/1440); - if sum(tu) >= min_samples_per_window && sum(tu) <= max_samples_per_window % at least 8 measurements within a window but not more than 20 - k = k + 1; +if not(equal_spacing) + % pre-allocate some arrays + clearMean = NaN(size(GHI)); + clearMax = clearMean; + clearMaxSlope = clearMean; + timeDiff = zeros(max_samples_per_window, length(clearMean)); + clearGHIDiff = timeDiff; + + measuredMean = clearMean; + measuredMax = clearMean; + measuredMaxSlope = clearMean; + measuredSqSlope = clearMean; + measuredStdSlope = clearMean; + measuredLineLength = clearMean; + + k = 0; % counts number of intervals meeting data size criteria, i.e., samples between min_samples_per_window and max_samples_per_window + for j = 1:length(Time) + tu = Time>=Time(j) & Time<(Time(j)+ (win_length + 1)/1440); tidx = ivec(tu); - w = diff(Time(tidx)*1440); % time intervals in minutes between samples - - % Calculate parameters (all but line length) for alpha=1 case - wx = [w; 0]/sum(w); % leave last point out of mean - clearMean(j) = sum(wx.*csGHI0(tidx)); - clearMax(j) = max(csGHI0(tidx)); - clearMaxSlope(j) = max(abs(diff(csGHI0(tidx),1,1)./w)); - tmp_diffGHI = diff(csGHI0(tidx)); - clearGHIDiff(1:length(tmp_diffGHI),j) = tmp_diffGHI; - timeDiff(1:length(tmp_diffGHI),j) = w; - - % Calculate parameters for measured GHI - measuredMean(j) = sum(wx.*GHI(tidx)); - measuredMax(j) = max(GHI(tidx)); - tmpSlope = diff(GHI(tidx),1,1)./w; - measuredMaxSlope(j) = max(abs(tmpSlope)); - measuredSqSlope(j) = sum(tmpSlope.^2); - measuredStdSlope(j) = std(tmpSlope); - measuredLineLength(j) = sum(sqrt(diff(GHI(tidx),1,1).^2 + w.^2)); - else - % too much missing data or too many data samples - disp(['Data problem between ' datestr(min(Time(tu))) ' and ' datestr(max(Time(tu))) ... - ' have ' num2str(sum(tu)) ' values']); + tmp_GHI = GHI(tidx); + if sum(daylight(tu)) < min_samples_per_window + % sun is not above horizon, skip interval + elseif sum(~isnan(tmp_GHI)) >= min_samples_per_window && ... + sum(~isnan(tmp_GHI)) <= max_samples_per_window % e.g., at least 8 measurements within a window but not more than 20 + k = k + 1; + tmp_csGHI0 = csGHI0(tidx); + w = diff(Time(tidx)*1440); % time intervals in minutes between samples + + % Calculate parameters (all but line length) for alpha=1 case + wx = [w; 0]/sum(w); % leave last point out of mean + clearMean(j) = sum(wx.*tmp_csGHI0); + clearMax(j) = max(tmp_csGHI0); + clearMaxSlope(j) = max(abs(diff(tmp_csGHI0,1,1)./w)); + tmp_diffGHI = diff(tmp_csGHI0); + clearGHIDiff(1:length(tmp_diffGHI),j) = tmp_diffGHI; + timeDiff(1:length(tmp_diffGHI),j) = w; + + % Calculate parameters for measured GHI + measuredMean(j) = sum(wx.*tmp_GHI); + measuredMax(j) = max(tmp_GHI); + tmpSlope = diff(tmp_GHI,1,1)./w; + measuredMaxSlope(j) = max(abs(tmpSlope)); + measuredSqSlope(j) = sum(tmpSlope.^2); + measuredStdSlope(j) = std(tmpSlope); + measuredLineLength(j) = sum(sqrt(diff(tmp_GHI,1,1).^2 + w.