Wind speed modeling based on measurement data to predict future wind speed with modified Rayleigh model

International Journal of Power Electronics and Drive Systems (IJPEDS)
Vol. 12, No. 3, September 2021, pp. 1823~1831
ISSN: 2088-8694, DOI: 10.11591/ijpeds.v12.i3.pp1823-1831  1823
Journal homepage: http://ijpeds.iaescore.com
Wind speed modeling based on measurement data to predict
future wind speed with modified Rayleigh model
Suwarno, Rohana
Department of Electrical Engineering, Universitas Muhammadiyah Sumatera Utara, Medan, Indonesia
Article Info ABSTRACT
Article history:
Received Apr 1, 2021
Revised Jun 27, 2021
Accepted Jul 12, 2021
The development of modeling wind speed plays a very important in helping
to obtain the actual wind speed data for the benefit of the power plant
planning in the future. The wind speed in this paper is obtained from a PCE-
FWS 20 type measuring instrument with a duration of 30 minutes which is
accumulated into monthly data for one year (2019). Despite the many wind
speed modeling that has been done by researchers. Modeling wind speeds
proposed in this study were obtained from the modified Rayleigh
distribution. In this study, the Rayleigh scale factor (Cr) and modified
Rayleigh scale factor (Cm) were calculated. The observed wind speed is
compared with the predicted wind characteristics. The data fit test used
correlation coefficient (R2
), root means square error (RMSE), and mean
absolute percentage error (MAPE). The results of the proposed modified
Rayleigh model provide very good results for users.
Keywords:
Measurement data
Test fit-of data
Wind speed
Wind speed modeling
This is an open access article under the CC BY-SA license.
Corresponding Author:
Suwarno
Department of Electrical Engineering, Faculty of Engineering
Universitas Muhammadiyah Sumatera Utara, St. Denai No 217, Medan (20371), Indonesia
Email: suwarno@umsu.ac.id
1. INTRODUCTION
The wind speed is one of the indicators for measuring weather somewhere and is indicated by the
steepness of the pressure differences. The difference in pressure affects the strong and weak wind speeds.
According to several previous researchers [1], [2] and others have presented wind speed parameters [3]-[6].
Wind speed characteristics are analyzed in depth to improve the performance of the electricity production in
specific locations [7]-[10]. A Weibull distribution with maximum likelihood, energy pattern factor, and R2
were analyzed [11], comparing the fourth accuracy is a Weibull distribution, Rayleigh, Gamma, and
lognormal [12], comparison W3, W2, Gamma, Lognormal has been analyzed [13], estimate wind speed using
the Weibull distribution function [14].
An estimated wind speed estimation technique based on a Kalman filter is applied to the wind
turbine has been carefully reviewed [15]. Wind speed model development model MMODA ICEEDAN-based
simulations can exceed the comparative method [16]. Weibull parameters calculated using ten different
methods and a comparison of performance that, MO is the best method to find the Weibull parameters for the
entire duration of the measurement [17]. A new sensorless based wind speed model was developed using
SCIM as the wind turbine simulator [18], ANN models were developed, the results show that the suitability
of the use of ANN in the accuracy of the predicted [19]- [22].
ARIMA modeling has been frequently used in recent decades to model wind speed and wind power
variation over large intervals, usually on the order of one hour [23], [24], and this model has the advantage of
the computational cost, as well as on simple repetitive procedure [25]. In the energy market, in terms of
investment strategies, modeling wind generation output is very important [26], including variations in energy

