Forecasting total and industrial sector electricity demand based on genetic algorithm approach: Turkey case study

This study deals with estimation of the total and industrial sector electricity consumption based on genetic algorithm (GA) approach, and then proposes two scenarios to project future consumptions. Total electricity consumption is estimated based on gross national product (GNP), population, import and export figures of Turkey. Industrial sector electricity is calculated based on the GNP, import and export figures. Three forms of the genetic algorithm electricity demand (GAED) models for the total and two forms for the industrial electricity consumption are developed. The best‐fit GAED model in terms of total minimum relative average errors between observed and estimated values is selected for future demand estimation. ‘High‐ and low‐growth scenarios’ are proposed for predicting the future electricity consumption. Results showed that the GAED estimates the electricity demand in comparison with the other electricity demand projections. The GAED model plans electricity demand of Turkey until 2020. Copyright © 2005 John Wiley & Sons, Ltd.     

Prediction of net energy consumption based on economic indicators (GNP and GDP) in Turkey

The most important theme in this study is to obtain equations based on economic indicators (gross national product—GNP and gross domestic product—GDP) and population increase to predict the net energy consumption of Turkey using artificial neural networks (ANNs) in order to determine future level of the energy consumption and make correct investments in Turkey. In this study, three different models were used in order to train the ANN. In one of them (Model 1), energy indicators such as installed capacity, generation, energy import and energy export, in second (Model 2), GNP was used and in the third (Model 3), GDP was used as the input layer of the network. The net energy consumption (NEC) is in the output layer for all models. In order to train the neural network, economic and energy data for last 37 years (1968–2005) are used in network for all models. The aim of used different models is to demonstrate the effect of economic indicators on the estimation of NEC. The maximum mean absolute percentage error (MAPE) was found to be 2.322732, 1.110525 and 1.122048 for Models 1, 2 and 3, respectively. R² values were obtained as 0.999444, 0.999903 and 0.999903 for training data of Models 1, 2 and 3, respectively. The ANN approach shows greater accuracy for evaluating NEC based on economic indicators. Based on the outputs of the study, the ANN model can be used to estimate the NEC from the country's population and economic indicators with high confidence for planing future projections.     

Application of genetic algorithm (GA) technique on demand estimation of fossil fuels in Turkey

The main objective is to investigate Turkey's fossil fuels demand, projection and supplies by using the structure of the Turkish industry and economic conditions. This study develops scenarios to analyze fossil fuels consumption and makes future projections based on a genetic algorithm (GA). The models developed in the nonlinear form are applied to the coal, oil and natural gas demand of Turkey. Genetic algorithm demand estimation models (GA-DEM) are developed to estimate the future coal, oil and natural gas demand values based on population, gross national product, import and export figures. It may be concluded that the proposed models can be used as alternative solutions and estimation techniques for the future fossil fuel utilization values of any country. In the study, coal, oil and natural gas consumption of Turkey are projected. Turkish fossil fuel demand is increased dramatically. Especially, coal, oil and natural gas consumption values are estimated to increase almost 2.82, 1.73 and 4.83 times between 2000 and 2020. In the figures GA-DEM results are compared with World Energy Council Turkish National Committee (WECTNC) projections. The observed results indicate that WECTNC overestimates the fossil fuel consumptions.     

Forecasting annual gross electricity demand by artificial neural networks using predicted values of socio-economic indicators and climatic conditions: Case of Turkey

In this work, the annual gross electricity demand of Turkey was modeled by multiple linear regression and artificial neural networks as a function population, gross domestic product per capita, inflation percentage, unemployment percentage, average summer temperature and average winter temperature. Among these, the unemployment percentage and the average winter temperature were found to be insignificant to determine the demand for the years between 1975 and 2013. Next, the future values of the statistically significant variables were predicted by time series ANN models, and these were simulated in a multilayer perceptron ANN model to forecast the future annual electricity demand. The results were validated with a very high accuracy for the years that the electricity demand was known (2007–2013), and they were also superior to the official predictions (done by Ministry of Energy and Natural Resources of Turkey). The model was then used to forecast the annual gross electricity demand for the future years, and it was found that, the demand will be doubled reaching about 460 TW h in the year 2028. Finally, it was concluded that the approach applied in this work can easily be implemented for other countries to make accurate predictions for the future.     

