METHOD OF ARTIFICIAL NEURAL NETWORKS WITH PARAMETER
ESTIMATING WIND SPEEDS BETWEEN CITIES IN TURKEY
Numan S. Çetin1, Cem Emeksiz2,
1
Ege University, Institute of Solar Energy 35100, Bornova / IZMIR / TURKEY
2
Gaziosmanpaşa University, Turhal Vocational High School 60300, Turhal/TOKAT/ TURKEY
E-mail: numan.sabit.cetin@ege.edu.tr, cem.emeksiz@gop.edu.tr
I.
ABSTRACT
Artificial neural networks, for solving problems which can not be expressed as a
complex and complete offers an alternative way . Neural networks learn from examples,
errors and incomplete of noisy data that are tolerated and can be used for solving nonlinear
problems. In this study, using the data of wind speeds received by the General Directorate of
State Meteorology Affairs, wind speed forecasting for 4 cities (Tokat, Amasya, Corum,
Yozgat) was performed. By using the learning algorithms (Back Propagation and
Levenberg-Marquardt) were performed for training. The data used for training in the year
2000 until 2005. Between cities wind speed forecasting was performed in 2005.
Keywords: Artificial Neural Networks, Renewable Energy, Estimating Wind Speed
II.
INTRODUCTION
Energy is a key factor in economic development and in providing vital services that
improve quality of life. Energy is required for meeting all of the basic needs such as food and
health, agriculture, education, information, and other infrastructure services and shows clear
correlation with the Human Development Index HDI figure 1 [1].
FIG. 1. HDI and commercial energy consumption
Even so unlimited use of fossil fuels (coal, oil, and natural gas) brings serious
influences to the environment and the future development of society. Due to increasing
exploitation, fossil fuels are becoming fewer and fewer [2]. In addition, emission of
greenhouse gases from fossil fuels causes global warming and declining air quality [3]. It is
estimated that 16 million tones of CO2 are emitted into the atmosphere every 24 h worldwide.
If all emissions were to stop today, the CO2 that has already been emitted will result in an
enhanced GHG effect for the next 50 years [4].
Boyle concludes that, renewable energy is a kind of energy which can be obtained
from the continuous or repetitive currents of energy recurring in the natural environment or
energy flows which are replenished at the same rate as they are “used” [5].Compared with
fossil fuels, renewable sources of energy are not substantially depleted by continued use and
have less pollutant emissions. Consequently, renewable energy sources are becoming more
and more attractive to all nations, especially for all countries.
Among renewable energy sources, wind energy is the one with the lowest cost of
electricity production [6], but is feasible only as long as weather conditions allow. Surrounded
by the Black Sea to the north, the Marmara and the Aegean Sea to the west and the
Mediterranean Sea to the south, Turkey has huge potential for wind power generation [7]. A
study carried out in 2002 concluded that Turkey has a theoretical wind energy potential of
nearly 90,000MW [8]. So far only about 1000MW capacity wind farms are in operation in
Turkey, generating less than 0.5% of total electricity consumed. There are a number of cities
in Turkey with relatively high wind speeds [9]. To maintain economical power expedition of
wind generated electricity, it is significancy to be able to make short term predictions of
future wind speeds, which directly affects generation capacity. Without this ability, a wind
farm operator is prone to allocate more generation units or supplemental energy reserves than
necessary in order to ensure budgeted electricity outputs are met [6], with an end result of
increased operating costs.
Generally, wind speed uses numerical weather prediction for longer time. For shorter
time prediction, only the historical data are used. The methods for shorter time prediction are:
Artificial Neural Network Model [10-11-12], Persistence Model[13-14-15], Lineer
Regression Model, Auto Regression and Moving Average Model [16-17], Auto Regression
Least Square Model, Self-Adapting Fuzzy Logic Model, Kalman Filtering Model [18], Fuzzy
Expert System [19] etc.
Several studies are presented to propose some models for wind speed prediction. In
an earlier study, Njau developed an electronic system for air temperature and wind speed
prediction and found good agreement between the predicted and actual values of both the
wind speed and temperature [20]. Rehman and Halawani used stochastic time series analysis
for the prediction of hourly wind speed for nine Saudi Arabian cities and found a relatively
good agreement between predicted and actual values [21]. Also, Mohandas and Halawani
introduced support vector machines (SVM) to wind speed prediction and compared their
performance with the multilayer perceptron (MLP) neural networks [22].
This paper reports an ANN model for wind speed prediction, which uses back
propagation algorithm and multi-layer feed forward type of network . In this paper, an ANN
modeled wind speed (WS) prediction program was developed to simulate wind speed of a city
using that of another cities, for each pair of cities and each combination between them.
III.
ARTIFICIAL NEURAL NETWORKS
The term artificial neural network (ANN) has been conducted right after the
recognition of the way the human brain computes. The human brain computes in an entirely
different way from the conventional computer, the brain is a highly complex, nonlinear and
parallel information processing system. A neural network is a machine that is designed to
model the way in which the brain performs a particular task. The network is implemented by
using electronic components or is simulated in software on a digital computer. A neural
network is a massively parallel distributed processor made up of simple processing units,
which has a natural propensity for storing experimental knowledge and making it available for
use.
A neuron is an information processing unit that is fundamental to the operation of a
neural network. The three basic elements of the neuron model are:
1. A set of weights, each of which is characterized by a strength of its own. A signal
“xj” connected to neuron “k” is multiplied by the weight “wkj”. The weight of an artificial
neuron may lie in a range that includes negative as well as positive values.