 ISSN: 2088-8694
Int J Pow Elec & Dri Syst, Vol. 12, No. 3, September 2021 : 1823 – 1831
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demand [27], and monthly variations in wind power [28]. Several approaches have been used to forecast
wind power by developing an algorithmic model to anticipate the level of uncertainty and variability of wind
generation [29].
Yuri et al. [30], modeling wind speed using Slashed-Rayleigh, where the ratio between the two
independent random variables, Rayleigh distribution in the numerator, and the power of a random variable
uniform in the denominator where Rayleigh sliced to provide a better match than the distribution of slash-
Weibull. Rashad et al. [31], modeled the wind speed using the Rayleigh unit distribution to estimate the
unique unknown parameter. Kachnia and Szewczyk [32], modeled the Rayleigh distribution which was
applied to the hysteresis circle of magnetic materials. Yolanda et al. [33], modeling wind speed with
Rayleigh-Lindley with the EM algorithm as an alternative solution. Gorla et al. [34], Rayleigh distribution
model for wind farms and the monthly output is expected to consider the seasonal effect of the wind speed
can be used.
Generally, some previous researchers have done a mathematical modeling approach to the
characteristics of wind speed but need to develop other models to add to their knowledge. Rayleigh model of
the proposed modifications aimed at minimizing defect characteristics obtained from a previous. To get wind
speed modeling that is closer to the actual characteristics, it is necessary to have a model that is suitable for a
certain area and is expected to be used in the process of assessing the potential for future wind energy.
Researchers conducted the development of modeling wind speed with a modified Rayleigh distribution
model approach for eliminating defects characteristics. Apart from the observed characteristics of the
Rayleigh distribution function, the suitability of the measured/recorded data and the modeling data is also
analyzed. This study aimed to obtain a new model of the modified Rayleigh distribution and analyze the
suitability of the characteristics of wind speed.
2. RESEARCH METHOD
The use of wind speed data observed in this study was obtained from the measuring instrument
PCE-FWS 20. The proposed modeling wind speed is approached with the measurement data recorded by the
device. Based on the measured data, then do the model proposed approach to obtain data that will be used for
simulated and tested for compliance with the measured data, then testing to ensure conformity of the
proposed model of wind speed. The suitability test uses the correlation coefficient (R2
), root means square
error (RMSE), and mean absolute percentage error (MAPE).
2.1. Wind speed data recorder
PCE-FWS 20 is a wireless weather station that is versatile, as it allows the accurate recording of
wind direction, wind force, temperature, relative humidity, and rainfall. Weather data is sent up to 100 meters
via a radio signal to the main station, equipped with the latest technology in weather analysis and powered by
solar panels and batteries. With a USB interface and the included USB cable, the weather data can be sent
directly from the wireless weather station to a PC or laptop. All these data are stamped with the time/date to
be set even after a longer period and weather data can be stored indefinitely.
The analysis software provided makes it possible to observe and compare the weather over a longer
period using charts. The PCE-FWS 20 Weather Station allows high accuracy detection of wind direction,
wind speed, temperature, relative humidity, and rainfall. The PCE-FWS 20 station is shown in Figure 1.
(a) (b)
Figure 1. device PCE-FWS 20: (a) wind speed detector, (b) wind speed recording device

Int J Pow Elec & Dri Syst ISSN: 2088-8694 
Wind speed modeling based on measurement data to predict future wind speed … (Suwarno)
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2.2. Wind speed data
Wind speed data recording is taken based on the duration of 30 minutes installed and processed into
monthly wind speed data, from January to December 2019. This wind speed data is a benchmark for the
proposed wind speed modeling and is analyzed and evaluated.
2.3. Modified Rayleigh distribution
The Rayleigh distribution is often used in physics when it comes to modeling processes such as
sound and light radiation, wave height, and wind speed. In addition to the Weibull distribution, Rayleigh
distribution is also a distribution deemed appropriate to describe the distribution of wind speed. This
distribution is used when the Weibull distribution area is considered less accurate to apply.
The Weibull distribution for Pdf and Cdf is given by
( ) [ ( ) ] (1)
* + (2)
By giving the shape parameter value (k) of k = 2 in the Weibull distribution, the probability density
functions of the Rayleigh distribution (Pdfr) and Cdfr are stated as:
[ ( ) ] (3)
* + (4)
where v is the wind speed (m/s), c is the scale parameter.
The parameter c is a function of v when the curve reaches its peak. By taking the derivative of Pdfr
concerning v and setting it to zero and solving (3), then v is obtained, namely;
√
(5)
or √ (6)
with Cm, the scale parameter of the Rayleigh model is modified and the value of v is estimated so that the
shape of the entire curve and its area can be determined to v.
The previous formula shows the standard distribution, specifically, the total area under the Pdf curve
is 1. In actual applications, the constant K is multiplied by (3) and (4), where K is the total number of defects
or the total cumulative damage rate.
Substituting the value of (6) into (3) and (4) and to determine the model of a set of data points, K
and v are parameters that need to be estimated, so that the Pdfm and Cdfm forms for the proposed Rayleigh
model are;
[
( √ )
[ (
√
) ]] or * * ( )++ (7)
[ ( (
√
) )] or * ( ( ))+ (8)
The proposed modified Rayleigh model to eliminate the estimated wind speed characteristic defects
is shown in (7) and (8), wherein the study the K value is around 1.15.
2.4. Wind speed modeling
The Rayleigh distribution scale parameter is obtained using the maximum likelihood estimator as
expressed by (9) as;
√ ∑ (9)

