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Turk J Elec Eng & Comp Sci
() : –
© TÜBİTAK
doi:10.3906/elk-1601-213
Turkish Journal of Electrical Engineering & Computer Sciences
http://journals.tubitak.gov.tr/elektrik/
Researc h Article
Forecasting of short-term wind speed at dierent heights using a comparative
forecasting approach
Emrah KORKMAZ1,2∗,, Ercan İZGİ1,, Salih TÜTÜN3,
1Department of Electrical Engineering, Yıldız Technical University, İstanbul, Turkey
2Department of Electrical and Computer Engineering, Binghamton University, Binghamton, NY, USA
3Department of Systems Science and Industrial Engineering, Binghamton University, Binghamton, NY, USA
Received: 19.01.2016 • Accepted/Published Online: 29.04.2018 • Final Version: ..201
Abstract: The forecasting of wind speed with high accuracy has been a very signicant obstacle to the enhancement
of wind power quality, for the volatile behavior of wind speed makes forecasting dicult. In order to generate more
reliable wind power and to determine the best model for dierent heights, wind speed needs to be predicted accurately.
Recent studies show that soft computing approaches are preferred over physical methods because they can provide fast
and reliable techniques to forecast short-term wind speed. In this study, a multilayer perceptron neural network and an
adaptive neural fuzzy inference system are utilized to both forecast wind speed and propose the best model at heights of
30, 50, and 60 m. It is obvious that various internal and external parameters for soft computing methods have paramount
importance for forecasting. In order to analyze the impact of these parameters, new wind speed data were collected from
a wind farm location. Miscellaneous models were created for every wind turbine elevation by adjusting the parameters of
soft computing methods in order to improve wind speed forecasting errors. The experimental results demonstrate that
elevation of collected wind speed data signicantly aects the wind speed forecasting. Our experimental results reveal
that although behavior of wind speed for every height appears identical there is no single model to predict wind speed
with the best accuracy. Therefore, every model for the soft computing methods shall be modied for every particular
wind turbine height so that wind speed forecasting accuracy is improved. In this way, the approaches perform with fewer
errors and models can be used to predict wind speed and power at dierent heights.
Key words: Forecasting, wind energy, soft computing methods, time series analysis
1. Introduction
Nowadays, most countries rely heavily on fossil fuels to generate their own electricity. Power plants based on
fossil fuels, however, cause many environmental problems. These power plants have emitted large amounts of
greenhouse gases. As a result, many people face a sharply high risk of breathing problems, cancer, and heart
attacks [1]. Thus, worldwide many people are seeking out new energy sources that will produce cleaner energy.
Even though nonrenewable energy sources (e.g., coal, petroleum, and natural gas) are available in most
of the world, these sources will get more expensive because of restricted reserves [2,3]. On the other hand,
renewable energy is clean, environmentally friendly, and inexhaustible. Over the last decade renewable electricity
generation capacity has increased signicantly, but this capacity has still not been enough to replace the energy
capacity that comes from fossil fuels. If we want to replace fossil fuels with renewable energy sources, then more
∗Correspondence: emr3234@hotmail.com
This work is licensed under a Creative Commons Attribution 4.0 International License.
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research is needed concerning their negative sides, and better renewable energy technologies must be developed.
This could well lead to people having cheaper and cleaner energy.
Wind energy is one of the fastest growing forms of renewable energy. For instance, over the last 10 years
in Turkey, installed wind power capacity has increased approximately 10% each year [4]. Despite this rapid
growth of wind power, it still does not look like a reliable energy source that can meet future demands of the
electricity grid. One of the most important reasons for this is the unreliability issue: wind speed proles are
irregular [5]. In this regard, it is essential to make accurate wind speed estimates in order to develop a more
reliable structure for wind power plants.
In the present paper, a comparative forecasting approach based on soft computing methods is proposed
to improve the prediction of short-term wind speed at dierent heights. It is well known that soft computing
methods outperform other methods and can achieve better results in short-term wind speed forecasting [6].
