Long-Term Load Forecasting on the Java-Madura-Bali Electricity System Using Artificial Neural Network Method

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1 Long-Term Load Forecasting on the Java-Madura-Bali Electricity System Using Artificial Neural Network Method Arief Heru Kuncoro 1,2, Zuhal 1, Rinaldy Dalimi 1 1 Department of Electrical Engineering, University of Indonesia, Depok 2 National Nuclear Energy Agency, BATAN, Jakarta Abstract LONG-TERM LOAD FORECASTING ON THE JAVA-MADURA-BALI ELECTRICITY SYSTEM USING ARTIFICIAL NEURAL NETWORK METHOD. A long-term forecasting of electric power peak load on the Java-Bali electricity system using Artificial Neural Network (ANN) method has been researched. Result has been compared with the forecasting from National Electricity General Plan (NEGP), with the study period of ANN is a part of Artificial Intelligence (AI) that promises new generation of information-processing systems that demonstrate the ability to learn, recall, and generalize from training patterns or data. The ANN model used in this research is back propagation (BP). The research uses MATLAB version R2006b, and the steps of ANN methodology applied are as follow: assembling the training set data (TSD), creating the network object, training the network using historical data, simulating the network response to new inputs, and finally resulting in the output of forecasting. TSD consists of 2 data types, i.e.: input data (consist of: Gross Regional Domestic Product (GRDP), population, number of households, total electric energy demand, electricity consumption on households, electricity consumption on commercial, electricity consumption on public, electricity consumption on industry, electric energy on the Java-Bali system, & electrification ratio), and output target data (consist of: historical peak load data). The result of peak load forecasting using ANN method is reasonable and considered good enough, since the electricity utility will accept error until 10% in long term forecasting. Based on ANN approach, the Java-Bali system s peak load of the years 2007, 2010, 2015, 2020 and 2025 are predicted to be MW, MW, MW, MW and MW respectively, meanwhile according to NEGP are MW, MW, MW, MW and MW respectively. Keywords Load forecasting, ANN, NEGP. 1. Introduction The Republic of Indonesia is located in Southeast Asia on an archipelago of more than 17,000 islands astride the equator. Indonesia s population is the fourth largest in the world and more than 60% living in the islands of Java, Madura and Bali (that three islands have only around 6.89% of total land area). Before the crisis, electricity demand in Indonesia was very high due to a National Economic Development Plan based on industrialization and supported by a strong agriculture base. It should be noted that in the twenty-five years prior to the crisis, the annual electricity growth rate averaged around 15% per annum. Due to the economic crisis, electricity demand has declined notably. However, in the early 2000s the Indonesian economy is set to return to an expansionary path again and energy and electricity demands are projected to grow substantially over the coming decade. At the end of December 2005, PT. PLN (Persero) and Subsidiary owned and operated about 5,210 generating units with total capacity of 22, MW, of which 16, MW (72,64%) was installed in Java. The system peak load for Indonesia for the calendar year 2005 was 19,263 MW, increased by 1.94% over the previous year. The peak load for the Java-Madura-Bali system was 14,821 MW (this was up 2.92% over the previous year)[1]. According to Minister of Energy and Mineral Resources, in year 2004, to push economic growth s 5%, is needed in order to reach the growth of electricity supply s 9%. Therefore, for the future, the supply growth of electricity should be raced until % per-year to reach economic growth to be 6.5-8% per-year (such as targeted by Mr. President)[2]. Every country or electric utility must know the accurate amount of required power in order to prepare power supply capability for maximum electric load demand up to that period. 177

