Electricity estimation using genetic algorithm approach: a case study of Turkey

This paper describes the use of stochastic search processes that are the basis of genetic algorithms (GAs), in developing Turkey's electric energy estimation. The industrial sector electricity consumptions and the totals are estimated, based on the basic indicators of the gross national product, population, import and export figures. Two different non-linear estimation models are developed using GA. Developed models are validated with actual data, while future estimation of electricity demand is projected between 2002 and 2025. It may be concluded that the both GAs can possibly be applied to estimate electric energy consumption.     

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.     

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.     

Industrial electricity demand for Turkey: A structural time series analysis

This research investigates the relationship between Turkish industrial electricity consumption, industrial value added and electricity prices in order to forecast future Turkish industrial electricity demand. To achieve this, an industrial electricity demand function for Turkey is estimated by applying the structural time series technique to annual data over the period 1960 to 2008. In addition to identifying the size and significance of the price and industrial value added (output) elasticities, this technique also uncovers the electricity Underlying Energy Demand Trend (UEDT) for the Turkish industrial sector and is, as far as is known, the first attempt to do this. The results suggest that output and real electricity prices and a UEDT all have an important role to play in driving Turkish industrial electricity demand. Consequently, they should all be incorporated when modelling Turkish industrial electricity demand and the estimated UEDT should arguably be considered in future energy policy decisions concerning the Turkish electricity industry. The output and price elasticities are estimated to be 0.15 and − 0.16 respectively, with an increasing (but at a decreasing rate) UEDT and based on the estimated equation, and different forecast assumptions, it is predicted that Turkish industrial electricity demand will be somewhere between 97 and 148 TWh by 2020.     

Modeling and forecasting of Turkey’s energy consumption using socio-economic and demographic variables

This study deals with the modeling of the energy consumption in Turkey in order to forecast future projections based on socio-economic and demographic variables (gross domestic product-GDP, population, import and export amounts, and employment) using artificial neural network (ANN) and regression analyses. For this purpose, four diverse models including different indicators were used in the analyses. As the result of the analyses, this research proposes Model 2 as a suitable ANN model (having four independent variables being GDP, population, the amount of import and export) to efficiently estimate the energy consumption for Turkey. The proposed model predicted the energy consumption better than the regression models and the other three ANN models. Thus, the future energy consumption of Turkey is calculated by means of this model under different scenarios. The predicted forecast results by ANN were compared with the official forecasts. Finally, it was concluded that all the scenarios that were analyzed gave lower estimates of the energy consumption than the MENR projections and these scenarios also showed that the future energy consumption of Turkey would vary between 117.0 and 175.4 Mtoe in 2014.     

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.     

Reforming the Energy Market and Utilization of Electrical Energy

The main objective of the present study is to provide an overview of reforming the Turkish energy market, including the electricity production and consumption values of Turkey and restructuring in the eight European Union countries. Turkey's electricity demand has been growing very rapidly. It has increased from about 47 TWh in 1990 to some 142 TWh in 2003, and it is expected to continue for the foreseeable future. Besides this, Turkey's total electricity capacity increased from 16,318 MW in 1990 to 31,846 MW in 2002. Restructuring of the electricity sector in the country has started with the establishment of the Energy Market Regulatory Authority (EMRA) upon the law (no. 4628) that came into force on March 3, 2001. The Energy Market Regulatory Board, which runs the EMRA, was commissioned on November 19, 2001. In May 2002, the EMRA issued drafts of the Energy Market Licensing Regulation and the Electricity Market Tariffs Regulation, and these regulations went into effect in August 2002. The Electricity Market Implementation Manual was also issued by the EMRA in April 2003. At present, not only the electricity sector, but the whole Turkish energy sector is in a dynamic change.     

Turkish aggregate electricity demand: An outlook to 2020

This paper investigates the relationship between Turkish aggregate electricity consumption, GDP and electricity prices in order to forecast future Turkish aggregate electricity demand. To achieve this, an aggregate electricity demand function for Turkey is estimated by applying the structural time series technique to annual data over the period 1960 to 2008. The results suggest that GDP, electricity prices and a UEDT (Underlying Energy Demand Trend) are all important drivers of Turkish electricity demand. The estimated income and price elasticities are found to be 0.17 and −0.11 respectively with the estimated UEDT found to be generally upward sloping (electricity using) but at a generally decreasing rate. Based on the estimated equation, and different forecast assumptions, it is predicted that Turkish aggregate electricity demand will be somewhere between 259 TWh and 368 TWh in 2020.     