Therefore, we utilized algorithms of ANFIS and MLP neural network to predict wind speed with minimum
errors. In order to achieve this goal, we created dierent models for each forecasting method and compared
these models based on forecasting errors so that wind speed estimation error can be diminished.
This paper presents current and future research aimed at the development of comprehensive wind speed
forecasting for dierent wind turbine heights. The study is performed as experimental work in order to guide
wind speed forecasting researchers who might utilize soft computing methods. With these methods, a large
number of model parameters shall be optimized and the best models can be determined for realistic wind speed
forecasting. However, the authors are not familiar with publications featuring experimental studies for wind
speed prediction at dierent heights using soft computing methods. As a result, we present a comparative
forecasting approach in order to forecast short-term wind speed at dierent heights for the same location. Since
wind speed has nonlinear behavior, the features of models of soft computing methods should be particularly
distinguished for dierent wind turbine height.
Furthermore, many studies on soft computing based wind speed forecasting were conducted at height of
10 m. It is obvious that wind speed data at 10 m is not sucient for selection of wind turbine location and
not feasible for wind energy estimation and so we collected new data directly from a data logger located at a
wind farm. Therefore, the present study aims to enhance soft computing based wind speed prediction results
regarding their use in industrial applications for choosing dierent wind turbine heights of 30, 50, and 60 m.
The rest of this paper is organized as follows. Section 2 describes related work. In Section 3 we present
the materials and methodologies used in our problem formulations. In Section 4 we introduce our results to
show the proposed models predict wind speed at dierent heights with outperformance. Finally, we provide a
conclusion with the best models to forecast wind speed in Section 5.
2. Related work
Dierent forecasting methods are used in most research studies. Generally speaking, all forecasting techniques
can be described under three main approaches: physical, statistical, and hybrid methods. The most used
physical method is the numerical weather prediction method (NWP) developed by experts in meteorology. The
main purpose of NWP is to dene atmospheric phenomena using mathematical models. This can be done
using large amounts of weather data that represent every small region. It is quite hard to develop a perfect
weather prediction because it needs many calculations, and these calculations need supercomputers. Even when
supercomputers are used to predict weather, however, the calculation time is very long. Although NWP needs
long calculation times, it is more eective for long-term prediction. Therefore, many industries and government
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KORKMAZ et al./Turk J Elec Eng & Comp Sci
agencies are extensively using the NWP technique. Furthermore, it has been very important in allowing military
operations to forecast weather. However, the NWP technique is quite ineective for short-term wind forecasting
because wind speed has a very high variation in a short period of time [7].
Recent publications have preferred using statistical methods over physical methods. Statistical methods
can be developed in which historical values of wind speed are utilized. Statistical prediction methods can be
classied into two main approaches: (1) time-series models (such as autoregressive and moving average models)
and (2) soft computing models (such as articial neural networks (ANNs), fuzzy logic). Recent publications
demonstrate that genetic algorithms (GAs), support vector machines (SVMs), and Kalman ltering (KF) models
are integrated into gray-box methodologies in order to reduce prediction errors. A brief review of recent studies
on wind speed prediction is introduced in the following paragraphs.
Based on the time series models, short-term wind speed prediction is performed by linear and nonlinear
autoregressive models [8]. Wind speed prediction models dier regarding their prediction intervals ranging from
10 min to 1 h. According to [9], KF can be successfully implemented for short-term predictions and the authors
propose a new KF method instead of using the standard KF in order to yield better prediction results. In
[10], the researchers develop a new technique using heteroscedastic support vector regression diminishing the
uncertainty of short-term wind speed. The wind speed prediction results are obtained for wind power plants in
China and predicted for 30, 60, and 120 min in the future with errors above 10% MAPE.
Today, more and more researchers are using soft computing algorithms based on ANNs to make short-
term forecasts [11–14]. This is because ANNs use linear assumptions and they are more eective in modeling
the nonlinearity relationship of wind speed data [15].
The literature on this issue includes many publications. These can be summarized in the following
studies. Cadenas and Rivera [16] used the ANN model to predict short-term wind speed. The results of their
study show that the two-layer and three-neuron models for the training and testing stages give satisfactory
accuracy for short-term forecasting. The implementation of ANN to forecast wind speed in [17] demonstrates
that ANN models predict wind speed with acceptable accuracy (8.9% MAPE and correlation of 0.9380 m/s).