Electric energy demands of Turkey in residential and industrial sectors

The main objective of the present study is to apply the artificial neural network (ANN) methodology, linear regression (LR) and nonlinear regression (NLR) models to estimate the electricity consumptions of the residential and industrial sectors in Turkey. Installed capacity, gross electricity generation, population and total subscribership were selected as independent variables. Two different scenarios (powerful and poor) were proposed for prediction of the future electricity consumption. Obtained results of the LR, NLR and ANN models were also compared with each other as well as the projection of the Ministry of Energy and Natural Resources (MENR) and the results in literature. Results of the comparison showed that the performance values of the ANN method are better than the performance values of the LR and NLR models. According to the poor scenario and ANN model, Turkey's residential and industrial sector electricity consumptions will increase to value of 140.64 TWh and 124.85 TWh by 2015, respectively.     

Estimating the Turkish residential–commercial energy output based on genetic algorithm (GA) approaches

The present study develops three forms of equations to better analyze energy use and make future projections based on genetic algorithm (GA) notion, and examines the effect of the design parameters on the energy utilization values. The models developed in the quadratic form are applied to Turkey, which is selected as an application country. Turkey's future residential energy output demand is estimated based on GDP, population, import, export, house production, cement production and basic house appliances consumption figures. Among these models, the so-called GA-RWTVR model, which uses residential housing production, house appliances sales of washing machine, television, vacuum cleaner and refrigerator as design parameters/indicators, was found to provide the best fit solution to the observed data. It may be concluded that the models proposed can be used as an alternative solution and estimation techniques to available estimation techniques in predicting the future energy utilization values of countries.     

Forecasting future oil demand in Iran using GSA (Gravitational Search Algorithm)

Growing energy demand of the world, made the major oil and gas exporting countries to have critical role in the energy supply. The geostrategic situation of Iran and its access to the huge hydrocarbon resources placed the country among important areas and resulted in the investment development of oil and gas industry.In this study, a novel approach for oil consumption modeling is presented. Three demand estimation models are developed to forecast oil consumption based on socio-economic indicators using GSA (Gravitational Search Algorithm). In first model (PGIE) oil consumption is estimated based on population, GDP, import and export. In second model (PGML) population, GDP, export minus import, and number of LDVs (light-duty vehicles) are used to forecast oil consumption and in third one (PGMH) population, GDP, export minus import, and number of HDVs (heavy-duty vehicles) are used to estimate oil consumption. Linear and non-linear forms of equations are developed for each model.In order to show the accuracy of the algorithm, a comparison is made with the GA (Genetic Algorithm) and PSO (Particle Swarm Optimization) estimation models which are developed for the same problem. Oil demand in Iran is forecasted up to year 2030.     

Projection of future transport energy demand of Thailand

The objective of this study is to project transport energy consumption in Thailand for the next 20 years. The study develops log-linear regression models and feed-forward neural network models, using the as independent variables national gross domestic product, population and the numbers of registered vehicles. The models are based on 20-year historical data between years 1989 and 2008, and are used to project the trends in future transport energy consumption for years 2010–2030. The final log-linear models include only gross domestic product, since all independent variables are highly correlated. It was found that the projection results of this study were in the range of 54.84–59.05 million tonnes of oil equivalent, 2.5 times the 2008 consumption. The projected demand is only 61–65% of that predicted in a previous study, which used the LEAP model. This major discrepancy in transport energy demand projections suggests that projects related to this key indicator should take into account alternative projections, because these numbers greatly affect plans, policies and budget allocation for national energy management.     

Use of artificial neural networks for transport energy demand modeling

The paper illustrates an artificial neural network (ANN) approach based on supervised neural networks for the transport energy demand forecasting using socio-economic and transport related indicators. The ANN transport energy demand model is developed. The actual forecast is obtained using a feed forward neural network, trained with back propagation algorithm. In order to investigate the influence of socio-economic indicators on the transport energy demand, the ANN is analyzed based on gross national product (GNP), population and the total annual average veh-km along with historical energy data available from 1970 to 2001. Comparing model predictions with energy data in testing period performs the model validation. The projections are made with two scenarios. It is obtained that the ANN reflects the fluctuation in historical data for both dependent and independent variables. The results obtained bear out the suitability of the adopted methodology for the transport energy-forecasting problem.     