2 The electric loads consist of many complex factors having nonlinear characteristics, and good results may not be obtained using the traditional methods. Therefore, a better method of forecasting would be one that could find nonlinear relations between load and various economic and other factors and adaptable to changes. A methodology that best suits these requirements is the application of artificial neural networks. The Artificial Neural Network (ANN), particularly feed-forward and feed-backward back-propagation methods, were reported to be proper for load forecasting[3]. Since the period of long-term load forecasting is different from country to country and even from company to company, comparison among them become difficult. As they have different characteristics, therefore each load forecasting approach becomes valid only for that country, particular region or company. However, this paper presents the research of electric power peak load forecasting in the Java-Madura-Bali system by using ANN method, with the study period is Methodology This research used the Artificial Neural Network (ANN) method to peak load forecasting on the Java-Madura-Bali electricity system. Artificial Neural Network (ANN) is a part of Artificial Intelligence (AI), that s promising new generation of informationprocessing systems that demonstrate the ability to learn, recall, and generalize from training patterns or data. ANN has certain performance characteristics in common with biological neural networks. A neural net consists of a large number of simple processing elements called neurons, units, cells, or nodes. Each neuron is connected to other neurons by means of directed communication links, each with an associated weight. The weights represent information being used by the net to solve a problem. Each neuron has the activation, which is a function of the inputs it has received. A neuron sends its activation as a signal to several other neurons [4,5]. ANN model that s used in this research is back propagation (BP). The BP learning algorithm is one of the most important historical developments in neural networks (see Figure 1). Figure 1. Flow cart of ANN back propagation training algorithm. Note: x i input vectors for training. v ij weight of connection between node i to j. z_in j output vectors of hidden unit before f activation function. z j output vectors of hidden unit after w jk weight of connection between node j to k. y_in k output vectors of training before y k output vectors of training after t k target output vectors (reference). δ error vectors. α training rate constant. 178

3 Δ weight correction q iteration of q th. This learning algorithm is applied to multilayer feed-forward networks consisting of processing elements with continuous differentiable activation functions. Given a training set of input-output pairs {(x, t)}, the algorithm provides a procedure for changing the weights in a BP network to classify the given input pattern correctly. The basis for this weight update algorithm is simply the gradientdescent method as used for simple perceptrons with differentiable units. For a given inputoutput pairs {(x, t)}, the BP algorithm performs two phases of data flow. First, the input pattern x is propagated from the input layer to the output layer, and as a result of this forward flow of data, it produces an actual output y. Then the error signals resulting from the difference between t and y are back-propagated from the output layer to the previous layer for them to update their weights [6]. This algorithm adopts the incremental approach in updating the weight, that is, the weights are changed immediately after training pattern is presented. The training process will be terminated until the target error is reached. In this research, we use the steps for ANN simulation using MATLAB7 to the longterm peak load forecasting (see Figure 2), i.e.: a. Assemble the Training Set Data (TSD)[1,7,8,9,10]. TSD consists of 2 data types: Input and Output data. Input data (consists of 10 parameters thought to influence the load forecasting), i.e.: - Gross Regional Domestic Product (GRDP) - Population - Number of households - Total electric energy demand - Electricity consumption on households - Electricity consumption on commercial - Electricity consumption on public - Electricity consumption on industry - Electric energy on Java-Bali system - Electrification Ratio Output target data, i.e.: historical peak load data. b. Create the network object. ANN is designed with 4 layers, which the first layer, second layer, third layer and fourth layer consist of 10, 15, 7 and 1 neuron(s), respectively (Figure 2). c. Train the network. d. Simulate the network response to new inputs, which the new inputs are projection data of 10 important factors that influence the long-term forecasting from many sources ( )[8,9,10,11]. e. Output results of forecasting ( ). Figure 2. Design of ANN for long term peak load forecasting. In the process simulation, ANN is always changes weight value, until maximum epoch is reached or mean square error (MSE) is equal/less than When the maximum epoch is reached, the value of MSE is about 8, (Figure 3). Figure 3. Performance MSE illustrasion of ANN training proccess. 3. Results 179

4 The research results of long-term peak load forecasting which conducted for year can be seen at Table 1, Table 2 and Figure 4. In the tables and figure are presented forecasting results by using ANN method and NEGP forecasting, and also the actual data of peak load from PLN Statistics, PT. PLN (Persero). Table 1. Peak load forecasting in the Java-Madura-Bali electricity system. Year ANN Actual Data NEGP 112] Based on the ANN calculation, the peak load forecasting will increase from 15,396 MW (2006), becoming 57,030 MW (2025), so the power increase is about 41,634 MW. By consideration of actual load data in year 2006, the calculation result of mean annual load growth rate is about 7.1% (during period of study). Meanwhile, according to NEGP projection, the peak load forecasting will increase from 15,886 MW (2006), becoming 59,107 MW (2025), so the power increase is about 43,711 MW, and the annual average load growth rate is about 7.3% (during the period of study). Peak Load (MW) NEGP ANN Figure 4. Peak load forecasting in the Java- Madura-Bali electricity system. From ANN study result can be known that annual peak load growth rate will increase about 6.4% for the period of ; 7.4% for period of ; 7.4% for period of ; and then 7.2% for period of (Table 2). Table 2. Annual load mean growth rate in the Java-Madura-Bali system. Year ANN Metode Power increase (MW) Annual growth rate Power increase (MW) NEGP Year Annual growth rate ,4% ,3% ,4% ,6% ,4% ,1% ,2% ,6% * % ,3% Meanwhile, according to NEGP forecasting, the annual peak load growth rate will increase about 8.3% for the period of ; 7.6% for period of ; 7.1% for period of ; and then 6,6% for period of (Table 2). 4. Discussion Forecasting of electric power peak load in the Java-Madura-Bali electricity system by using ANN method is influenced by the historical data of demographic, economic, electricity consumption, and electrification ratio. That historical data is used for ANN training. The annual electricity growth rate before crisis averaged around 15% per annum. Due to the crisis, electricity demand has declined. However, in 180