In the study conducted by De Giorgi et al. (the wind farm model in southern Italy), wind energy prediction
is made by autoregressive moving average (ARMA), ve dierent ANN models, and the neuro-fuzzy inference
system (ANFIS) [18]. For predictions of 1, 3, 6, and 12 h, multilayer perceptron (MLP) performance appears
better than other methods and gives a short calculation time.
Another study is conducted by [19], where the authors predict daily wind speed using an MLP neural
network and propose to utilize meteorological parameters (e.g., temperature, air pressure, solar addition, and
altitude) as input variables. However, the collected data are obtained for only two dierent altitudes that are
very close to each other (14.5 m and 18.5 m). The recently published article by [20] presents a short-term wind
speed prediction technique that demonstrates that wind speed can be forecasted easily using ANNs, but the
maximum lead time for the measured data must be 14 h.
In [7], several dierent ANFIS models were used to predict very short-term wind speed. The data set is
prepared by a 21-month time series using 2.5-min intervals. In that study, the ANFIS model estimates results
in less than 4% MAPE. Approaches using BPNN, RBFNN, and ANFIS short-term wind speed forecasts, which
are 1-h-ahead and 3-h-ahead, are assessed in [21]. These forecasting techniques are combined with a similar day
(SD) approach. ANFIS models based on the SD approach are more successful in transforming historical data
into wind speed forecasts.
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KORKMAZ et al./Turk J Elec Eng & Comp Sci
Furthermore, in the literature, hybrid approaches are able to predict wind speed with high accuracy.
Hybrid models can consist of not only physical and statistical techniques but also can only be developed by
dierent statistical methods. In [22], two new hybrid approaches, known as the ARIMA-ANN and ARIMA-
Kalman lter models, are found suitable for wind speed prediction. Guo et al. [23] presented a case study
using a hybrid forecasting method with a back propagation neural network (BPNN) and seasonal adjustment.
The results show that rather than using only BPNN a hybrid technique must be used to improve prediction
performance.
In spite of that, in the literature, the performance of ANFIS and MLP approaches is not evaluated in
terms of dierent heights in a particular wind farm location. In order to propose or establish a reliable soft
computing method for short-term wind forecasting in the wind power industry, multivariate soft computing
models must be evaluated considering their use at dierent heights. The main contribution of the present paper
is to provide researchers with a comprehensive analysis of the eects of various heights on short-term wind
speed forecasting by using ANFIS and MLP approaches.
3. Materials and methods
3.1. Adaptive neural fuzzy inference systems approach
ANFIS methodology was rst introduced by Roger Jang in 1993 [24]. It aims to combine fuzzy logic and ANN
methods. Figure 1 provides a simple description of the ANFIS architecture, which has two inputs and one
output. In the rst layer (called the “input layer”), input values are transferred to the second layer. The
second layer, known as the “fuzzication layer”, enables nodes to change output depending on the membership
function. The parameters are dened for membership functions (µ(e.g., bell-shaped) as given by the equations
below:
A1 A2 B1 B2
X Y
N
W1
N
-—
X
Y
X
Y
Layer1
Layer2
Layer3
Layer4
Layer5
Lay
Σ
ππ
er 6
f
W2
-—
W1W2
W2
-— f2
W1
-— f1
Figure 1. Architecture of a two input ANFIS model.
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O2,i =µAi(x)(1)
O2,j =µBj−2(y)(2)
µ(x) = exp −1
2x−c
σ2,(3)
where cand σare parameters that correspond to the mean and standard deviation of the membership function,
respectively. In general, these parameters are referred to as premise or antecedent parameters.
In the third layer, each neuron represents a single fuzzy rule. The output of each node can be calculated
by the ring power of each fuzzy rule as Eq. (4).
O3,k =µAk(x)µBk(y) = ωk, k = 1,2(4)
The fourth layer can be described as a normalization layer. The normalized ring level can be expressed as the
ratio of the kth ring power of the sum of all ring powers.