2. An adder for summing the input signals, weighted by the respective weights of the
neuron.
3. An activation function for limiting the amplitude of the output of a neuron. It is also
referred to as squashing function which squashes the amplitude range of the output signal to
some finite value figure 2.
FIG. 2. Model of ANN [23]
A neural network has 3 layers. The first layer is always the input layer and the last
layer is always the output layer. The layers placed between the first and the last layers are the
hidden layers.
In this study; multi-layer feed forward type of network is used. Feed forward type of
network where computations proceed along the forward direction only Figure 3. Multiple
layer feed forward network has been applied successfully to solve some difficult diverse
problems by training them in a supervised manner with a highly popular algorithm known as
the back-propagation algorithm. This algorithm is based on the error-correction learning rule.
Most important reason for this; the capacity of learning is high and algorithm is simple [25].
FIG. 3. Typical feed forward network [24]
IV.
MODEL OF ANN
In this study, we combined the wind speed data of four cities (Tokat, Amasya,
Corum,Yozgat). We have estimated wind speeds of the fourth city by using wind speeds of
the other three cities with the network that is created. Therefore four different types of
training was performed. These training processes are also performed on the same network.
V.
TRANING PROCEDURES
In this study, wind speed data of four cities (Tokat, Amasya, Corum, Yozgat),
collected by General Directorate of State Meteorology Affairs between 2000-2005, were used
for wind speed prediction using ANN.
The wind speed data were normalized between 0-1. Normalization procedure was
initiated before the start of the training of the selected data. Nonlinear approach that makes a
significant feature of ANN that the data kept in a normalization of the course. Method chosen
for the normalization of the data, it directly affects the performance of ANN. Because of
normalization, the input data can be transferred in the active region the function to be passed
to. Data normalization, the data provides cumulative totals constitute a negative block [26].
The network that is used for traning has an input layer with 3 neurons, a hidden layer
with 10 neurons and a output layer with one neuron figure 4.
FIG. 4. Neural Network Diagram
The data for 180 months during 2000-2004 were used for each training purpose and the 12
months’data from 2005 as testing data. The testing data were not used in training the neural
networks. The mean squared error (MSE) for these data was found for each traning. Mean
square error defined by Gauss often just "average error" is also. Gives the most accurate idea
about the degree of accuracy of measurements. The mean square error is the most commonly
used in criteria for the degree of accuracy. Because the squares of errors taken, the effect of
large error is more and small errors and large errors don’t deal equally.
VI.
RESULTS
The result of education, the info window in figures 5-6-7-8 Regression values (R) of
considering coefficients for four cities were evaluated. R determines the level of the
relationship between the desired output of the network output. Take the values of R close to 1
indicates that the accuracy of the relationship between the desired output of the network
output [27].
Regression graph of between the network output and desired output shown in figure 5
for Tokat city. As a result of the first training process graph obtained from the regression
analysis; R = 0.90223 for the test data, R = 0.9218 for the training data and R = 0.96712 for
the validation data were as. The mean-square error was calculated for Tokat city as 0.0164.
Regression graph of between the network output and desired output shown in figure 6
for Corum city. As a result of the first training process graph obtained from the regression
analysis; R = 0.95078 for the test data, R = 0.92821 for the training data and R = 0.96388 for
the validation data were as. The mean-square error was calculated for Corum city as 0.0487.
Regression graph of between the network output and desired output shown in Figure 7
for Yozgat city. As a result of the first training process graph obtained from the regression
analysis; R = 0.99999 for the test data, R = 0.90325 for the training data and R = 0.99873 for
the validation data were as. The mean-square error was calculated for Yozgat city as 0.0532.
Regression graph of between the network output and desired output shown in Figure 8
for Amasya city. As a result of the first training process graph obtained from the regression
analysis; R = 0.97425 for the test data, R = 0.92038 for the training data and R = 0.95347 for
the validation data were as. The mean-square error was calculated for Amasya city as 0.1025.
And also for graps that obtained for measured and estimated wind speeds of 4 cities were
shown in figures 9-10-11-12.
FIG. 5. Regression graphic for Tokat City
FIG. 6. Regression graphic for Corum City
FIG. 7. Regression graphic for Yozgat City
FIG. 8. Regression graphic for Amasya City
FIG. 9. Estimated and measured wind speeds of Tokat
FIG. 10. Estimated and measured wind speeds of Corum
FIG. 11. Estimated and measured wind speeds of Yozgat
FIG. 12. Estimated and measured wind speeds of Amasya
VII.
CONCLUSION
In this study, forecasting was performed by using wind speeds of 4 different cities ın
Turkey. Mean-square error was the lowest for wind speed prediction of Tokat city. Wind
speed data for 4 different cities in Turkey between 2000 and 2004 were used for training a
feed forward ANN using back propagation algorithm. Data for 12 months of the year 2005
were used to test the performance of the ANN system. The results obtained in this study show
that while articial neural network investigated have the potential for short term wind speed
prediction. The applicability of this technique to systems that have the potential for renewable
energy sources. Parameters necessary to calculate the annual energy retrieval of system can
easily be estimated with ANN. As a result, the system is reduced before the establishment of
the necessary financial resources allocated to the taping and shortens the process of goal
achievement. Future work forecasting by using different neural network is considered.
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