Turkey's Energy Demand Research Articles

Strategic location analysis for offshore wind farms to sustainably fulfill railway energy demand in Turkey

Providing the energy demands of various sectors through the utilization of renewable energy sources offers a more sustainable and long-term approach compared to fossil fuel consumption. In this study, the proposition is made to address Turkey's rail industry energy demand through Offshore Wind Farms (OWF). Accordingly, the spatial analysis for determining the most suitable OWF location is explored. The research methodology is conducted based on a Geographic Information System-Multi-Criteria Decision Making (GIS-MCDM) hybrid approach. Alternative OWF locations are identified using GIS. Location selection criteria are established based on literature review, and a fuzzy-based decision matrix is constructed, incorporating expert opinions. To leverage two distinct fuzzy number sets, Intuitionistic Fuzzy Weighted Averaging (IFWA) and Spherical Fuzzy Stepwise Weight Assessment Ratio Analysis (SF-SWARA) methods are employed for criteria weighting, followed by the aggregation of criterion weights using the Einstein Operations t-norm technique. To ensure comparable OWF alternative rankings, five different alternative ranking methods are employed to obtain alternative rankings. Ultimately, the most suitable OWF location for meeting the energy demand of the rail industry is determined to be the Izmir region. The research findings contribute to managerial perspectives by proposing recommendations concerning strategic location selection, optimized resource allocation, risk mitigation and policy alignment and sustainable long-term planning.

Research and Development Expenditures and Renewable Energy: An Empirical Analysis in Türkiye

In recent years, the increase in imported energy demand in Turkey significantly increases the current account deficit. In developing countries such as Turkey, foreign dependence on energy resources such as oil and natural gas, which is necessary for the realization of production, causes the current account deficit to increase. In this context, turning to renewable energy sources and increasing the amount of energy obtained from these sources is one of the alternatives to get rid of Turkey`s dependence on foreign energy in terms of energy. The aim of this study is to examine the relationship between renewable energy consumption and research and development (R&D) expenditures in Turkey for the period 1998-2021 and to determine to what extent these variables affect each other. In the study, Granger causality analysis was applied using renewable energy consumption and R&D expenditure data. According to the findings obtained in the study, a unilateral causality relationship was determined from renewable energy consumption to R&D expenditures.

TÜRKİYE'S ROLE IN ENERGY SECURITY AND RENEWABLE ENERGY SOURCES

The process of globalization, accelerated industrial development, continuous technological advancements, and the ever-growing global population are factors that contribute to the increased utilization of existing energy sources. However, fossil energy sources are limited and inadequate in the face of these developments. Consequently, countries have started seeking alternative energy sources, leading to the utilization of renewable energy sources. This research aims to highlight the significance of Turkey, a strategically positioned country with the potential to be a key player in the energy market, in terms of energy security and its energy policies. Moreover, the study compares renewable energy sources with conventional energy sources within the context of energy security. This work is based on a literature analysis that synthesizes information obtained from existing sources, without involving fieldwork or experimental methods. The findings of this research indicate that Turkey is a country heavily reliant on energy imports. Considering its insufficient fossil fuel resources and its status as a developing nation experiencing continuous growth, it is foreseeable that Turkey's energy demand will steadily rise in parallel with its development. Turkey's current level of energy dependency poses a significant risk to its energy security. Furthermore, Turkey's geographical location places it as a natural energy bridge between the East and the West, which imposes further responsibilities in terms of energy security. To reduce Turkey's external energy dependency and diversify its energy sources, it should prioritize its existing and renewable resources, directing its investments, support, and incentives accordingly.

Prediction of transportation energy demand in Türkiye using stacking ensemble models: Methodology and comparative analysis

