The two main challenges of predicting the wind speed depend on various atmospheric factors and random variables. This paper explores the possibility of developing a wind speed prediction model using different Artificial Neural Networks (ANNs) and Categorical Regression empirical model which could be used to estimate the wind speed in Coimbatore, Tamil Nadu, India using SPSS software. The proposed Neural Network models are tested on real time wind data and enhanced with statistical capabilities. The objective is to predict accurate wind speed and to perform better in terms of minimization of errors using Multi Layer Perception Neural Network (MLPNN), Radial Basis Function Neural Network (RBFNN) and Categorical Regression (CATREG). Results from the paper have shown good agreement between the estimated and measured values of wind speed.

1. Physica A 542 (2020) 123383
Contents lists available at ScienceDirect
Physica A
journal homepage: www.elsevier.com/locate/physa
Artificial Neural Network based computing model for wind
speed prediction: A case study of Coimbatore, Tamil Nadu,
India
R Kaja Bantha Navas a,∗
, Dr. S Prakash a
, Dr. T Sasipraba b
a
School of Mechanical Engineering, Sathyabama Institute of Science and Technology, Chennai, India
b
School of Computing, Sathyabama Institute of Science and Technology, Chennai, India
a r t i c l e i n f o
Article history:
Received 25 August 2018
Received in revised form 23 June 2019
Available online 4 November 2019
Keywords:
Wind speed
Forecasting
Neural Network
Multilayer Perceptron
Radial Basis Function
Categorical Regression
a b s t r a c t
The two main challenges of predicting the wind speed depend on various atmospheric
factors and random variables. This paper explores the possibility of developing a
wind speed prediction model using different Artificial Neural Networks (ANNs) and
Categorical Regression empirical model which could be used to estimate the wind speed
in Coimbatore, Tamil Nadu, India using SPSS software. The proposed Neural Network
models are tested on real time wind data and enhanced with statistical capabilities.
The objective is to predict accurate wind speed and to perform better in terms of
minimization of errors using Multi Layer Perception Neural Network (MLPNN), Radial
Basis Function Neural Network (RBFNN) and Categorical Regression (CATREG). Results
from the paper have shown good agreement between the estimated and measured
values of wind speed. According to the result, it can be concluded that ANN model with
MLPNN could produce the acceptable prediction of the wind speed for given on wind
direction.
© 2019 Elsevier B.V. All rights reserved.
1. Introduction
Energy resources for electricity generating exhibit relationship with economic growth [1]. Researchers and govern-
ments explore new sources of energy for economic development [2]. Oil price imply an increase of renewable energy
consumption. Researchers have proved that Gross Domestic Product (GDP) is proportional to the electricity utilization
using Artificial Neural Network (ANN) [3]. Mohandes, M.A et al. (2004), worked out the wind speed prediction model
using support vector machines and MLPNN. Mean daily wind speed data from Madina city, Saudi Arabia, was used for
building and testing both models. Their results indicated that SVM complies favourably with the MLP model based on the
root mean square errors between the actual and the predicted data [4]. Radial basic function in the hidden layer produces
a significant non zero response only when the input falls within a small localized region of the input space. Gao Chang C
and Dao Huo H (2015) proposed RBFNN based on wind speed using one of the wind frame data from Beijing [5]. Li G and
Shi J (2018) investigated North Dakota sites wind data using different ANN models. In this analysis, one of them has been
RBFNN [6]. Categorical regression (CATREG) is optimal linear regression involves quantifying categorical data by assigning
numerical values to the categories. Compared to linear regression analysis, it minimizes the sum of squared differences
between a response (dependent) variable and a weighted combination of predictor (independent) variables [7]. However
∗ Corresponding author.
E-mail addresses: sumai244@gmail.com (R.K.B. Navas), facultyhead.mech@sathyabama.ac.in (Dr. S. Prakash), tsasipraba@yahoo.com
(Dr. T. Sasipraba).
https://doi.org/10.1016/j.physa.2019.123383
0378-4371/© 2019 Elsevier B.V. All rights reserved.