An Application of Genetic Algorithm Search Techniques to the Future Total Exergy Input/Output Estimation

Since 1975, there has been a great deal of interest, particularly during the past decade, in the promising genetic algorithm (GA) and its application to various disciplines from medicine to cogeneration. However, the studies performed on energy-related GA modeling are relatively low in numbers. The main objective of the present study is to develop the exergy input/output estimation equations in order to estimate the future projections based on the GA notion. In this regard, the GA Future Total EXergy Input/Output Estimation Models (GAFTEXIEM/GAFTEXOEM) are used to estimate total exergy input/output demand of Turkey, which is selected as an application country, based on the economic and social indicators of gross domestic product (GDP), population, import, export and house production figures. The future prediction of Turkey's total exergy input/output values are projected between 2003 and 2023. It may be concluded that the models proposed here can be used as an alternative solution and estimation techniques to available estimation techniques. It is also expected that this study will be helpful in developing highly applicable and productive planning for energy policies.     

Assessing of energy policies based on Turkish agriculture:: current status and some implications

In this study, the current energy status of Turkey and the effects of national energy policies on Turkish agricultural support policies are discussed for both current and future requirements. Turkey is an energy-importing country producing 30 mtoe (million tons of oil equivalent) energy but consuming 80 mtoe. The energy import ratio of Turkey is 65–70% and the majority of this import is based on petroleum and natural gas. Furthermore, while world energy demand increases by 1.8% annually, Turkey’s energy demand increases by about 8%. Although energy consumption in agriculture is much lower than the other sectors in Turkey, energy use as both input and output of agricultural sector is a very important issue due to its large agricultural potential and rural area. Total agricultural land area is 27.8 million hectares and about 66.5% of this area is devoted for cereal production. On the other hand, Turkey has over 4 million agricultural farm holdings of which 70–75% is engaged in cereal production. Machinery expenses, mainly diesel, constitute 30–50% of total variable expenses in cereal production costs. It is observed that energy policies pursued in agriculture have been directly affected by diesel prices in Turkey. Therefore, support policy tools for using diesel and electricity in agriculture are being pursued by the Turkish government.     

Modelling and forecasting Turkish residential electricity demand

This research investigates the relationship between Turkish residential electricity consumption, household total final consumption expenditure and residential electricity prices by applying the structural time series model to annual data over the period from 1960 to 2008. Household total final consumption expenditure, real energy prices and an underlying energy demand trend are found to be important drivers of Turkish residential electricity demand with the estimated short run and the long run total final consumption expenditure elasticities being 0.38 and 1.57, respectively, and the estimated short run and long run price elasticities being −0.09 and −0.38, respectively. Moreover, the estimated underlying energy demand trend, (which, as far as is known, has not been investigated before for the Turkish residential sector) should be of some benefit to Turkish decision makers in terms of energy planning. It provides information about the impact of past policies, the influence of technical progress, the impacts of changes in consumer behaviour and the effects of changes in economic structure. Furthermore, based on the estimated equation, and different forecast assumptions, it is predicted that Turkish residential electricity demand will be somewhere between 48 and 80 TWh by 2020 compared to 40 TWh in 2008.     

A system dynamic model for production and consumption policy in Iran oil and gas sector

A system dynamic model is presented, which considers the feedback between supply and demand and oil revenue of the existing system in Iran considering different sectors of the economy. Also the export of the oil surplus and the injection of the gas surplus into the oil reservoirs are seen in the model by establishing a balance between supply and demand. In this model the counter-effects and existing system feedbacks between supply and demand and oil revenue can be seen considering different sectors of the economy. As a result, the effects of oil and gas policies in different scenarios for different sectors of Iran’s economy together with the counter-effects of energy consumption and oil revenue are examined. Three scenarios, which show the worst, base and ideal cases, are considered to find future trends of major variables such as seasonal gas consumption in power plants, seasonal injected gas in oil reservoirs, economic growth in the industrial sector, oil consumption in the transportation sector, industrial gas consumption and exported gas. For example, it is shown that the exported gas will reach between 500 and 620 million cubic-meter per day in different scenarios and export revenues can reach up to $500 billion by 2025.     

Forecasting the electricity consumption of the Mexican border states maquiladoras

The consumption of electricity by maquiladora industries in the Mexican border states is an important driver for determining future powerplant needs in that area. An industrial electricity forecasting model is developed for the border states' maquiladoras, and the outputs are compared with a reference forecasting model developed for the US industrial sector, for which considerably more data are available. This model enables the prediction of the effect of implementing various energy efficiency measures in the industrial sector. As an illustration, here the impact of implementing energy‐efficient lighting and motors in the Mexican border states' maquiladoras was determined to be substantial. Without such energy efficiency measures, electricity consumption for these industries is predicted to rise by 64% from 2001 to 2010, but if these measures are implemented on a gradual basis over the same time period, electricity consumption is forecast to rise by only 36%. Copyright © 2004 John Wiley & Sons, Ltd.     