The transportation sector accounts for 61.5% of global oil consumption and is responsible for 29% of the world’s total energy demand. Passenger transportation utilizes around 50%–60% of the energy used for transportation-related activities. Accurate prediction of future transportation energy consumption is essential for governments to make well-informed decisions regarding transportation infrastructure development and utilization, which supports the United Nations’ Sustainable Development Goals (SDGs) and advances the shift to a net-zero carbon economy. With the expected increase in population, vehicles, and economic growth, it is essential to predict the energy demand to ensure sustainable urban transportation. This is crucial not only for economic prosperity but also for promoting human health and mitigating carbon emissions. Therefore, transportation energy demand prediction plays a vital role in designing sustainable future urban transportation and making informed energy investment and policy decisions. This study proposes a novel methodology and investigates for the application of machine learning stacking ensemble method with hyperparameter tuning and multicollinearity removal to predict transportation energy demand in Turkey based on historic data from 1975–2019. The dataset includes GDP, year, vehicle miles traveled, population, oil price, passenger miles traveled, and ton-miles traveled as features. A performance evaluation and comparison of 19 machine learning algorithms is first carried out to find the best candidate for the stacking ensemble models, including eXtreme Gradient Boosting algorithm. This performance comparison uses all features and also only two of them during the training phase, and it takes into consideration a 4-fold cross-validation. A combination of permutation importance and hierarchical clustering algorithm on the Spearman rank-order correlations is used for dimensionality reduction of the dataset. Extra Tree Regressor and ADABoost Regressor, which are both placed in the second level of the suggested models, are two meta-regressors that are proposed for stacking ensembles because they perform better compared to single machine learning algorithm. In total, eight stacking ensemble models – four for each of the meta-regressors – were developed and investigated considering all features and only two of them separately. Six metrics – R-squared, MSE, MAE, RMSE, RMSLE, and MAPE – are used to assess all models. The Extra Trees Regressor can be used as a meta-regressor in the best proposed stacking ensemble model to predict the energy demand for transportation. This model achieves an R-squared value of approximately 0.99 when all the features are taken into consideration. When only two features from the dataset are considered the same stacking ensemble model can achieve an accuracy of 0.98. These findings have the potential to contribute to the development of more accurate models and results, which can, in turn, lead to improved strategies for managing future transportation energy demand. Additionally, this research can support the advancement of alternative technologies that promote sustainable urban development, ultimately helping to move towards a net-zero carbon economy.

Economic viability assessments of high voltage direct current for wind energy systems

This research shows an economic assessment for decision-makers of high voltage direct current utilization in wind energy systems applied to a case study in Turkey. The increasing energy demand in Turkey can be supplied effectively by this transmission in long distances and leading the use of renewable energy sources. Thereby, wind energy systems are being one of the main developed renewable sources, but the economic feasibility of its feature has been demonstrated only in some cases. The transmission of wind energy is possible both inside a country and between the countries, depending on the deployments about the utilization of high voltage direct current technology, operating with renewable energy systems. Thus, life-cycle cost scenarios are proposed by recommending a 1,000 km length high voltage direct current overhead transmission line in optimum economic conditions between Canakkale and Samsun-Bafra to transport energy from wind farms. The economic feasibility of these systems can be proven by showing the comparison between investment cost and profit from the 25 years life-time of the project. A novel cumulative cash flow analysis is applied for this comparison employing the net present value. A voltage source converter and a current commuted converter are considered in the scenarios. The findings show that voltage source converter can be chosen for both overhead and cable transmission for wind energy systems with the need of government subsidies in the investment. Moreover, the possibility of the investment is discussed through economic terms, i.e., the positive externality, the opportunity cost and the economies of scale. Therefore, this study can be taken as a pre-model for both the other regions of Turkey and for the developing countries.

Investigation of Turkey's Energy Demand in Transportation by Robust Statistical Methods

Toplumların nüfuslarının ve kalkınma düzeylerinin artması buna bağlı olarak, taşıt sayılarının ve seyahat eden kişi sayılarının da artmasıyla, dünyadaki enerji talebi de artmaktadır. Türkiye’nin enerji kaynaklarının sınırlı olması sebebiyle, sürekli artan enerji talebini ve tüketimini karşılaması gerekmektedir. Türkiye, petrol ve sıvı yakıt ithalatı ile enerji talebini ve tüketimini karşılamaktadır. Enerji ile ilgili talep ve tüketim modellerinin kurulması önemli bir adım olarak karşımıza çıkmaktadır. Bu çalışmada, regresyon analizi tekniği yaklaşımlarından olan sağlam regresyon analiziyle, Türkiye’nin ulaşıma enerji talebi değerlendirilmiştir. Tüm yöntemler içinde, en iyi yöntemin Kantil regresyonu 0.50 olduğu belirlenmiştir. EKK yöntemine göre, Ton-Km, Araç-Km, Yolcu-Km ve Petrol fiyatı değişkenlerinin ulaşım enerjisini açıklama oranının %85,6 olduğu belirlenmiştir. Kantil regresyonu 0.50 yöntemi uygulandığında, Ton-Km, Araç-Km, Yolcu-Km ve Petrol fiyatı değişkenlerinin ulaşım enerjisini açıklama oranının %90,5 olduğu görülmektedir.