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where Cr is the Rayleigh scale parameter and vi is the wind speed at the ith
time. The average of the
Rayleigh distribution function is determined by (10).
√ (10)
where 𝑟 is on the average of the Rayleigh distribution function.
The wind speed modeling developed in this study is a modified Rayleigh distribution and is stated
as;
√ ∑ (11)
where N is the amount of data; vi is the measured (recorded) wind speed data; vm is a proposed wind
speed modeling.
2.5. Statistical analysis of distributions
Model selection has become an important focus in recent years in statistical learning, machine
learning, and big data analytics [35]-[37]. Currently, there are several criteria in the model selection
literature. Many researchers [38], [39] have studied the problem primarily variable regression election in
three decades. The statistical significance of the model comparison can be determined based on the suitability
criteria in the literature [40]. Wind speed data modeling for the Rayleigh distribution function [41].
Deviations wind speed distribution using the Root Mean Square Error (RMSE) and annual energy production
(AEP) [42]. A statistical test in the case of this study is shown in Table 1.
Table 1. Presents a statistical test in the case of this study
No. Criteria Formula Explanation
1. R2
∑
∑
Measures the amount of variation accounted for the
fitted model
2. RMSE
√
∑
The square root of the measures the deviation
between the fitted values with the actual data
observation
3. MAPE
∑
| | MAPE is percentages, compares them between sets,
and can easily understand and interpret percentages
where yi is the ith
data; is the mean data to ith
; is the average data n is the number of model
observations; k is the estimated number where At are actuals and Ft corresponding forecasts or predictions.
3. RESULTS AND DISCUSSION
3.1. Rayleigh parameters and probability distribution functions
Rayleigh scale parameter (Cr) measured wind speed is calculated based on the equation of the (9),
whereas the modified Rayleigh scale parameter (Cm) is based on (6). The Rayleigh probability distribution
function (Pdfr) and the modified Rayleigh distribution function (Pdfm) are shown in (3) and (7), respectively.
Rayleigh scale parameter of the wind speed data is recorded and a modified Rayleigh scale parameter amount
of 5.2492 and 6.2424, respectively. The parameters scale for Rayleigh and Rayleigh modified are shown in
Table 2. The difference in minimum, maximum, and average between Rayleigh and Rayleigh probability
function is modified by -0.0095, 0.0277, and 0.0844, respectively, and the characteristics of the Rayleigh
probability function are shown in Figure 2.
The comparison of the mean error value between the modified Rayleigh and Rayleigh scale
parameters is about -18.94% (<0.0 %), this indicates that the proposed model has a very small error than the
Rayleigh scale factor model. Figure 2 shows a comparison between the probability function of the measured
data and the prediction that at wind speeds greater than 3 m/s, the modified Rayleigh model will give a better
Pdf value when compared to the Rayleigh model before it was modified.

