orta dönem yük tahmi̇ni̇ anali̇zi̇nde ysa yaklaşimlari

ORTA DÖNEM YÜK TAHMİNİ ANALİZİNDE YSA YAKLAŞIMLARI
Emrullah Aslankaya1, Ümmühan Başaran Filik2
Özet
Yük tahmini güç sistemlerinin güvenli işletimi ve elektrik endüstrisinin temelidir. Yük
tahmininin doğruluğu en düşük maliyetle işlemlerin yapılmasına imkan sağlar. Planlama için
gelecekteki değeri bilmek önemlidir. Birim yüklenme, ekonomik dağıtım, yakıt dağılımı,
birimlerin bakımı doğru tahmin yöntemleriyle verimli bir şekilde işletilebilir. Bu çalışmada,
Türkiye’de orta dönem yük tahmini analizi yapay sinir ağları (YSA) yaklaşımları kullanılarak
çalışılmıştır. İleri besleme geri yayılım ağı analizlerde kullanılmıştır. Üç farklı ağ modeli
uygulanarak sonuçları kare ortalama hata yöntemi kullanılarak karşılaştırılmıştır. Çalışmada
kullanılan veriler, Türkiye Elektrik İletim A.Ş.’den alınmıştır.
Anahtar Kelimeler: yük tahmini, yapay sinir ağları, MATLAB
ANN APPROACHES for MEDIUM -TERM LOAD FORECASTING
Abstract
Load forecasting is essential for the secure operation of power systems, and electric industry
in the deregulated ecenomy. Load forecasting accuracy can allow utilities to operate at least
cost. The main problem of the planning is the demand knowledge in the future. Basic
operating functions such as unit commitment, economic dispatch, and fuel scheduling and
unit maintenance can be performed efficiently with an accurate forecast. In this work,
medium-term load forecast is carried out in Turkey using artificial neural network (ANN)
approaches. Feed forward backpropagation network is used for analyzing. The identified
three different network model are compared in terms of mean square error values. The data
used in this study taken from Turkish Electricity Transmission Company (TEIAS).
Keywords: load forecasting, artificial neural networks, MATLAB
1
2
Elektrik Elektronik Müh., Anadolu Üniversitesi, [email protected]
Yrd. Doç. Dr., Anadolu Üniversitesi, [email protected]
ORTA DÖNEM YÜK TAHMİNİ ANALİZİNDE YSA YAKLAŞIMLARI
Giriş
The load forecasting classified into three types in the literature (i) short-term, (ii)
medium forecast, and (iii) long-term is extremely important problem for the electrical
industry. Short term forecast in interval ranging from one hour to one week a medium term
forecast week to a year, and long term forecast for long time horizons generally up to 20
years. Each time interval has a different operation to be relevant. Short-term load forecasting
used to real time operation and control of power systems. Medium-term is generally used for
maintenance and scheduling programs. To develop future generation, transmission, and
distribution facilities is used to improve long-term load forecasting.
There are lots of different methods are developed to solve this problem. The most
important studies are regression-based methods (Papalexopoulos AD, Hesterberg TC., 1989),
time-series approach (Hagan MT, Behr SM,1987), fuzzy logic (Kiartzis SJ, Bakirtzis AG.,
1988), wavelet transform (Yao SJ, Song YH, Zhang LZ, et al., 2000), particle swarm
optimization (El-Telbany M, El-Karmi F. ,2008), Fourier series approach (González-Romera
E, Jaramillo-Morán MA, Carmona-Fernández D., 2008), and support vector machines (Hong
WC., 2009). Recently an ANN technique became the most widely used approach Park (DC,
El-Sharkawi MA, Marks II RJ, et al., 1991), and mathematical models (Başaran Filik, Ü.,
Gerek, Ö. N., ve Kurban, M., 2011), self-recurrent wavelet neural network (Hamed C.,
Hamid S., Hamidreza Z., David W., Nima A.) are also used. In this work, feedforward back
propagation ANN approaches applied to hourly real-load data values of Turkey for mediumterm load forecasting.
This paper is organized as follows. Section 2 explains the ANN models and its
implementation procedure. Section 3 presents the application of ANN to a real load data.
Section 4 provides the conclusions of the paper.
Method
ANN has the ability to provide solutions too many problems which is a branch of
artificial intelligence. In general terms, neural network is a simulation system for the purpose
of fulfilling the brain's learning function. A neural network, consisting of processing units,
experiential information for accumulating the natural tendency, and of their intensive use
parallel is a distributed processor. This processor is similar to the brain in two ways (Haykin,
S., 1999).
1.
The information is obtained by a learning process by the network.
2.
The information that the processing unit is used to save power
interconnections.
An ANN consists of several process units running in parallel and the algorithm which
function, network structure performed by the connection weights.