On the Prediction of One-Year Ahead Energy Demand in Turkey using Metaheuristic Algorithms

On the Prediction of One-Year Ahead Energy Demand in Turkey using Metaheuristic Algorithms

Forecasting of transportation-related energy demand and CO2 emissions in Turkey with different machine learning algorithms

Adverse impacts of the transportation sector on not only air quality but also economic growth of a country are nowadays well-noticed, particularly by developing countries. Today, the transportation sector is powered by burning the fossil-based fuels at more than 99% and approximately 6.5 million deaths annually occur due to air-pollution-related diseases worldwide. Therefore, knowledge of both energy demand and CO 2 emission of a country is a very significant issue in order to revise its future energy investments and policies. In this framework, three machine learning algorithms (deep learning (DL), support vector machine (SVM), and artificial neural network (ANN)) are used to forecast the transportation-based-CO 2 emission and energy demand in Turkey. The gross domestic product per capita (GDP), population, vehicle kilometer, and year are used as input parameters in the study. It is noticed that there is a very high correlation among year, economic indicators, population, vehicle kilometer, transportation-based energy demand, and CO 2 emissions. To present a better comparison, the results of these algorithms are discussed with six frequently used statistical metrics (R 2 , RMSE, MAPE, MBE, rRMSE, and MABE). For all machine learning algorithms, R 2 values are varying between 0.8639 and 0.9235, and RMSE is smaller than 5 × 10 6 tons for CO 2 emission and 2 Mtoe for energy demand. According to the classifications in the literature, the forecast results are generally categorized as "excellent" for rRMSE metric (<10%), and “high prediction accuracy” for MAPE metric (<10%). On the other hand, with two mathematical models, future energy demand and CO 2 emission arising from the transportation sector in Turkey are forecasted by the year 2050. In the results, it is forecasted that the annual growth rate for transportation-related energy demand and CO 2 emission in Turkey cumulatively rise by 3.7% and 3.65%, respectively. Both energy demand and CO 2 emissions from the transportation sector in Turkey will increase nearly 3.4 times higher in the year 2050 than those of today. In conclusion, the paper clearly reports that the future energy investments of the country should be revised, and various policies, regulations, norms, restrictions, legislations, and challenges on both energy consumption and emission mitigation from the transportation sector should be established by the policy-makers.

A Hybrid Algorithm for Forecasting Transportation Energy Demand of Turkey

A Hybrid Algorithm for Forecasting Transportation Energy Demand of Turkey

An Empirical Analysis of Energy Demand in Turkey: Estimating Price and Income Elasticities of Crude Oil With Different Data Frequencies

The present study aims to examine crude oil import demand in Turkey. The country has quite limited oil production and is dependent on foreign supply to power its growing economy. We estimate the price and income elasticities of imported crude oil for Turkey exploiting three different data sets (i.e., monthly, quarterly, and annual) to test whether data frequency matters in estimation. We employ an Auto Regressive Distributed Lag (ARDL) model which shows both short-run and long-run effects of price and income changes on crude oil import demand. Results show that i) data frequency plays an important role in the estimation process as the findings of monthly and quarterly model show a long-run equilibrium while annual models do not and that ii) the estimated elasticities of monthly and quarterly data are in line with microeconomic theory and literature in that price elasticities are inelastic suggesting that consumers are less responsive to the price changes due to high dependency and lack of substitute for crude oil and that income elasticities are elastic suggesting the demand for crude oil increases as income level increases.

Artificial Intelligence Approaches to Estimate the Transport Energy Demand in Turkey

In this study, eight parameters are selected and their historical data are collected to predict the future of the energy demand of Turkey. The initial eight parameters were the gross domestic product (GDP) of Turkey, average annual US crude oil price (COP), inflation for Turkey in percentages (INF), the population of Turkey, total vehicle travel in kilometers for Turkey, total amount of goods transported on motorways, employment for Turkey, and trade of Turkey. However, after these eight parameters data are analyzed using Pearson and Spearman correlation methods, it is found out that five of these parameters are highly correlated. The remaining three parameters are the GDP of Turkey, COP, and INF for Turkey. Afterward, five separate scenarios are developed to forecast the future of the energy demand of Turkey. The first two scenarios involve the third- and fourth-order polynomial fitting, the third and fourth scenarios employ static and recurrent neural networks, and the fifth scenario utilizes autoregressive models to predict the future energy demand of Turkey. The efficient hybridization of the seagull optimization and very optimistic method of minimization metaheuristic algorithms is carried out to achieve the polynomial fitting of the data. The optimization performance of the hybrid algorithm is assessed by applying the algorithm on benchmark optimization problems and comparing the results with that of some other metaheuristic optimizers. Moreover, it is seen that the forecasts of the first scenario agree well with the Ministry of the Energy and Natural Resources estimates.