O4,k =ωk
ωk
= ¯ωk, k = 1,2(5)
In the fth layer, in order to calculate the results of the fuzzy logic rules, weighted result values for each node
are calculated by the following formula, where α, βγ are constant values:
O5,k = ¯ωk(αk+βkx+γky), k = 1,2(6)
Finally, in the last layer, the sum of each output value received from previous layers is calculated and found as
f.
3.2. MLP articial neural network approach
ANNs are a commonly used technique in dierent tasks from process monitoring, fault diagnosis, and adaptive
human interference to articial intelligence based on atmospheric processes and computers [25]. The MLP is a
widely used type of neural network and usually called a feed-forward neural network. It consists of an input
layer, one or more hidden layers, and an output layer. Basically, the MLP solves the complex relationship
between input vector and output vector using connections of weighted layers. To solve a relationship of this
complexity the MLP needs training sets that consist of both input and output data. The MLP uses delta
learning, which is based on the least square method. This learning methodology consists of two training steps:
feed-forward and backpropagation.
The feed-forward learning is usually called feed forward because feedbacks among the nodes do not
appear. With a feed-forward neural network, the connection between the ith and jth neuron can be described
by the weight coecient ωij [26]. The output of each node for n input neuron and m hidden neuron can be
represented by the following equation:
yi=fH
vi+
n
j=1
ωij xj
,(7)
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KORKMAZ et al./Turk J Elec Eng & Comp Sci
where fHis called the activation function of a node and Viis the threshold coecient that corresponds to the
weight coecient of each jth neuron. This coecient is called the bias value when xjequals 1. All connection
weights and bias values must be initially assigned random values. In the training process, these values will be
adjusted by the network to nd the best output results.
The sigmoid function, which varies between 0 and 1, is used as the activation function of a node, as given
in the following equation:
fH(s) = 1
1 + e−s(8)
The supervised adaption process changes weight coecients and bias values between predicted and target
outputs [26]. By minimizing the objective function with a training algorithm, these values can be obtained.
The objective function can be calculated as
e=
n
i=1
(ti−yi)2(9)
In the case of backpropagation training, a Bayesian approach was utilized to accomplish better t, minimum
error, and minimum number of patterns and weights of the network. This process can be demonstrated by the
following equations:
ω(k+1)
ij =ω(k)
ij −λ∂e
∂ωij (k)
(10)
v(k+1)
i=v(k)
i−λ∂e
∂vi(k)
,(11)
where is λa constant learning rate. The learning process is repeated by many iterations so that the MLP
network may memorize the training data. Therefore, generalization between input and output patterns can be
eliminated.
3.3. Forecasting of wind speed at dierent heights
The steps used to predict accurate wind speeds as seen in Figure 2 can be summarized as follows:
(1) Use the ANFIS method and select the most accurate models separately for all heights.
(2) Use the MLP-based method and select the most accurate models separately for all heights.
(3) Do a comparison between the MLP and ANFIS models for every height and determine the best models
and wind speed prediction values.
The Silivri region in İstanbul was selected in order to implement wind speed forecasting methods. The
latitude and longitude of the recorded area is N 041◦08.377’ and E 028◦19.110’, respectively and the site
elevation is 210 m. Silivri is in the Marmara region, which has very high energy potential. It has been thus
a very investable area for the wind energy sector [27]. Wind speeds were measured at heights of 30 m, 50 m,
and 60 m via NRG #40C cup anemometers. The wind speed data was recorded as 10 min samples between
February 2009 and March 2010, and the total data has 56,548 points.
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S elect the Key
Features
Initialize
Determine
Parameters of
the Models
Choose Method
Utilize the
ANFIS
Approach
Utilize the
MLP-ANN
Approa ch
Compare the
Bes t Mode ls
h>60
S elect the Key
Features
End NoYes
Figure 2. The ow chart of the framework for forecasting wind speed.