2. 2 R.K.B. Navas, Dr. S. Prakash and Dr. T. Sasipraba / Physica A 542 (2020) 123383
Table 1
Research parameters and study location.
Ref Authors Year Study location Parameters considered References
1 Tukuran et al. 2016 Kutahya, Turkey Wind speed, Wind frame height [8]
2 Kaur D et al. 2014 Auckland City, New Zealand Wind speed [9]
3 Zhang J et al. 2017 South Dakota, USA Wind speed [10]
4 Ranganayaki V et al. 2016 Palladam, India Wind speed, Direction, Temperature, Humidity [11]
5 Imaie E et al. 2014 Semnan-Iran Wind speed, Direction, Pressure and Temperature [12]
6 Fazelpour F et al. 2016 Tehran, Iran Wind speed, Direction, Pressure and Temperature [13]
7 Giorgi M G D et al. 2014 Italy Wind speed [14]
Table 2
Mean wind speed and mean wind direction.
Sl.
No
Mean wind speed
(m/s) at 65 m
Mean wind speed
(m/s) at 50 m
Mean wind vane direction from
true north (degree) at 63 m
Mean wind vane direction from
true north (degree) at 48 m
1 6.480 5.992 212.475 219.080
Table 3
Automatic architecture selection details for MLPNN and RBFNN.
Sl. No Network layers Network information MLPNN RBFNN
1 Input layer Factors Wind speed (m/s) at 50 m, wind vane direction from true north
(degree) at 63 m, wind vane direction from true north (degree) at 48 m
No of units 674 672
2 Hidden layer No of units 16 6
Activation function Hyperbolic tangent Softmax
3 Output layer Factor Wind speed (m/s) at 65 m
No of units 1
Rescaling Method Standardized
Activation function Identity
Error function Sum of squares
to the best of the authors’ knowledge, the present study is one of the first applications of CATREG in renewable energy
resources forecasting. In Table 1 is presented some listed references for wind energy resources parameters considered by
researchers in different locations around the world. In this paper, ANN models MLPNN, RBFNN and CATREG are used for
the wind speed (m/sec) at 65 m height based on wind speed (m/sec) at 50 m height, wind vane direction from true north
(degree) at 63 m and wind vane direction from true north (degree) at 48 m at Palladam, Coimbatore located in Tamil
Nadu state of India.
2. Materials and methods
The real time wind farm data are obtained from Suzlon Energy Limited Palladam for a period from April, 2005 to March
2006. The wind data collection station is situated at latitude of 10◦
59′
25.8′′
longitude 77◦
17′
10.9′′
with geodetic WGS84
system. From the data collected, ie, wind speed and wind direction for every minute of one year wind data. Mean wind
speed and mean wind direction are presented in Table 2.
In this study wind speed (m/sec) at 50 m height, wind vane direction from true north (degree) at 63 m and wind vane
direction from true north (degree) at 48 m were used inputs and MLPNN, RBFNN and CATREG were used as the wind speed
estimation methodology. The block diagram Fig. 1 shows the procedure for the wind speed prediction based on wind speed
and wind direction by ANN models and Categorical Regression. 52 560 observations were randomly assigned cases based
on relative number of cases with 70 percentage training partition and 30 percentage testing partition. Automatic Neural
Network architecture selection details for both MLPNN, RBFNN are presented in Table 3.
3. Result
Forecasted wind speeds with the three ANN models for the period of April 2005 to March 2006 is compared with the
actual measured wind speed data for the period of April 2006 to March 2007. According to the goodness-of-fit criterion,
Error, MSE and R2
approaches are applied for performance analysis. The statistical indicators of wind speed estimation is
presented in Tables 4 and 5 based on the ANN models MLPNN, RBFNN and CATREG. For MLPNN, correlation Coefficient
between the actual and predicted wind speed of 0.977 was obtained with Mean Square Error 0.022 and error 0.025 for
wind speed. It can be seen that MLPNN gives the minimum error and the most reliable technique. The results of validation
and comparative study indicate that the MLPNN based estimation technique for wind speed is more suitable to predict the
wind speed than RBFNN and CATREG. R2
is one of the common statistical performance evaluating methods. R2
presents

3. R.K.B. Navas, Dr. S. Prakash and Dr. T. Sasipraba / Physica A 542 (2020) 123383 3
Fig. 1. Wind speed estimation by Neural Network models and Categorical Regression model.