Determination of industrial energy demand in Turkey using MLR, ANFIS and PSO-ANFIS

Energy is one of the most critical inputs in social and economic development, is an essential factor in increasing living standards and creating sustainable development. Since energy is an indispensable input in all sectors, energy dependence contributed to the necessity of countries' energy policy. It has critical importance to predict energy demand to determine energy policies. According to Turkey's annual energy consumption, the industry sector has consumed the most energy in the last five years. The prediction and analysis of energy demand with economic data in the industrial sector is an essential indicator of the economic development relationship between energy demand and industry. In this study, Multiple Linear Regression (MLR), Adaptive Neuro-Fuzzy Inference System (ANFIS), and optimized ANFIS with Particle Swarm Optimization (PSO) methods are employed to forecast energy demand for Turkish industrial sectors. The indicators which affect energy consumption were determined to estimate the energy demand. The 30-year dataset between 1990 and 2019 was split as training and test set. MLR, ANFIS and PSO- ANFIS were compared according to performance evaluation, and the most proper model was identified. The coefficient of determination (R2) for PSO-ANFIS, MLR, and ANFIS models are 0.9951, 0.9889, and 0.9932 in the training stage, and 0.9423, 0.9181, and 0.8776 in the testing stage, respectively. The study results indicated that the PSO-ANFIS model showed superior prediction capability with the least estimation error than MLR and ANFIS models. Consequently, parameters tuned PSO-ANFIS is able to predict the industrial energy demand in Turkey with high accuracy.

Change in energy demand and CO&lt;SUB align="right"&gt;2 emission in Turkey from the period 1981-1998 to the period 2007-2019

Due to population increase and change in climate, energy demand and CO2 emissions have risen. This study investigated the effects of climate and population change on energy demand in Turkey between periods of 1981-1998 and 2007-2019. In addition, CO2 emissions based on heating and cooling degree-days were examined since CO2 release with increased energy consumption is a major environmental impact that can contribute to climate change. The results showed that total heating degree-days decreased by 11.21% and total cooling degree-days increased by 38.98% due to climate change. The total product of population heating and cooling degree-days were increased by 14.42% from 1981-1998 to 2007-2009. CO2 emissions from heating in 78 cities decreased, while CO2 emissions from cooling in 77 cities increased. Results can be useful for planning future energy consumption in buildings as the climate changes.

Change in energy demand and CO&lt;SUB align="right"&gt;2 emission in Turkey from the period 1981-1998 to the period 2007-2019

Due to population increase and change in climate, energy demand and CO2 emissions have risen. This study investigated the effects of climate and population change on energy demand in Turkey between periods of 1981-1998 and 2007-2019. In addition, CO2 emissions based on heating and cooling degree-days were examined since CO2 release with increased energy consumption is a major environmental impact that can contribute to climate change. The results showed that total heating degree-days decreased by 11.21% and total cooling degree-days increased by 38.98% due to climate change. The total product of population heating and cooling degree-days were increased by 14.42% from 1981-1998 to 2007-2009. CO2 emissions from heating in 78 cities decreased, while CO2 emissions from cooling in 77 cities increased. Results can be useful for planning future energy consumption in buildings as the climate changes.

The Estimation of Turkey's Energy Demand Through Artificial Neural Networks and Support Vector Regression Methods

Energy demand and consumption are very important for the development and progress of countries. Energy demand is increasing rapidly day by day, especially in developing countries. Energy policies should be determined correctly to sustain the industry sector and make the right investments. Forecasting energy demand in the near and long term is important for the strategy that countries will follow. In this study, by using the monthly electricity energy data realized in Turkey between January 2016 and March 2020 and other data affecting this, a model to estimate electrical energy consumption was developed. In this model, artificial neural networks (ANN) and support vector regression (SVR) were used as methods. This study used 15 independent variables as the input value, and Turkey's energy consumption value as the dependent variable was estimated. Correlation, coefficient of determination, MAE, MSE, RMSE, MAPE statistical methods were used to measure success and error rate, and both models were found to have acceptable error values and success estimation rates. According to the results, it was concluded that the ANN method was more successful than the SVR method.