^2)); + else + % too much missing data or too many data samples + disp(['Data problem between ' datestr(min(Time(tu))) ' and ' datestr(max(Time(tu))) ... + ' have ' num2str(sum(tu)) ' values']); + end end + +else % equal data spacing + + samples_per_window = win_length / sample_interval; + H = hankel(1:samples_per_window,samples_per_window:length(Time)); + clearMean = mean(csGHI0(H)); + clearMax = max(csGHI0(H)); + clearSlope = diff(csGHI0(H),1,1); + + % Calculate parameters for measured GHI + measuredMean = mean(GHI(H)); + measuredMax = max(GHI(H)); + measuredSlope = diff(GHI(H),1,1); + measuredLineLength = sum(sqrt(measuredSlope.^2 + (sample_interval*ones(size(measuredSlope))).^2 )); + end -% Normalized variance of measured GHI < 0.005 criterion doesn't depend on -% clear sky GHI -c4 = measuredStdSlope ./ measuredMean < VarDiff; +% Normalized variance of measured GHI < Vardeiff criterion doesn't depend on +% clear sky GHI, compute outside of iteration on CS model adjustment alpha +if not(equal_spacing) + c4 = measuredStdSlope ./ measuredMean < VarDiff; +else + c4 = std(measuredSlope) ./ measuredMean < VarDiff; +end % initialize state for iterative process alpha = 1; %start off with Ineichen clear sky model @@ -159,8 +202,15 @@ csGHI = alpha * csGHI0; % Adjust Clear Sky Model parameters for scaling by alpha - clearLineLength = sum(sqrt((alpha*clearGHIDiff).^2 + timeDiff.^2 )); - clearLineLength = clearLineLength(:); + if not(equal_spacing) + clearLineLength = sum(sqrt((alpha*clearGHIDiff).^2 + timeDiff.^2 )); + clearLineLength = clearLineLength(:); + else + clearLineLength = sum(sqrt((alpha*clearSlope).^2 + (sample_interval*ones(size(measuredSlope))).^2 )); + measuredMaxSlope = max(abs(measuredSlope)); + clearMaxSlope = max(abs(clearSlope)); + end + % Evaluate comparison criteria c1 = abs(measuredMean - alpha * clearMean) < MeanDiff; @@ -184,11 +234,15 @@ clearSamples = false(size(GHI)); - for j = 1:length(Time) - if clearWindows(j) - tu = Time>=Time(j) & Time<(Time(j)+win_length/1440); - clearSamples(tu) = true; + if not(equal_spacing) + for j = 1:length(Time) + if clearWindows(j) + tu = Time>=Time(j) & Time<(Time(j)+win_length/1440); + clearSamples(tu) = true; + end end + else + clearSamples = ismember(ivec,H(:,clearWindows)); end RMSE = @(x)sqrt(mean((GHI(clearSamples) - x*csGHI0(clearSamples)).^2)); From d6d0cfffd6ecc3995c385e956f8b5207504ffd1c Mon Sep 17 00:00:00 2001 From: cwhanse Date: Thu, 7 Jul 2016 13:53:07 -0600 Subject: [PATCH 6/6] Add pvl_detect_shadows; update help library pvl_detect_shadows identifies shadows from nearby objects such as wires and poles in a time series of GHI data. --- Example Data/SMUD22_GHI_data.mat | Bin 0 -> 2326364 bytes GHIImage.m | 387 +++++++++++ html/PVL_Publish_Help.m | 12 +- html/html/PVL_TestScript1.html | 43 +- html/html/PVL_TestScript1.png | Bin 2987 -> 3024 bytes html/html/PVL_TestScript1_01.png | Bin 22837 -> 22838 bytes html/html/PVL_TestScript1_02.png | Bin 20913 -> 20904 bytes html/html/PVL_TestScript1_03.png | Bin 14895 -> 14890 bytes html/html/PVL_TestScript1_04.png | Bin 24125 -> 24036 bytes html/html/PVL_TestScript1_05.png | Bin 31220 -> 30273 bytes html/html/PVL_TestScript1_06.png | Bin 19707 -> 19752 bytes ...PVL_TestScript1_eq01540178742677260109.png | Bin 0 -> 2325 bytes