The recorded dataset is used as inputs for all ANFIS and ANN models. Firstly, the dataset was split
into training (50%), evaluation (25%), and testing (25%) sets. The training set is used to train parameters of
the ANFIS and ANN models. The evaluation dataset is used to evaluate measures of forecasting accuracy by
tuning its parameters. The testing set is utilized to show error between real and forecasted wind speed values.
In order to solve wind forecasting problems using ANFIS and ANN methods, the input of each model
must be chosen correctly. The most common way to choose wind vectors is time-series methodology. Hence,
this methodology was used to determine model inputs in the present study. For instance, if one wants to predict
wind speed at v(t+ 1) time lag, the inputs of time series must be chosen using m previous measurements (such
as v(t−m), v (t−m+ 1), v (t−m+ 2) . . . v (t)) . In this particular wind forecasting study, the number of
previous observations was changed for both methodologies, and then for the selected wind speed input number,
each model was named Model 1, Model 2, Model 3, and Model 4.
3.3.1. Models of adaptive neural fuzzy inference systems
In the present study, many dierent ANFIS architectures are compared to nd the best wind forecasting
accuracy. In order to use ANFIS methods, the type of membership function must be decided on. The
membership function is considered as linear and constant. After many experiments, we settled on 2 and 3
membership functions as the appropriate number. Furthermore, since membership function type changes are
linear and constant, the number of epochs for each model must be selected. A lot of research has shown that a
large number of epochs usually does not improve the accuracy of models or signicantly increase the forecasting
time [28]. Therefore, it is adjusted to 10 and 100 in the structure of ANFIS. All of the parameter changes above
were implemented in all combinations individually.
3.3.2. Models of MLP articial neural networks
In order to test MLP based neural networks, the characteristic parameters of each model must be chosen so
that the number of input neurons is changed to 1, 2, 3, and 4. For all MLP models, hidden layers are used
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KORKMAZ et al./Turk J Elec Eng & Comp Sci
and the output neuron number is assigned to 1. The error between computed and desired wind speed values
is selected as 1 ×10 −5instead of 0 to stop training in a specic time. The number of epochs is selected as
50. The reason for the selection of a xed number of iterations and errors is that after a certain number these
values do not aect the results of estimated wind speed values.
3.4. Performance metrics for forecasting results
It is also essential to compare the performance of models in terms of forecasting accuracy. Unfortunately, there
is no unique metric to evaluate models as a universal standard [29]. Thus, the performance of models must be
evaluated by using dierent metrics such as determination of coecient (R2), root-mean-square error (RMSE),
and mean absolute percentage error (MAPE).
R2= 1 −σE
σY
(12)
RMSE =
1
N
n
i=1
(Ti−Yi)2(13)
M AP E =1
n
n
i=1
Ti−Yi
Yi
×100 (14)
The values of σYand σEindicate the standard deviation of observation values and error values, which is
calculated by each actual and forecasted wind speed dierence, and Tiand Yivalues show observation and
forecast values, respectively. The number of the dataset is also symbolized as nin the equations.
The coecient of determination shows the accuracy of predicted values. This value is expected to be
between 0 and 1. To have the best forecast model, the coecient of determination value should approach 1. The
RMSE is known as the standard deviation of the estimation errors in regression analyses. It can be found by
calculating the square root of the average of quadratic errors. The last metric is chosen as MAPE to calculate
forecast error. Since it is indicated by percentage expression, it gives the idea to everyone to understand the
calculated error simply. In the present study, the RMSE and the MAPE are minimized. In many papers, it
goes nearly to zero, but in this particular research some large numbers might be seen due to the large number
of test datasets used.
4. Results for wind speed prediction at dierent heights
Forecasting methods ensure decision makers develop stronger knowledge for better decision making [30,31]. The
purpose of the present research is to analyze the applicability of soft computing methods such as ANFIS and
ANN. In order to address this issue, we formed dierent soft computing models based on ANFIS and ANN and
then compared the best models in each category. The same validation data through forecasting measures (R2,
RMSE, and MAPE) are used to evaluate and compare the proposed models. In order to obtain experimental
wind speed forecasting results MATLAB is utilized in developing various soft computing models.
