CommunicationEstimating the net electricity energy generation and demand using the ant colony optimization approach: Case of Turkey
Introduction
This paper sheds light on Turkey's net electricity energy generation and demand based on economic indicators. Electricity energy is a vital input for technical, social and economic development of the country. Therefore, the identification and analysis of energy issues and the development of energy policy options are of prime importance (Utlu and Hepbasli, 2006; Dincer and Dost, 1996). Turkey, which is a Eurasian country that stretches across the Anatolian peninsula in Southwestern Asia and the Balkan region of Southeastern Europe, is among upper middle income countries (World Bank). Turkey has been rapidly growing in terms of both its economy and its population (Hamzacebi, 2007). In parallel, its demand for energy, particularly for electricity, has been increasing fast. Electricity energy is a vital input for technical, social and economic development of Turkey as the other countries. According to the colleted data from the Ministry of Energy and Natural Resources (MENR) and Turkish Electricity Transmission Company (TETC), Table 1 shows that energy consumption in the 1990s increased about 4.4% per year, with electricity consumption growing at an average annual rate of about 8.5%.
According to the colleted data from MENR, the Turkey's electricity energy generation for 2006 is 1,76,300 GWh when its electricity energy import and export are 573 and 2236 GWh, respectively. On the other hand, Turkey's electricity energy demand for 2006 is 1,74,637 GWh.
In the future, a very large growth in electricity energy generation and demand in Turkey is expected. Table 2 shows Turkey's annual development of installed capacity and generation for the period 1970–2006 according to data collected from TETC in detail.
Some researchers have studied recently on forecasting of Turkey's electricity energy demand. Ediger and Tatlidil (2002) forecasted the primary energy demand and analyzed the cyclic patterns. By using the cycle analysis method, they proposed that the energy demand will be around 130 million toes in 2010. Ozturk et al. (2004) estimated energy demand and electricity consumption using genetic algorithms. Developed models using stochastic search processes based on genetic algorithms are validated with actual data, while future estimation of electricity demand is projected between 2002 and 2025. Yumurtaci and Asmaz (2004) forecasted the energy demand until 2050 and calculated which percentage of the required energy can be produced by hydraulic and thermal power stations. They presented that the energy use projection of Turkey for the period of 1980–2050 is calculated based on the population increase and energy consumption increase rates per capita. Furthermore, their study includes the evaluation of energy requirement in the year 2050 in the case of whole hydro-energy potential usage. Hamzacebi and Kutay (2004) utilized regression, Box–Jenkins and artificial neural networks (ANN) models for forecasting net electricity energy consumption until 2010.
Sozen et al. (2005) developed two different models for forecasting net energy consumption using by ANN. In one of them, population, gross generation, installed capacity and years are used in the input layer of the network (Model 1). Other energy sources are used in input layer of network (Model 2). Madlaner et al. (2005) have applied a dynamic technology adoption model for the evaluation of irreversible investment options for electricity generating technologies in the Turkish power supply industry, taking into account uncertainty, vintage-specific life-cycle capital and operation costs. Tunc et al. (2006) predicted Turkey's electricity energy consumption rates with regression analysis for the years 2010 and 2020 and, developed a linear mathematical optimization model to predict the distribution of future electrical power supply investments in Turkey. Yuksek et al. (2006) investigated the role of Turkey's hydro-electric power in meeting the long-term electricity energy demand. They obtained that Turkey's hydro-electric potential can meet 33–46% of its electric energy demand in 2020 and this potential may easily and economically be developed (Salvarli, in press) . Hamzacebi (2007) estimated Turkey's net electricity energy consumption on sectoral basis until 2020 is explored. Artificial neural networks is preferred as forecasting tool (Illustration 1).
In this paper, models obtained by using the ant colony optimization (ACO)-based algorithm were being suggested to forecast electricity energy generation and demand. The estimation of electricity energy generation and demand based on economic indicators was to be modeled by using various forms of equations. These forms are linear and quadratic. The economic indicators that were used during the model development are GDP, population and import and export data.