Improvement of energy demand forecasts using swarm intelligence: The case of Turkey with projections to 2025

The energy supply and demand should be closely monitored and revised the forecasts to take account of the progress of liberalization, energy efficiency improvements, structural changes in industry and other major factors. Medium and long-term forecasting of energy demand, which is based on realistic indicators, is a prerequisite to become an industrialized country and to have high living standards. Energy planning is not possible without a reasonable knowledge of past and present energy consumption and likely future demands. Energy demand management activities should bring the demand and supply closer to a perceived optimum. Turkey's energy demand has grown rapidly almost every year and is expected to continue growing. However, the energy demand forecasts prepared by the Turkey Ministry of Energy and Natural Resources overestimate the demand. Recently many studies are performed by researchers to forecast the energy demand of Turkey. Particle swarm optimization (PSO) technique has never been used for such a study. In this study a model is proposed, using PSO-based energy demand forecasting (PSOEDF), to forecast the energy demand of Turkey more efficiently. Although there are other indicators as well, gross domestic product (GDP), population, import and export are used as basic energy indicators of energy demand. In order to show the accuracy of the algorithm, a comparison is made with the ant colony optimization (ACO) energy demand estimation model which is developed for the same problem.     

Modeling and prediction of Turkey’s electricity consumption using Artificial Neural Networks

Artificial Neural Networks are proposed to model and predict electricity consumption of Turkey. Multi layer perceptron with backpropagation training algorithm is used as the neural network topology. Tangent-sigmoid and pure-linear transfer functions are selected in the hidden and output layer processing elements, respectively. These input–output network models are a result of relationships that exist among electricity consumption and several other socioeconomic variables. Electricity consumption is modeled as a function of economic indicators such as population, gross national product, imports and exports. It is also modeled using export–import ratio and time input only. Performance comparison among different models is made based on absolute and percentage mean square error. Electricity consumption of Turkey is predicted until 2027 using data from 1975 to 2006 along with other economic indicators. The results show that electricity consumption can be modeled using Artificial Neural Networks, and the models can be used to predict future electricity consumption.     

Situation of Turkey's energy indicators among the EU member states

As a candidate country for EU accession, Turkey should make significant future plans about strategy of consumption and production of basic energy sources. The main goal of this study is determination of energy indicators situation for Turkey—to allow us to draw up a good energy policy for the future using the method of multiple variables data analysis. Basic energy and economic indicators, such as gross generation, installed capacity, net energy consumption per person, import, export, consumption of coal, lignite, fuel oil, natural gas and hydroelectricity are used in the analysis. Energy indicators used in the analysis are taken from the EUROSTAT and Turkish Statistical Institute (TURKSTAT). Results of analysis show that Turkey's most important goal for the future is to produce proper energy policies.     

Energy demand estimation of South Korea using artificial neural network

Because South Korea's industries depend heavily on imported energy sources (fifth largest importer of oil and second largest importer of liquefied natural gas in the world), the accurate estimating of its energy demand is critical in energy policy-making. This research proposes an artificial neural network model (a structure with feed-forward multilayer perceptron, error back-propagation algorithm, momentum process, and scaled data) to efficiently estimate the energy demand for South Korea. The model has four independent variables, such as gross domestic product (GDP), population, import, and export amounts. The data are obtained from diverse local and international sources. The proposed model better estimated energy demand than a linear regression model (a structure with multiple linear variables and least square method) or an exponential model (a structure with mixed integer variables, branch and bound method, and Broyden–Fletcher–Goldfarb–Shanno (BFGS) method) in terms of root mean squared error (RMSE). The model also forecasted better than the other two models in terms of RMSE without any over-fitting problem. Further testing with four scenarios based upon reliable source data showed unanticipated results. Instead of growing permanently, the energy demands peaked at certain points, and then decreased gradually. This trend is quite different from the results by regression or exponential model.     

Future projection of the energy dependency of Turkey using artificial neural network

Energy dependency (ED) implies the extent to which an economy relies upon imports in order to meet its energy needs. The ED is calculated as net imports divided by the sum of gross inland energy consumption plus bunkers. This study aims at obtaining numerical equations to estimate of Turkey's energy dependency based on basic energy indicators and sectoral energy consumption by using artificial neural network (ANN) technique. It seeks to contribute to the strategies necessary to preserve the supply–demand balance of Turkey. For this purpose, two different models were used to train the ANN approach. In Model 1, main energy indicators such as total production of primary energy per capita, total gross electricity generation per capita and final energy consumption per capita were used in the input layer of the ANN while sectoral energy consumption per capita was used in Model 2.The ED was in the output layer for both models. Different models were employed to estimate the ED with a high confidence for future projections. The R² values of ED were found to be 0.999 for both models. In accordance with the analysis results, ED is expected to increase from 72% to 82% within 14 years of period. Consequently, the utilization of renewable energy sources and nuclear energy is strictly recommended to ensure the ED stability in Turkey.     

