WindRose Module Verification
The question is that Does the GetRandomWindِSample method of WindRose object correctly samples the Defined wind Data?. To find the answer of this question, first a WindRose object will be created. Then numerous samples will be created and record. Finally the distribution of recorded samples will be check by defined WindRose data.
The below codes can be downloaded from here in Jupyter Notebook format.
Verification:
#Import the software
import opensrane as opr
#Clear Memory from probable created objects
opr.wipe()
#Define Date time object
DTobj=opr.DateAndTime.DateTime(1)
#Defind WindRose Object
windobj=opr.WindData.WindRose(1)
windobj.WindDayClassList=['F','D','B','E','D','D']
windobj.WindNightClassList=['F','D','B','E','D','D']
windobj.AlphaCOEFlist=[0.6,0.25,0.15,0.4,0.25,0.25]
windobj.DayWindSpeedList=[[1,2],[2,3],[3,5],[5,7],[7,9],[9,9]]
windobj.NightWindSpeedList=[[1,2],[2,3],[3,5],[5,7],[7,9],[9,9]]
#You don't need to define calmn Condition and program will understand it automatically
windobj.DayWindFreqMatrix=[ [0.446,0.372,0.355,0.109,0.017,0],
[0.44,0.938,1.55,0.755,0.097,0.029],
[0.898,1.321,3.06,1.402,0.767,0.892],
[0.875,1.241,2.626,1.51,0.892,0.646],
[0.801,0.927,1.63,0.658,0.355,0.097],
[0.87,1.121,0.984,0.309,0.023,0.029],
[0.778,0.801,0.91,0.315,0.029,0],
[0.652,0.875,1.35,0.498,0.086,0.023],
[0.566,0.887,1.659,0.709,0.149,0],
[0.583,0.807,2.128,2.998,1.041,0.137],
[0.898,1.093,2.408,2.059,1.327,0.154],
[1.985,2.088,2.488,1.098,0.332,0.069],
[4.067,3.123,1.442,0.292,0.063,0.011],
[3.93,5.372,3.85,1.201,0.349,0.057],
[1.71,1.619,2.38,0.767,0.109,0.006],
[0.698,0.469,0.383,0.154,0.011,0],
]
windobj.NightWindFreqMatrix=windobj.DayWindFreqMatrix
#---------------- Verification For Day---------------------------------
N=100000 #Number of Sampling
#Define Variables to store data
windDir=set()
WindDirData=dict()
WindSpeedFreq=[[0.0,0.0,0.0,0.0,0.0,0] for i in range(16)]
#Sampling DayTime
DTobj.SampleisDay()
WindDirData['isCalmn']=0
for i in range(N):
rslt=windobj.GetRandomWindِSample(); #Create a sample
#Get wind Direction
if rslt['isCalmn']==False: windDir.add(rslt['WindDirection'])
if len(windDir)!=len(WindDirData)-1 and rslt['isCalmn']==False:
wdir=list(windDir)
add=[i for i in wdir if i not in WindDirData.keys()][0]
WindDirData[add]=0
if rslt['isCalmn']== True:
WindDirData['isCalmn'] =WindDirData['isCalmn']+1
else:
WindDirData[rslt['WindDirection']] =WindDirData[rslt['WindDirection']]+1
#Calculating Wind Speed Frequency
if rslt['isCalmn']==False:
WindRow=[i for i in range(len(windobj.DayTheta)) if windobj.DayTheta[i]==rslt['WindDirection']][0]
WindCol=[i for i in range(len(windobj.DayWindSpeedList)) if (windobj.DayWindSpeedList[i][0]<=rslt['WindSpeed']<=windobj.DayWindSpeedList[i][1])][0]
WindSpeedFreq[WindRow][WindCol]=WindSpeedFreq[WindRow][WindCol]+1
#Normalize Wind Matrix
for i in range(len(WindSpeedFreq)):
for j in range(len(WindSpeedFreq[0])):
WindSpeedFreq[i][j]=WindSpeedFreq[i][j]/N*100
#print('Monte Carlo Sampling Results: \n', WindSpeedFreq)
#print('\n Defined Values: \n',windobj.DayWindFreqMatrix)
#print()
#Normalizing Wind Direction Data
for key in WindDirData.keys():
WindDirData[key]=WindDirData[key]/N*100
print('\nComparision of Direction Sampling Ratio and User Defined Values:\n')
userdata=dict(zip(windobj.DayTheta,windobj.DayDirectProbability));
for keys in userdata.keys():
print(f'for direction {keys} average of \t sampled values: {round(WindDirData[keys],3)} \t and UserDefined value: {round(userdata[keys],3)}')
print(f"and Calm condition is= {WindDirData['isCalmn']}")
The result of above code has been printed in the following:
Comparision of Direction Sampling Ratio and User Defined Values:
for direction 0.0 average of sampled values: 1.289 and UserDefined value: 1.299
for direction 22.5 average of sampled values: 3.855 and UserDefined value: 3.809
for direction 45.0 average of sampled values: 8.296 and UserDefined value: 8.34
for direction 67.5 average of sampled values: 7.768 and UserDefined value: 7.79
for direction 90.0 average of sampled values: 4.424 and UserDefined value: 4.468
for direction 112.5 average of sampled values: 3.293 and UserDefined value: 3.336
for direction 135.0 average of sampled values: 2.827 and UserDefined value: 2.833
for direction 157.5 average of sampled values: 3.516 and UserDefined value: 3.484
for direction 180.0 average of sampled values: 4.033 and UserDefined value: 3.97
for direction 202.5 average of sampled values: 7.831 and UserDefined value: 7.694
for direction 225.0 average of sampled values: 7.922 and UserDefined value: 7.939
for direction 247.5 average of sampled values: 7.913 and UserDefined value: 8.06
for direction 270.0 average of sampled values: 9.003 and UserDefined value: 8.998
for direction 292.5 average of sampled values: 14.692 and UserDefined value: 14.759
for direction 315.0 average of sampled values: 6.638 and UserDefined value: 6.591
for direction 337.5 average of sampled values: 1.749 and UserDefined value: 1.715
and Calm condition is= 4.951
As it can bee seen, the samples values and defined values for each direction have properly close to each other.
Verification by: Bijan Sayyafzadeh