The paper is organized as follows. First, ACO will be explained shortly. The proposed ACO algorithm to estimate electricity energy generation and demand will be detailed in Section 3. In Section 4, models for Turkey's electricity energy generation and demand are developed by using algorithm proposed in Section 3. Finally, proposed models will be discussed Turkey's electricity energy generation and demand in the years 2006–2025 based three different scenarios.
Section snippets
Ant colony optimization
ACO belongs to the class of biologically inspired heuristics. The basic idea of ACO is to imitate the cooperative behavior of ant colonies. ACO for solving combinatorial optimization problems was initiated by Dorigo (1992). ****The principle of these methods is based on the way ants search for food and finds their way back to the nest. During trips of ants a chemical trail called pheromone is left on the ground. The role of pheromone is to guide the other ants towards the target point. By one
Estimating the net electricity energy generation and demand using the ant colony optimization approach
Ant colony optimization electricity energy generation and demand (ACOEEGE and ACOEEDE) were developed inspiring from ACOEDE algorithm used to estimate energy demand (Toksarı, 2007). The ACOEEE model is developed using population, GDP, import and export. The estimation with respect to electricity energy based on economic indicators was modeled by using various forms as linear and quadratic. For example, quadratic form can be expressed as
ACOEEE models for Turkey
ACOEEGE and ACOEEDE models are developed by using the ACO-based algorithm and observed data between 1979 and 2006. The data are collected from different sources, which are Turkish Statistical Institute (TSI), the MENR and TETC. When GDP, import, export and population are obtained from TSI and WECTNC, the observed data for electricity energy generation and demand are collected from MENR and TEWTC. Table 3 shows the observed data between 1979 and 2006.
Two models for both electricity energy
ACOEEE models for the future estimation of Turkey's electricity energy
The proposed ACOEEE models can be used to estimate the Turkey's future electricity generation and demand under different scenarios. So, three scenarios for estimating Turkey's electricity generation and demand in the years 2007–2025 were used.
Scenario 1: It is assumed that the average growth rate of GDP is 3.5%, population growth rate is 0.1%, import growth rate figure is 7%, and proportion of import covered by export is 50% during the period 2007–2025. When Illustration 4 presents that the
Conclusions
In this study, the estimation of Turkey's net electricity energy generation and demand using the ACO is studied based on the GDP, population, import and export. Two forms of the both ACOEEGE and ACOEEDE developed using twenty-eight data (1979–2006). Three scenarios are proposed to estimate Turkey's electricity energy generation and demand in the years 2007–2025 using ACOEEE models. In this study, the following main conclusions may be drawn:
- (a)
Linear_ACOEEGE and quadratic_ACOEEGE for three
Acknowledgements
The author is grateful for the support provided for the present work by the MENR, TETC and TSI.
References (22)
- et al.
Estimating energy demand of Turkey based on economic indicators using genetic algorithm approach
Energy Conversion and Management
(2004) - et al.
Energy intensities for Canada
Applied Energy
(1996) - et al.
Forecasting the primary energy demand in turkey and analysis of cyclic patterns
Energy Conversion and Management
(2002) Forecasting of Turkey's net electricity energy consumption on sectoral bases
Energy Policy
(2007)Some aspects on hydraulic energy and environment in Turkey
Energy Policy
(2006)- et al.
Turkey's net energy consumption
Applied Energy
(2005) - et al.
Parallel ant colonies for the quadratic assignment problem
Future Generation Computer Systems
(2001) Ant colony optimization for finding the global minimum
Applied Mathematics and Computation
(2006)- et al.
Comparison of Turkey's electrical energy consumption and production with some European countries and optimization of future electrical power supply investments in Turkey
Energy Policy
(2006) - et al.
Assessment of the energy utilization efficiency in the Turkish transportation sector between 2000 and 2020 using energy and energy analysis method
Energy Policy
(2006)
The role of hydropower in meeting Turkey's electric energy demand
Energy Policy
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