Turkey’s net energy consumption

The main goal of this study is to develop the equations for forecasting net energy consumption (NEC) using an artificial neural-network (ANN) technique in order to determine the future level of energy consumption in Turkey. In this study, two different models were used in order to train the neural network. 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). The net energy consumption is in the output layer for two models. Data from 1975 to 2003 are used for the training. Three years (1981, 1994 and 2003) are used only as test data to confirm this method. The statistical coefficients of multiple determinations (R²-value) for training data are equal to 0.99944 and 0.99913 for Models 1 and 2, respectively. Similarly, R² values for testing data are equal to 0.997386 and 0.999558 for Models 1 and 2, respectively. According to the results, the net energy consumption using the ANN technique has been predicted with acceptable accuracy. Apart from reducing the whole time required, with the ANN approach, it is possible to find solutions that make energy applications more viable and thus more attractive to potential users. It is also expected that this study will be helpful in developing highly applicable energy policies.     

Ant colony optimization approach to estimate energy demand of Turkey

This paper attempts to shed light on the determinants of energy demand in Turkey. Energy demand model is first proposed using the ant colony optimization (ACO) approach. It is multi-agent systems in which the behavior of each ant is inspired by the foraging behavior of real ants to solve optimization problem. ACO energy demand estimation (ACOEDE) model is developed using population, gross domestic product (GDP), import and export. All equations proposed here are linear and quadratic. Quadratic_ACOEDE provided better-fit solution due to fluctuations of the economic indicators. The ACOEDE model plans the energy demand of Turkey until 2025 according to three scenarios. The relative estimation errors of the ACOEDE model are the lowest when they are compared with the Ministry of Energy and Natural Resources (MENR) projection.     

Forecasting of Turkey's net electricity energy consumption on sectoral bases

In this study forecast of Turkey's net electricity energy consumption on sectoral basis until 2020 is explored. Artificial neural networks (ANN) is preferred as forecasting tool. The reasons behind choosing ANN are the ability of ANN to forecast future values of more than one variable at the same time and to model the nonlinear relation in the data structure. Founded forecast results by ANN are compared with official forecasts.     

Forecasting Net Energy Consumption Using Artificial Neural Network

The main goal of this study is to develop the equations for forecasting net energy consumption (NEC) using the artificial neural network (ANN) technique in order to determine the future level of the energy consumption in Turkey. Logistic sigmoid transfer function was used in the network. In order to train the neural network, population, and gross generation, installed capacity and years is used in input layer of network. The net energy consumption is in output layer. The input values in 1965, 1981, and 1997 are only used as test data to confirm this method. The statistical coefficient of multiple determinations (R ²-value) is equal to 0.9999 and 1 for training and test data, respectively. According to the results, the NEC using the ANN technique has been obviously predicted within acceptable errors. Apart from reducing the whole time required, the importance of the ANN approach is possible to find solutions that make energy applications more viable and thus more attractive to potential users. It is also expected that this study will be helpful in developing highly applicable and productive planning for energy policies.     

Energy usage and cost in textile industry: A case study for Turkey

The Turkish textile industry holds a relatively important position in the world and thus plays a major role in Turkey's exports. Energy consumption is important for the textile industry in Turkey because it is the largest export sector. Energy usage in the textile industry in Turkey is inefficient, and energy consumption has been growing very rapidly due to population growth, rapid urbanization and industrial development. For future planning, it is important to know the current specific energy consumption (energy consumption/production) and the energy intensity (energy consumption/cost of energy) in order to estimate future energy consumption for the textile sector. In this study, a survey has been carried out to show energy consumption, energy cost and the relationship between the energy usage and textile production. The results of the energy survey have been presented in both figure and table form.     

An econometric study on long-term energy outlook and the implications of renewable energy utilization in Malaysia

We developed a comprehensive econometric model to study the long-term outlook of Malaysia's economy, energy and environment to 2030. Our projections under the reference scenario indicated that Malaysia's gross domestic production (GDP) is expected to average 4.6% from 2004 to 2030, and total primary energy consumption will triple by 2030. Coal import will increase following governmental policy of intensifying its use for power generation. Oil import is predicted to take place by 2013 and reach 45 Mtoe in 2030. Hence, in the near future, Malaysia's energy import dependency will rise. Carbon emissions will triple by 2030. On the other hand, our projections under an alternative renewable energy (RE) scenario showed that the utilization of RE is a strategic option to improve the long-term energy security and environmental performance of Malaysia. However, substantial governmental involvements and support, as well as the establishment of a regulatory framework are necessary.