The human brain defined learning, merging, because of the recent adaptation and
generalization capability highly complex, nonlinear and parallel information processing in a
distributed system. ANN according to the brain's information processing method, information
gathering after a learning process, the connection weights between processing units is to store
this information and a processor parallel structure with the ability to generalize and to
determine a set of outputs, which may correspond to an input set of basic objectives.
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The learning process achieve the desired goal includes providing the regeneration of
neural network learning algorithms. This neural network has now found a place in many
application areas with the result of the ability to solve complex problems (Haykin, S., 1999).
One of the most important functions of the neural network is training process. ANN
characteristics are altered by the application network model. Using examples about making
generalizations about events they carry out the learning process. These networks are first
trained then is tested as healthy working to be used safely. ANN can produce outputs samples
that it is not shown to him because of characterized in that for generalization.
Algorithms are used during training specifies how to use it to due to problems of
learning rules by neural network. ANN may also work with missing data after training the
case shown itself allows adaptation and learning new events.
These networks can work only with numeric expression. If the data are symbolic
expressions or image types need to be converted into a numerical representation. Enabling the
generalization ability of a network and can minimize the output error that optimal number of
processing units requires appropriate training time.
The network's generalization capability does not mean to obtain a maximum processing
unit. Number of network processing unit is greater than fair value and if too much repetition
is made for educational excessive compliance. In this case, the relationship between inputs
and outputs in the testing process, although very good, generalization of the results cannot be
good.
A feedforward multilayer neural network are referred to as the cradle. These networks
is referred to as the one-way transmission networks to mark the entrance to the exit from the
network.
In a feedforward ANN, processing units arranged in layers and outputs as input to the
next layer out of weights of the cells in a layer is issued. Input layer, which forwards the
information received from the external environment without modifying transmit to the
process units in the middle layer (Haykin, S., 1999).
Application and Results
In this study, hourly real-load values are used to forecast next month values. Turkey's
gross electricity consumption (production + import-Turkey's gross foreign sales) increased by
8.4% in 2010, it increased 9.4% to 230.3 billion kWh in 2011, and amounted to 210.4 billion
kWh. The net consumption in Turkey has been (internal consumption, excluding network
losses) 172 billion kWh in 2010 and 186 billion kWh in 2011 (10).
2002 - 2011 year of Turkey Electrical System peak power and energy demand is given
in Table 1. In 2010, peak demand is recorded as 33392 MW, the minimum load is recorded as
13513 MW. The ratio of the minimum load to maximum load is 40.5%. In 2011, peak
demand is recorded as 36122 MW, the minimum load is recorded as 14822 MW. In 2011, the
ratio of minimum load to the maximum load is 41% (10).
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Table1: 2002 - 2011 Year of Turkey Electrical System Peak Power and Energy Demand
Year
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
Peak Power
Demand
(MW)
21006
21729
23485
25174
27594
29249
30517
29870
33392
36122
Increase
(%)
7,1
3,4
8,1
7,2
9,6
6,0
4,3
-2,1
11,8
8,2
Energy
Demand(GWh)
Increase
(%)
132553
141151
150018
160794
174637
190000
198085
194079
210434
230306
4,5
6,5
6,3
7,2
8,6
8,8
4,3
-2,0
8,4
9,4
In this study, the ANN forecasting results compared to the real hourly load data. 9months loads for considered as an input data and the delayed one week target in 2002. 6377
data is used for input and target and 168 is taken as the output data.
The mean squared error (MSE) evaluate for prediction accuracy and testing the success
of applied method. If it is the actual observation for a time period t and Ft predicts for the
same period, then the error is defined as:
et = yt - Ft
(1)
The standard statistical error measures can be defined as
𝑀𝑆𝐸 = ∑
𝑒
(2)
Three different network models are determined for comparison and number of
neurons in the hidden layer (10,100, and 150 respectively). Comparison of the real hourly
data with the results obtained with 10 hidden layer with mean squared error is given in
Figure1.
Figure 1: Comparison of the real hourly data with the results obtained 10 hidden layer.
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Mean squared error value is 4.82%
Comparison of the real hourly data with the results obtained by 100 hidden layer with
mean squared error is given in Figure 2.
Figure 2: Comparison of the real hourly data with the results obtained 100 hidden layer.
Mean squared error = 5.13%
Comparison of the real hourly data with the results obtained by 150 hidden layer with
mean squared error is given in Figure 3.
Figure 3: Comparison of the real hourly data with the results obtained by 150 hidden layer.
Mean squared error = 10.61%
Conclusion
Accurate load forecasting is significant for the secure operation of power systems.
Medium-term load forecasting is mainly important for maintenance and scheduling programs.
In this study, medium term load forecasting is achieved by using real load data values. Feed
forward backpropagation network is used for analyzing. Three different network models are
determined for comparison and number of neurons in the hidden layer (10,100, and 150
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respectively). The best results is obtained with ten hidden layer system.
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