html/html/PVL_TestScript2.html | 29 +- html/html/PVL_TestScript2.png | Bin 3500 -> 3500 bytes html/html/PVL_TestScript2_01.png 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0 -> 107744 bytes html/html/pvl_detect_clear_times_help_02.png | Bin 0 -> 99342 bytes html/html/pvl_detect_shadows_help.html | 188 ++++++ html/html/pvl_detect_shadows_help.png | Bin 0 -> 3736 bytes html/html/pvl_detect_shadows_help_01.png | Bin 0 -> 94358 bytes html/html/pvl_detect_shadows_help_02.png | Bin 0 -> 13619 bytes html/html/pvl_dirint_help.html | 2 +- html/html/pvl_dirint_help.png | Bin 3491 -> 3489 bytes html/html/pvl_dirint_help_01.png | Bin 24883 -> 24901 bytes html/html/pvl_disc_help.html | 2 +- html/html/pvl_disc_help.png | Bin 3610 -> 3614 bytes html/html/pvl_disc_help_01.png | Bin 24991 -> 24994 bytes html/html/pvl_doy2date_help.html | 2 +- html/html/pvl_ephemeris_help.html | 12 +- html/html/pvl_ephemeris_help.png | Bin 2440 -> 2444 bytes html/html/pvl_ephemeris_help_01.png | Bin 18432 -> 18446 bytes html/html/pvl_erbs_help.html | 2 +- html/html/pvl_erbs_help.png | Bin 3453 -> 3451 bytes html/html/pvl_erbs_help_01.png | Bin 25971 -> 25974 bytes html/html/pvl_exceltime2matlab_help.html | 2 +- html/html/pvl_extraradiation_help.html | 2 +- html/html/pvl_getISDdata_help.html | 2 +- html/html/pvl_getaoi_help.html | 2 +- html/html/pvl_grounddiffuse_help.html | 2 +- html/html/pvl_haydavies1980_help.html | 2 +- html/html/pvl_isotropicsky_help.html | 2 +- html/html/pvl_kingdiffuse_help.html | 2 +- html/html/pvl_klucher1979_help.html | 2 +- html/html/pvl_lambertw_help.html | 2 +- html/html/pvl_leapyear_help.html | 2 +- html/html/pvl_louche_help.html | 2 +- html/html/pvl_louche_help.png | Bin 3617 -> 3617 bytes html/html/pvl_louche_help_01.png | Bin 25572 -> 25573 bytes html/html/pvl_makelocationstruct_help.html | 2 +- html/html/pvl_maketimestruct_help.html | 2 +- html/html/pvl_matlabtime2excel_help.html | 2 +- html/html/pvl_orgill_hollands_help.html | 2 +- html/html/pvl_orgill_hollands_help.png | Bin 3455 -> 3455 bytes html/html/pvl_orgill_hollands_help_01.png | Bin 25711 -> 25716 bytes html/html/pvl_perez_help.html | 2 +- html/html/pvl_perez_help.png | Bin 4341 -> 4344 bytes html/html/pvl_perez_help_01.png | Bin 38642 -> 38617 bytes html/html/pvl_readISH_help.html | 2 +- html/html/pvl_readtmy2_help.html | 2 +- html/html/pvl_readtmy3_help.html | 2 +- html/html/pvl_reindl1990_help.html | 2 +- html/html/pvl_reindl_1_help.html | 2 +- html/html/pvl_reindl_1_help.png | Bin 3454 -> 3455 bytes html/html/pvl_reindl_1_help_01.png | Bin 25823 -> 25826 bytes html/html/pvl_reindl_2_help.html | 2 +- html/html/pvl_reindl_2_help.png | Bin 3454 -> 3455 bytes html/html/pvl_reindl_2_help_01.png | Bin 25823 -> 25826 bytes html/html/pvl_relativeairmass_help.html | 2 +- html/html/pvl_relativeairmass_help.png | Bin 3676 -> 3679 bytes html/html/pvl_relativeairmass_help_01.png | Bin 29379 -> 29436 bytes html/html/pvl_robustfit_help.html | 132 ++++ html/html/pvl_robustfit_help.png | Bin 0 -> 2548 bytes html/html/pvl_robustfit_help_01.png | Bin 0 -> 13474 bytes html/html/pvl_sapmcelltemp_help.html | 4 +- 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html/html/pvl_spa_help_01.png | Bin 20211 -> 20251 bytes html/html/pvl_spa_help_02.png | Bin 17837 -> 17830 bytes html/html/pvlib_features.html | 2 +- html/html/pvlib_functions_by_cat.html | 19 +- html/html/pvlib_getting_started.html | 2 +- html/html/pvlib_product_page.html | 2 +- html/html/pvlib_release_notes.html | 22 +- html/html/pvlib_system_requirements.html | 12 +- html/html/pvlib_user_guide.html | 2 +- html/pvl_PVsyst_parameter_estimation_help.m | 1 - html/pvl_clearsky_haurwitz_help.m | 2 +- html/pvl_detect_clear_times.html | 599 ++++++++++++++++++ html/pvl_detect_shadows_help.m | 75 +++ html/pvl_ephemeris_help.m | 6 +- html/pvl_huld_parameter_estimation_help.m | 54 ++ html/pvl_robustfit_help.m | 43 ++ html/pvlib_functions_by_cat.m | 12 +- html/pvlib_release_notes.m | 18 + html/pvlib_system_requirements.m | 8 +- pvl_detect_shadows.m | 338 ++++++++++ pvl_snlinverter.m | 2 +- 143 files changed, 2283 insertions(+), 89 deletions(-) create mode 100644 Example Data/SMUD22_GHI_data.mat create mode 100644 GHIImage.m create mode 100644 html/html/PVL_TestScript1_eq01540178742677260109.png create mode 100644 html/html/pvl_absoluteairmass_help_eq05340242215055113677.png create mode 100644 html/html/pvl_calcparams_desoto_help_eq10346328397062103586.png create mode 100644 html/html/pvl_detect_clear_times_help.html create mode 100644 html/html/pvl_detect_clear_times_help.png create mode 100644 html/html/pvl_detect_clear_times_help_01.png create mode 100644 html/html/pvl_detect_clear_times_help_02.png create mode 100644 html/html/pvl_detect_shadows_help.html create mode 100644 html/html/pvl_detect_shadows_help.png create mode 100644 html/html/pvl_detect_shadows_help_01.png create mode 100644 html/html/pvl_detect_shadows_help_02.png create mode 100644 html/html/pvl_robustfit_help.html create mode 100644 html/html/pvl_robustfit_help.png create mode 100644 html/html/pvl_robustfit_help_01.png create mode 100644 html/html/pvl_sapmcelltemp_help_eq00772285443793674075.png create mode 100644 html/html/pvl_sapmcelltemp_help_eq01000226452344603674.png create mode 100644 html/html/pvl_sapmcelltemp_help_eq02044268985122151499.png create mode 100644 html/html/pvl_sapmcelltemp_help_eq03219983695159028104.png create mode 100644 html/html/pvl_sapmcelltemp_help_eq05508344529756732484.png create mode 100644 html/html/pvl_sapmcelltemp_help_eq11046168025807619678.png create mode 100644 html/html/pvl_sapmcelltemp_help_eq14900514129597942148.png create mode 100644 html/html/pvl_sapmcelltemp_help_eq16003806544088477123.png create mode 100644 html/html/pvl_sapmcelltemp_help_eq17300045553938971479.png create mode 100644 html/html/pvl_singlediode_help_eq13115885848780160647.png create mode 100644 html/pvl_detect_clear_times.html create mode 100644 html/pvl_detect_shadows_help.m create mode 100644 html/pvl_huld_parameter_estimation_help.m create mode 100644 html/pvl_robustfit_help.m create mode 100644 pvl_detect_shadows.m diff --git a/Example Data/SMUD22_GHI_data.mat b/Example 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