4.1. Evaluation of the models and results
In order to compare the performance of models in terms of forecasting accuracy, both algorithms for every
height were performed and the forecasting results are tabulated in Tables 1 through 6. In the ANFIS results,
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KORKMAZ et al./Turk J Elec Eng & Comp Sci
when 3 input membership functions are chosen for all models, the prediction values are less accurate. The best
predicted wind speed values are found when the MF number equals 2. For predicted wind speed values at 30
m, the 4 input model gives the fewest errors and higher R2values except MAPE. The lowest RMSE obtained
is 0.5942 m/s and the highest R2values obtained is 0.9753 m/s. At 50 m, Model 3 gives better wind speed
prediction results, both in the test period and the validation period. Table 3 shows that estimated values are
more accurate for Model 1 and Model 4. At 60 m, wind speed values are more irregular because the wind speed
changes sharply at this height. Thus, when the number of input models increase, poor forecasted results are
observed, and so Model 1 gives a better MAPE. However, during the testing period Model 3 would produce
better results in terms of R2and RMSE values. In order to demonstrate performance of the best models,
Figure 3 is shown to analyze best-tting lines for predicted and actual wind speed at 30 m, 50 m, and 60 m,
respectively. In addition, Figure 4 demonstrates that the best models predict wind speed with perfect accuracy
at all heights.
Table 1. Wind speed forecasting errors for 30 m.
No. Inputs Output Test Validation
MF no. Epoch no. MF type R2RMSE MAPE R2RMSE MAPE
1 2 10 Linear 0.9749 0.5992 8.9115 0.9791 0.6715 9.3041
2 2 100 Linear 0.9739 0.6103 8.9902 0.9789 0.6745 10.145
3 2 100 Constant 0.9752 0.595 8.972 0.9791 0.6715 10.238
4 2 100 Constant 0.9753 0.5942 8.945 0.9792 0.6702 10.648
Table 2. Wind speed forecasting errors for 50 m.
No. Inputs Output Test Validation
MF no. Epoch no. MF type R2RMSE MAPE R2RMSE MAPE
1 2 10 Linear 0.9762 0.61 8.0197 0.9808 0.6838 8.9543
2 2 10 Linear 0.9739 0.6387 8.1602 0.9808 0.6836 10.3151
3 2 100 Constant 0.9766 0.6052 8.1222 0.9848 0.6846 10.309
4 2 100 Constant 0.9765 0.6094 8.1273 0.9809 0.6833 10.3887
Table 3. Wind speed forecasting errors for 60 m.
No. Inputs Output Test Validation
MF no. Epoch no. MF type R2RMSE MAPE R2RMSE MAPE
1 2 10 Linear 0.9779 0.6125 8.0716 0.9821 0.69 9.22
2 2 100 Constant 0.9778 0.6139 8.4279 0.9816 0.7044 11.649
3 2 100 Constant 0.9781 0.6096 8.1991 0.9816 0.7011 10.436
4 2 100 Constant 0.9199 1.1721 17.015 0.928 1.3866 19.424
The wind speed was forecasted by dierent MLP models. As can be seen from Tables 4 and 5, Model
4 performs better than the other models. The amount of input wind data signicantly aects wind speed
estimation. According to the 30 m and 50 m values, input model 4 performs much better than other models
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KORKMAZ et al./Turk J Elec Eng & Comp Sci
0 5 10 15 20 25 30
Predicted Value
0
5
10
15
20
25
30
Actual Value
0 5 10 15 20 25 30
Predicted Value
0
5
10
15
20
25
30
Actual Value
0 5 10 15 20 25 30
Predicted Value
c) 60 meters
a) 30 meters
b) 50 meters
0
5
10
15
20
25
30
Actual Value
Figure 3. Correlation between actual and predicted values for the ANFIS testing stage.