Table 4
Precision of the wind speed prediction by Neural Network models.
Sl.No NN architecture R2
Training Testing
Mean Square Error Error Mean Square Error Error
1 MLPNN 0.977 397.723 0.022 195.417 0.025
2 RBFNN 0.527 8948.027 0.487 3853.775 0.477
Table 5
Precision of the wind speed prediction by Categorical Regression.
Sl. No Regression R2
Mean Square Error Error
1 CATREG 0.959 5598.937 0.041
forecasted values and measured values at x and y axis. If the forecasted values and measured values are closer, R2
will be
nearer to 1. Tables 4 and 5 show training validation and test results of R2
and all of them together. The actual Vs predicted
wind speed is presented in Figs. 2 and 3 for by MLPNN and RBFNN. In CATREG, We cannot able to correlate the predicted
wind speed and actual wind speed because of negative predicted wind speed values. In this situation, predicted wind
speed and actual wind speed are classified into cases using visual binning tool with equal percentiles. The cases for actual
Vs predicted wind speed is presented in Fig. 4 for by CATREG. The results of the model show that the predicted data is
close to the actual wind data available. Based on the coefficient of the determination values R2
one can conclude that the
MLPNN has the higher precision for the wind speed prediction. However RBFNN and CATREG produce lower precision for
wind speed estimation in our study.
4. Conclusion
Wind speed could play a main role in electricity market. This study confirms the ability of the ANN to predict wind
speed values precisely. The performance of MLPNN, RBFNN and CATREG were comprehensively investigated based on
the wind data in Coimbatore. Their predictive performances are compared with suitable measured data. According to the
Error, R2
and Mean Square Error approaches are applied for performance analysis. R2
is only an indicator of best fit of a
linear fit line to data, the data scatter and deviations which are high at the higher wind speed ranges even in the MLPNN
case, seems to be averaged out in the overall fit. Mere statistics matching is not sufficient for wind energy applications as
even 1% uncertainty in predictions will have high economic deviations in annual energy predictions. Recommendations
for future studies include employing the proposed model with Hybrid Neural Network for the wind speed prediction with
more wind parameters.
Acknowledgements
The authors are thankful to Suzlon Energy Limited for providing real time data of Wind frame through Dr. S.N. Deepa,
Associate Professor, Department of Electrical and Electronics Engineering, Anna University Regional Campus, Coimbatore,

4. 4 R.K.B. Navas, Dr. S. Prakash and Dr. T. Sasipraba / Physica A 542 (2020) 123383
Fig. 2. Wind speed estimation by MLPP.
Fig. 3. Wind speed estimation by RBFNN.
Tamil Nadu 641 046 in order to carry out the research work. The authors are also very grateful to Song, Wheyming (Tina),
Distinguished Professor, Dept. of Industrial Engineering and Engineering Management, National Tsing Hua University,
Taiwan for fruitful discussions during the Summer Research Program under Global Engineer Leadership Scholarship.
Appendix
The performances of the models are measured using error, Mean Square Error (MSE) and R2
by using the equations
Error = Xi − Yi (A.1)
Mean Square Error (MSE) = 1/n
i=n
∑
i=0
Xi − Yi (A.2)

5. R.K.B. Navas, Dr. S. Prakash and Dr. T. Sasipraba / Physica A 542 (2020) 123383 5
Fig. 4. Wind speed estimation by CATREG.
R2
= 1 − (
i=n
∑
i=0
(Xi − Yi)2
)/(
i=n
∑
i=0
(Xi −
⇀
X)2
) (A.3)
Xi, Yi,
⇀
X and n are measured value of ith observation, predicted value of ith observation, mean value of measured value
and no of observation.
References
[1] F. Ge, B. Ye, B. Wang, S. Sun, The analysis of the underlying reasons of the inconsistent relationship between economic growth and the
consumption of electricity in China – A Case Study of Anhui Province, Energy (2017) 601–608.