5 the early 2000s the Indonesia economy is set to return to an expansionary path again and electricity demand is projected to grow substantially over the coming decade. By consideration of the forecasting results in Table 1, Table 2 and Figure 3, we know that economic growth in the Java-Madura-Bali islands will increase with annual electricity growth rate averaged around 7.1% (ANN) 7.3% (NEGP) during the period of study ( ). For the next research, the load forecasting results of ANN will inputed into the Loadsy Module of Wien Automatic System Planning Package (WASP) for optimization of the expansion planning for Java-Madura-Bali electrical generating system with nuclear power option. Program package of WASP is developed by Tennessee Valley Authority Oak Ridge National Laboratory and International Atomic Energy Agency (IAEA) for the planning of electricity system development. 5. Conclusion Results of peak load forecasting by using ANN method and NEGP projection are reasonable and good enough, because in general, the electricity utility will accept error until 10% to long term forecasting[3]. Results of ANN approach and NEGP projection for the period of study , show that accelerate increase of annual average load growth rate s around 7.1% (ANN) - 7.3% (NEGP). This is a good situation reflection of Java-Madura_Bali region economic growth. Based on ANN approached, the Java- Madura-Bali system s peak load of years 2007, 2010, 2015, 2020 and 2025 are predicted to be MW, MW, MW, MW and MW, respectively. Meanwhile, according to NEGP, the peak load of years 2007, 2010, 2015, 2020 and 2025 are predicted to be MW, MW, MW, MW and MW, respectively. Considering with results which obtained in this research as well as consideration of pickings of research researchers before all, we expect to get a more accurate and better estimate forecast for the future. References , PLN Statistics 2005, PT. PLN (Persero), Jakarta, , Interkoneksi Jaringan Listrik Jawa- Sumatera Selesai 2007, Kompas, Jakarta, 28 Pebruari H. Iwamiya, and B. Kermanshahi, Long-term Load Forecasting using Neural Nets, Department of Electronics & Information Engineering, Tokyo University of Agriculture and Technology, Japan. 4. L.V. Fausett, Fundamentals of Neural Networks: Architectures, Algorithms, and Applications, Prentice-Hall, S. Kusumadewi, Artificial Intelligence (Teknik dan Aplikasinya), Graha Ilmu,Yogyakarta, C.T. Lin and C.S.G. Lee, Neural Fuzzy Systems, A Neuro-Fuzzy Synergism to Intelligent Systems, Prentice-Hall, 1994, ch. 9-10, pp , Statistik PLN 2001 sampai dengan 2004, PT. PLN (Persero), Jakarta, , Buku Pegangan Statistik Ekonomi Energi Indonesia 2002, Pusat Informasi Energi, Departemen Energi dan Sumber Daya Mineral (DESDM), EAPO, Jakarta, , Laporan Perekonomian Indonesia 2002, Biro Pusat Statistik (BPS), Jakarta, Indonesia, , Comprehensive Assessment of Different Energy Sources for Electricity Generation in Indonesia, Indonesia s Team and International Atomic Energy Agency (IAEA), , Prakiraan Energi Indonesia 2010, Indonesia s Energy Outlook 2010, Pusat Informasi Energi, Departemen Energi dan Sumber Daya Mineral (DESDM), EAPO, Jakarta, , Rencana Umum Ketenagalistrikan Nasional (RUKN), Departemen Energi dan Sumber Daya Mineral, Jakarta, 25 April