with 8.8431% and 8.042% MAPE. The results in Table 6 demonstrate that the accuracy improves when the
input number of models is increased. Figure 5 shows best-tting lines for predicted and actual wind speed at
30 m, 50 m, and 60 m, respectively. The predicted and actual values are almost the same as shown in Figure
6. Figure 6 demonstrates that the best models predict wind speed with perfect accuracy at all heights.
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KORKMAZ et al./Turk J Elec Eng & Comp Sci
0 5000 10000 15000
Time (min)
0
5
10
15
20
25
Wind Speed (m/s)
Predicted Value
Actual Value
c) 60 meters
a) 30 meters b) 50 meters
0 5000 10000 15000
Time (min)
0
5
10
15
20
25
Wind Speed (m/s)
Predicted Value
Actual Value
0 5000 10000 15000
Time (min)
0
5
10
15
20
25
Wind Speed (m/s)
Predicted Value
Actual Value
Figure 4. Comparison between prediction and actual results for ANFIS models.
4.2. Discussion
In the present study, it is noted that although the behavior of wind speed for all heights seems to be very similar,
the prediction of wind speed is substantially inuenced by changing collected data containing wind speed at
various heights. As seen in the forecasting wind speed results, it is not possible to say that a single model might
present good prediction results for all heights. Furthermore, a detailed experimental analysis was carried out
due to eects of the various internal and external parameters on the wind speed forecasting. Tables 1–3 show
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KORKMAZ et al./Turk J Elec Eng & Comp Sci
Table 4. Wind speed forecasting errors for 30 m.
No. Test Validation
R2RMSE MAPE R2RMSE MAPE
1 0.9773 0.6398 10.3683 0.9803 0.7709 18.0652
2 0.9543 0.8883 13.0279 0.9601 1.0407 18.9147
3 0.9357 1.0456 15.003 0.9435 1.2262 18.8005
4 0.975 0.597 8.8431 0.9792 0.6698 9.5798
Table 5. Wind speed forecasting errors for 50 m.
No. Test Validation
R2RMSE MAPE R2RMSE MAPE
1 0.9123 1.1713 18.0428 0.9222 1.3786 24.5106
2 0.9293 1.051 15.4362 0.9349 1.2211 20.4918
3 0.9507 0.8777 12.3616 0.9582 1.0107 15.6393
4 0.9763 0.6083 8.042 0.981 0.6809 9.3231
Table 6. Wind speed forecasting errors for 60 m.
No. Test Validation
R2RMSE MAPE R2RMSE MAPE
1 0.9773 0.6398 10.3683 0.9803 0.7709 18.0652
2 0.9543 0.8833 13.0279 0.9335 1.0407 18.9147
3 0.9357 1.0456 15.003 0.9435 1.2262 18.8004
4 0.9187 1.1805 16.9167 0.9271 1.3957 18.0814
that increasing the input number of ANFIS models does not always demonstrate better estimation errors for
all heights. In spite of that, for the best estimation results, the input number of wind speed data is usually
between 4 and 6 [15].
The constant type output MF usually shows better performance for all models. There is a certain rule
in the literature that increasing the epoch number of ANFIS models decreases the estimation errors. In the
current study, this is not obtained for every model because it depends on the output MF type. When output MF
decides linear function, better results can be seen with a lower number of epochs. However, it is also observed
that linear output MF function with big epoch numbers signicantly aects the calculation time of short-term
wind speed prediction. Thus, in order to obtain realistic wind speed predictions and propose reliable ANFIS
models, epoch number must be limited to the satised values especially for the linear MF output functions.
From the MLP-ANN prediction results, we observed that with respect to the amount of input wind
speed data MLP-ANN models illustrate the same patterns except at the height of 60 m. The 60 m wind
speed forecasting results show that various numbers of input wind speed data are required for all heights to
accomplish better prediction results. Furthermore, the height of the measured wind speed may signicantly
aect the results. For instance, while MLP-ANN gives sucient results for heights of 30 m and 50 m, it produces
very undesirable results when 60 m wind data are used for the MLP-ANN approach.