[2] S. Sarwar, R. Chen, Waheed, Electricity consumption, oil price and economic growth : Global perspective, Renew. Sustain. Energy Rev. 76 (2017)
9–18.
[3] M. Stevanovic, S. Vujicic, A.M. Gajic, Gross domestic product estimation based on electricity utilization by artificial neural network, Physica A
489 (2018) 28–31.
[4] M.A. Mohandes, T.O. Halawani, S. Rehman, A.A. Hussain, Support vector machines for wind speed prediction, Renew. Energy 29 (2004) 939–947.
[5] C. Gao Chang, H. Dao Huo, Ultra short term wind prediction using RBFNN, in: International Symposium on Computers and Informatics, 2015,
pp. 2441–2448.
[6] G. Li, J. Shi, On comparing three artficial neural networks for wind speed forecasting, Appl. Energy 87 (2010) 2313–2320.
[7] IBM Knowledge Centre Study Material, https://www.ibm.com/support/knowledgecenter/en/SSLVMB_24.0.0/spss/categories/idh_catr.html.
[8] Y.S. Turkan, H.Y. Aydogmus, H. Erdal, The prediction of the wind speed at different heights by machine learning methods, Int. J. Optim. Control:
Theor. Appl. 6 (2) (2016) 179–187.
[9] D. Kaur, T.T. Lie, N.K.C. Nair, Valles Brice, Wind speed forecasting using hybrid wavelet transform, AIMS Energy 3 (1) (2016) 13–24.
[10] J. Zhang, Y. Wei, Z. Tan, K. Wang, A hybrid method for short-term wind speed forecasting, Sustainability 5 (596) (2017) 1–10.
[11] V. Ranganayaki, S.N. Deepa, An intelligent ensemble neural network model for wind speed prediction in renewable energy systems, Sci. World
J. 2016 (2016) 1–14.
[12] E. Imaiel, A. Sheikholeslami, R.A. Ahangar, Improving short-term wind power prediction with neural network and ICA algorithm and input
feature selection, J. Adv. Comput. Res. 5 (3) (2014) 13–34.
[13] F. Fazelpour, N. Tarashkar, M.A. Rosan, Short-term wind speed forecasting using artificial neural networks for tehran, iran, Int. J. Energy Environ.
Eng. 07 (2016) 377–390.
[14] M.G.D. Giorgi, S. Campilongo, A. Ficarella, P.M. Congedo, Comparison between wind power prediction models based on wavelet decomposition
with least-squares support vector machine (LS-SVM) and artificial neural network (ANN), Energies 7 (2014) 5251–5272.
R Kaja Bantha Navas works as a doctoral student in the School of Mechanical Engineering at Sathyabama Institute of Science and Technology,
India. He received his Engineering Master’s in Industrial Engineering from Thiagarajar College of Engineering in 2007. His research interests are
Renewable Energy, Computational Intelligent, Industrial Engineering, Design of Experiments, Optimization, Mathematical Modelling and Machine
Learning Techniques.
Dr. S. Prakash has wide knowledge and experience in manufacturing processes, its modelling and optimization using various tools and software
packages like Minitab, Design Expert, MATLAB etc., He is currently the Professor of the School of Mechanical Engineering, Sathyabama Institute of

6. 6 R.K.B. Navas, Dr. S. Prakash and Dr. T. Sasipraba / Physica A 542 (2020) 123383
Science and Technology, India. His contributions in this field are witnessed by his publications, counting to more than 50, in various refereed journals.
He also holds prestigious positions like the Chairman of Indian Institute of Production Engineers, life time member at the Institute of Engineers etc.,
Dr. T. Sasipraba joined Sathyabama Institute of Science and Technology in 1995 as a Lecturer and her 25 years of meritorious career has promoted
her as a Pro Vice Chancellor in the year 2014. During the course of her career at SATHYABAMA, Dr. T. Sasipraba has made exceptional contributions
in the areas of research and developments, international linkages and Publications. She has published more than 75 papers in refereed international
journals and conference proceedings and has guided many Ph.D Scholars in the field of Computer Science and Engineering.