orta dönem yük tahmi̇ni̇ anali̇zi̇nde ysa yaklaşimlari
Transkript
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, easlankaya@anadolu.edu.tr Yrd. Doç. Dr., Anadolu Üniversitesi, ubasaran@anadolu.edu.tr 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. 173 EJOIR – ARALIK 2015 IWCEA ÖZEL SAYISI CİLT 2 ORTA DÖNEM YÜK TAHMİNİ ANALİZİNDE YSA YAKLAŞIMLARI 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). 174 EJOIR – ARALIK 2015 IWCEA ÖZEL SAYISI CİLT 2 ORTA DÖNEM YÜK TAHMİNİ ANALİZİNDE YSA YAKLAŞIMLARI 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. 175 EJOIR – ARALIK 2015 IWCEA ÖZEL SAYISI CİLT 2 ORTA DÖNEM YÜK TAHMİNİ ANALİZİNDE YSA YAKLAŞIMLARI 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 150 176 EJOIR – ARALIK 2015 IWCEA ÖZEL SAYISI CİLT 2 ORTA DÖNEM YÜK TAHMİNİ ANALİZİNDE YSA YAKLAŞIMLARI respectively). The best results is obtained with ten hidden layer system. References Başaran Filik, Ü., Gerek, Ö. N., ve Kurban, M. (2011). 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