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0 5 10 15 20 25
Predicted Value
0
5
10
15
20
25
Actual Value
Outputs vs. Targets, R=0.98935
Data Points
Best Linear Fit
0 5 10 15 20 25 30
Predicted Value
0
5
10
15
20
25
30
Actual Value
Outputs vs. Targets, R=0.99046
Data Points
Best Linear Fit
0 5 10 15 20 25 30
Predicted Value
0
5
10
15
20
25
30
Actual Value
Outputs vs. Targets, R=0.98955
Data Points
Best Linear Fit
c) 60 meters
b) 50 metersa) 30 meters
Figure 5. Correlation between actual and predicted values for MLP neural network testing stage at a) 30 m, b) 50 m,
c) 60 m.
In the evaluation and testing stages, both methods are compared according to the above tables. Two
methods are compared and then the best models are determined for each selected height according to three
dierent evaluation metrics. The error values are compared in Table 7. In this table, only testing errors are
shown because the dierences between actual and last estimated wind speed values are shown at this stage.
The evaluation stage optimizes parameters of articial intelligence algorithms. As seen in Table 7, according to
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0 5000 10000 15000
Time (min)
0
5
10
15
20
25
Wind Speed (m/s)
Predicted Value
Actual Value
c) 60 meters
a) 30meters b) 50 meters
0 5000 10000 15000
Time (min)
0
5
10
15
20
25
Wind Speed (m/s)
Predicted Value
Actual Value
0 5000 10000 15000
Time (min)
0
5
10
15
20
25
Wind Speed (m/s)
Predicted Value
Actual Value
Figure 6. Comparison between prediction and actual results for MLP neural network models.
the results at heights of 30 and 50 m, MLP-ANN provides better forecasting results for only the MAPE metric.
Additionally, while MLP-ANN performs as the poorest model with above 10% error, the ANFIS approach gives
satisfactory results for all heights in terms of R2and RMSE values. Therefore, we can say that ANFIS models
perform with reliable accuracy for all heights.
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Table 7. Comparison of performances of methods.
Height Method Model no. Test stage
R2RMSE MAPE
30 m ANFIS 4 0.9753 0.5942 8.945
MLP 4 0.975 0.597 8.8431
50 m ANFIS 3 0.9766 0.6052 8.1222
MLP 4 0.9763 0.6083 8.042
60 m ANFIS 3 0.9781 0.6096 8.1991
MLP 1 0.9773 0.6398 10.3683
5. Conclusions
Wind power generation has grown considerably in recent years; however, it is still unreliable as a main grid
supplier because variability in wind speed dramatically aects the predicted wind energy. Since wind speed
at various heights exhibits very dierent behavior, forecasted wind speed may not be calculated accurately.
The primary signicance of this study is that we ll a research gap for short-term wind speed prediction at
dierent heights. Recent research has shown that the MLP-ANN and ANFIS methods can successfully predict
short-term wind speed variation. However, wind speed predictions can be done at dierent heights and thus
model performances may be dierent at various heights.
In light of the results and discussion presented so far in this paper, it is clear that MLP-ANN and
ANFIS based algorithms can be used to predict wind speed 10 min in advance. The forecasting models vary
by the number of wind data inputs. MLP-ANN and ANFIS give satisfactory results for short-term wind speed
prediction with approximately under 10% MAPE and 0.6398 m/s RMSE. In light of the results of the best
models, we see that no single model works best for all heights. Thus, it is dicult to say with certainty that
one model works better for every height. Moreover, at all heights, the ANFIS approach gives better prediction
results. The best ANFIS models for wind prediction show that 3 and 4 inputs give the most accurate prediction
results. The MLP-ANN algorithm with 3 and 4 input wind speed models shows better performance for 30 and
50 m; however, a 1 input wind speed model enables better forecasting results for 60 m.
In conclusion, for the rst time, dierent ANFIS and MLP-ANN models are proposed to understand
the behavior of wind speed and to obtain the most accurate speed forecast at dierent heights. For dierent
heights, the proposed model can be used to dene the best location of wind turbines and to forecast irregular
wind energy. Short-term wind energy forecasting can be improved by using these models to enhance wind power
quality.
Acknowledgments
This research is part of a master’s thesis “Wind Power Calculation by Using Forecasted Wind Speed with Soft
Computing Method at Dierent Heights”. Therefore, we would like to appreciate everyone who contributed to
the successful realization of this research.
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