Estimating the net electricity energy generation and demand using the ant colony optimization approach: Case of Turkey

This paper presents Turkey's net electricity energy generation and demand based on economic indicators. Forecasting model for electricity energy generation and demand is first proposed by the ant colony optimization (ACO) approach. It is multi-agent system in which the behavior of each ant is inspired by the foraging behavior of real ants to solve optimization problem. Ant colony optimization electricity energy estimation (ACOEEE) model is developed using population, gross domestic product (GDP), import and export. All equations proposed here are linear electricity energy generation and demand (linear_ACOEEGE and linear ACOEEDE) and quadratic energy generation and demand (quadratic_ACOEEGE and quadratic ACOEEDE). Quadratic models for both generation and demand provided better fit solution due to the fluctuations of the economic indicators. The ACOEEGE and ACOEEDE models indicate Turkey's net electricity energy generation and demand until 2025 according to three scenarios.     

Grey prediction with rolling mechanism for electricity demand forecasting of Turkey

The need for energy supply, especially for electricity, has been increasing in the last two decades in Turkey. In addition, owing to the uncertain economic structure of the country, electricity consumption has a chaotic and nonlinear trend. Hence, electricity configuration planning and estimation has been the most critical issue of active concern for Turkey. The Turkish Ministry of Energy and Natural Resources (MENR) has officially carried out energy planning studies using the Model of Analysis of the Energy Demand (MAED). In this paper, Grey prediction with rolling mechanism (GPRM) approach is proposed to predict the Turkey's total and industrial electricity consumption. GPRM approach is used because of high prediction accuracy, applicability in the case of limited data situations and requirement of little computational effort. Results show that proposed approach estimates more accurate results than the results of MAED, and have explicit advantages over extant studies. Future projections have also been done for total and industrial sector, respectively.     

Electricity end-uses in the residential sector of Brazil

The residential energy consumption has been studied in many countries as it usually accounts for a large percentage of the total energy consumption. Energy end-uses have also been a matter of concern as they can assist energy system planning. The objective of this paper is to assess the actual scenario of electricity consumption and estimate electricity end-uses in the residential sector of Brazil for different bioclimatic zones. The analysis is based on a survey performed by 17 energy utilities enclosing a total of 17,643 houses or flats over 12 states in Brazil. The survey was performed to obtain electricity consumption data for all household appliances found in houses and flats. The electricity end-uses were estimated by performing weighted averages according to the location of the dwellings in each bioclimatic zone. Results indicate that the largest end-uses are for refrigerator and freezer together, which account for about 38–49% of the electricity consumption in dwellings in Brazil. Air-conditioning and electric shower are the end-uses that are more dependent on the climatic conditions. The main conclusion that can be made from the analysis is that air-conditioning should be a major concern in the residential sector of Brazil in the near future as its ownership is still low, but its electricity consumption is already significant mainly over summer.     

Electricity demand for South Korean residential sector

This study estimates the electricity demand function for the residential sector of South Korea with the aim of examining the effects of improved energy efficiency, structural factors and household lifestyles on electricity consumption. In the study, time series data for the period from 1973 to 2007 is used in a structural time series model to estimate the long-term price and income elasticities and annual growth of underlying energy demand trend (UEDT) at the end of the estimation period. The result shows a long-term income elasticity of 1.33 and a long-term price elasticity of −0.27% with −0.93% as the percentage growth of UEDT at the end of the estimation period. This result suggests that, in order to encourage energy efficiency in the residential sector, the government should complement the market based pricing policies with non-market policies such as minimum energy efficiency standards and public enlightenment.     

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.     

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.     

Fuel price determination in transportation sector using predicted energy and transport demand

This study determines fuel price based on estimated sectoral energy and transport demand using pumping prices. Three approaches are first used for estimating energy and transportation demand based on linear time series, polynomial time series and genetic algorithm based (GATEDE and GATDETR), as multi-parameter, models. Then, future fuel prices and marginal costs of the energy consumption are obtained. Transport demand-based energy efficiency methods are also developed. The fuel prices (FP) are analyzed under two scenarios: Linear and exponential price scenarios. Results showed that if the FP increases linearly, the marginal cost will slightly decreases from current trend, but will increases if demand increases exponentially. Results also showed that the demand-based pricing policy would help to develop a new pricing policy for fuel use in order to control fast growing demand on this sector. The exponential price increase would also help to locate financial sources to create environmentally friendly transportation systems.