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Table 2. Parameters scale Rayleigh (Cr) and
modified Rayleigh (Cm)
Month Cr Cm
January 5.04 5.99
February 5.19 6.17
March 5.02 5.97
April 5.15 6.13
May 5.17 6.15
June 5.45 6.49
July 5.13 6.10
August 5.39 6.42
September 5.42 6.44
October 5.46 6.49
November 5.34 6.36
December 5.60 6.65
Year 5.25 6.24
Figure 2. The difference between the two models of Rayleigh
3.2. Wind speed data recording
Based on the results of data recording with PCE-FWS 20, after processing the recording data with a
duration of 30 minutes into daily and monthly data, the results are shown in Figure 3. Figure 3 shows the
wind speed fluctuates between 2.4 m/s to 7.4 m/s. The minimum, maximum and average wind speeds are
2.37 m/s, 7.39 m/s, and 5.06 m/s, respectively.
3.3. Wind speed data modeling
Based on (11), the obtained results of modeling wind speed are shown in Figure 4. Figure 4 shows
the wind speed fluctuates between 3.6 m/s to 6.3 m/s. The minimum, maximum and average wind speeds are
3.62 m/s, 6.38 m/s, and 5.25 m/s, respectively.
Figure 3. Wind speed measure Figure 4. Wind speed modeling
3.4. Comparison of wind speed modeling and measurement
Comparison of the wind speed of the recorded data and modeling are shown in Figure 5. Figure 5
shows a comparison between the measurement data and modeling based on a graph, where the color „blue‟ of
the measurement data, while the color „green‟ for data modeling. The comparison of the two data shows a
difference between the minimum, maximum, and average values of 0.525, 0.136, and 0.037, respectively.
The measured wind speed and the modified wind speed model are shown in Figure 6, where both have
similar shapes, but the proposed model looks better.
0 5 10 15
-0.02
0
0.02
0.04
0.06
0.08
0.1
0.12
0.14
0.16
Wind speed (m/s)
Probability
function
Rayleigh Measurement
Rayleigh Modified
Difference Probability
0 50 100 150 200 250 300 350 400
2
3
4
5
6
7
8
day
Wind
speed
(m/s)
Wind speed measured
0 50 100 150 200 250 300 350 400
3.5
4
4.5
5
5.5
6
6.5
day
Wind
speed
(m/s)
Wind speed Aproximation

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Figure 7 shows a comparison of measured wind speed data and modeling with modified Rayleigh
with minimum, maximum and mean values of -1.0059, 1.2454, and 0.0236, respectively. Figure 7, color
'blue' measured wind speed data, the color 'red' is a predicted wind speed data and the color 'green' is the
difference between the measured wind speed data with predicted data.
Figure 5. Comparison of measurement and forecast wind speed
(a) (b)
Figure 6. Measured and proposed wind speed: (a) wind speed measured, (b) wind speed modified
Figure 7. Comparison of measurement and forecast wind speed
0 50 100 150 200 250 300 350 400
2
3
4
5
6
7
8
day
Wind
speed
(m/s)
Wind speed measured
Wind speed Aproximation

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3.5. Statistical test results
Based on the results of the suitability test of the measurement and approach wind speed data with
the correlation coefficient (R2
), root mean square error (RMSE), and mean absolute percentage error (MAPE)
are shown in Table 3 as follows;
Table 3. Statistic analysis for monthly of R2
, RMSE, and MAPE
R2
RMSE MAPE
January 0.9968 0.1126 -40.21
February 0.9960 0.1191 -27.17
March 0.9956 0.1303 -39.65
April 0.9988 0.1055 -31.11
May 0.9971 0.1073 -27.98
June 0.9976 0.0986 -2.686
July 0.9985 0.0902 -34.33
August 0.9994 0.0950 -9.664
September 0.9979 0.0793 -7.439
October 0.9989 0.0938 -1.571
November 0.9969 0.0921 -13.80
December 0.9987 0.0943 10.583
Average 0.9145 0.1015 -18.7528
Table 3, shows that the correlation coefficient test (R2
) every month is between 0.9956-0.9994 with
an average of 0.9145, this result gives a good meaning because it is close to 1. While the monthly RMSE test
is between 0.0793-0.1303 and with an average of 0.1015, this result gives a good meaning because close to
zero. While the MAPE test every month is between -40.21-10.583, with an average of -18.7528, this result
gives a very good meaning because <10%.
4. CONCLUSION
The proposed wind speed modeling has fulfilled the statistical test requirements, according to the
correlation coefficient (R2
), RMSE and, MAPE. The test result data by monthly statistics and averages
indicate that the modeling approach correlation coefficient (R2
) of 0.9145, the test results with RMSE of
0.1015, and test results with MAPE of -18.7528. The results of the three tests indicate that the proposed
model is well received.
ACKNOWLEDGEMENTS
The authors thank the chief editor and his staff, as well as the Executive Board Muhammadiyah
University of North Sumatra which has provided the opportunity to publish the results of thought and
research, which may be useful for everything.
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