A Model for Hourly Solar Radiation Data Generation from Daily Solar Radiation Data Using a Generalized Regression Artificial Neural Network

Pseudocode 1

% Start

clc

clear

close all

%  %— — — — — — —Input Data— — — — — —

fileName=Site.xlsx;

sheetName=hourly Solar radiation data;

L=; % add a latitude value

LOD=; % add a longitude value

% T means testing data

SRi=xlsread(fileName, sheetName, C3:C4358); % hourly solar radiation

Wi=xlsread(fileName, sheetName, D3:D4358); % hour angle

Wsi=xlsread(fileName, sheetName, E3:E4358); % sun set angle

GD=xlsread(fileName, sheetName, G3:G4358); % daily solar radiation

DN=xlsread(fileName, sheetName, F3:F4358); % day number (day & month)

h=xlsread(fileName, sheetName, I3:I4358); % hour

SRi_T=xlsread(fileName, sheetName, C4359:C8546);

Wi_T=xlsread(fileName, sheetName, D4359:D8546);

Wsi_T=xlsread(fileName, sheetName, E4359:E8546);

GD_T=xlsread(fileName, sheetName, G4359:G8546);

DN_T=xlsread(fileName, sheetName, F4359:F8546);

h_T=xlsread(fileName, sheetName, I4359:I8546);

SR=SRi1000;

SR_T=SRi_T1000;

W=(Wi_T(pi/180));

Ws=(abs(Wsi_T(pi/180)));

%=============Proposed GRNN Model=================

inputs=[L LOD Wi, Wsi, GD DN h];

I=inputs;

targets=SR;

T=targets;

net=newgrnn(I,T);

test=[L LOD Wi_T, Wsi_T, GD_T DN_T h_T];

Test=test;

x_predicted=sim(net,Test);

X_P=x_predicted;

%============Liu and Jordan Model==================

R_LiuJordan=((((pi)/24)(cos(W)-cos(Ws)))./(sin(Ws)-((piWs)/180).cos(Ws)));

SR_LiuJordan=R_LiuJordan.GD_T

%============Collares-Pereira and Rabel Model======

a=0.409+(0.5016sin(Ws-60));

b=0.6609+(0.4767sin(Ws-60));

R_Collares=((a + (b.cos(W))).((((pi/24))(cos(W)-cos(Ws))))./(sin(Ws)-((piWs)/180).cos(Ws)));

SR_Collares=R_Collares.GD_T;

%=================Plot data===========================

plot (SR_T)

xlim([0 100])

hold on

plot (SR_LiuJordan, k)

xlim([0 100])

hold on

plot (SR_Collares, g)

xlim([0 35])

hold on

plot (X_P, red)

xlim([0 100])

%— — — —Models evaluation— — — — — — — —

n=length(X_P);                                    

E3_Hour=SR_T-X_P;

GRNN_MAPE=abs(E3_Hour./SR_T);

GRNN_meanMAPE1=sum(GRNN_MAPE)/n;

GRNN_meanMAPE=GRNN_meanMAPE1100;

GRNN_RMSE=sqrt(sum((X_P-SR_T).2/n))

GRNN_MBE=sum(X_P-SR_T)/n

SUM=(sum(X_P)./n);

liner_RMSE_Percentage=(GRNN_RMSE/SUM)100

liner_MBE_Percentage=(GRNN_MBE/SUM)100

% End