Turkey’s energy transition from fossil-based to renewable up to 2030: milestones, challenges and opportunities

The energy demand of Turkey has been increasing along with increasing life standards and population and growing economy particularly since the 1980s. Most of the oil and natural gas have been imported by Turkey due to insufficient domestic reserves. Nowadays, the population of Turkey is about 83 million. The Turkish economy grew by 3% in the last 30 years and became the 18th largest economy in the world and the 6th one in Europe. Turkey aims to use energy resources efficiently, effectively and eco-friendly. Concerning climate change, Turkey signed the Paris Agreement and pledged a 21% reduction in greenhouse gas emission by the year 2030. While the energy world is in an inevitable transition to green energy, Turkey, which has a huge potential to achieve 100% energy transition in renewable energy, is still the 17th largest country in the utilization of renewable energy. Although Turkey has already reached the 2023 targets concerning the utilization of renewable energy, there is a long path to reach the target of reducing greenhouse gas emissions. The 2030 targets of Turkey are to increase the installed solar capacity up to 38,000 MW, wind capacity up to 25,000 MW and geothermal capacity up to 4000 MW. Turkey officially announced that the utilization of renewable energy will be 32%. However, taking into account the potential and success in 2023 targets, it was seen that 2030 targets should be revised and increased from 32% to at least 50%. This study presents the milestones, challenges and opportunities of Turkey on the way of the energy transition toward renewable up to 2030. The energy goals of Turkey which are to use domestic energy resources efficiently and effectively were discussed and presented in detail. The aim of Turkey is to reduce the energy import dependency (76%). The installed energy capacities, energy consumption and projections, energy security and energy policies of Turkey were also presented.

Determination with Linear Form of Turkey's Energy Demand Forecasting by the Tree Seed Algorithm and the Modified Tree Seed Algorithm

Energy plays an important role in every stage of human life in different forms and variations. Along with the developments in economic, social and industrial fields, the amount of ener ...

An Estimation of Transport Energy Demand in Turkey via Artificial Neural Networks

The transportation sector accounts for nearly 19% of total energy consumption in Turkey, where energy demand increases rapidly depending on the economic and human population growth and the increasing number of motor vehicles. Hence, the estimation of future energy demand is of great importance to design, plan and use the transportation systems more efficiently, for which a reliable quantitative estimation is of primary concern. However, the estimation of transport energy demand is a complex task, since various model parameters are interacting with each other. In this study, artificial neural networks were used to estimate the energy demand in transportation sector in Turkey. Gross domestic product, oil prices, population, vehicle-km, ton-km and passenger-km were selected as parameters by considering the data for the period from 1975 to 2016. Seven models in total were created and analyzed. The best yielding model with the parameters of oil price, population and motor vehicle-km was determined to have the lowest error and the highest R2 values. This model was selected to estimate transport energy demand for the years 2020, 2023, 2025 and 2030.

Modelling of the landslide-induced impulse waves in the Artvin Dam reservoir by empirical approach and 3D numerical simulation

Due to the burgeoning energy demand in Turkey, there has been a significant increase in the number of dams and hydroelectric power plants constructed; >250 dams have been built in the past 10 years. Consequently, various engineering problems were encountered due to landslides near reservoirs, both during and after the construction. Generally, two-dimensional (2D) and three-dimensional (3D) empirical equations have been used to calculate the physical properties and propagation of the landslide-induced impulse waves; however, 3D numerical simulation-based methodologies are rarely used, although they can model the complex geometry of dam reservoirs better, using detailed topographic data. In this study, therefore, 3D numerical analysis is carried out for a potential generation of impulse waves, using the data of paleo-landslide at the Artvin Dam reservoir (NE Turkey), and the results are compared with the calculated results by empirical equations. A finite element method-based shear strength reduction analysis (FEM-SSR) was performed to analyse the slope stability. After the establishment of the correct propagation model, the equations were utilised with different input parameters and 3D numerical analysis-based simulations to evaluate the characteristics of the impulse waves. In the numerical model created using the FLOW-3D software, the ‘drift-flux model’ was used to simulate the probable landslides. The Reynolds-averaged Navier–Stokes equations were used in a free-surface modelling technique together with the volume of fluid (VOF) model for simulating the wave generation. The renormalised group model (RNG) for viscous flow and k-ε turbulence model for fluid–fluid coupled condition, were used as the boundary conditions. The results indicated that the values obtained from the proposed empirical equations and 3D numerical analysis were not similar. Thus, if the reservoir geometry is very complex and the wave propagation distance is more than a few kilometers (as in this study), highly detailed engineering geological studies should be performed to accurately define the boundary conditions, and three-dimensional numerical analyses should be performed to construct the impulse wave model.

Flower pollination algorithm approach for the transportation energy demand estimation in Turkey: model development and application

ABSTRACTThis study proposes a new optimization technique to estimate the Transportation Energy Demand (TED) employing the Flower Pollination Algorithm (FPA). The TED estimation models were developed based on three parameters, which are Annual Vehicle-Km (AVK), Gross Domestic Product per Capita (GDPperC), and Carbon-dioxide (CO2) emission according to the linear, power and quadratic forms. These three parameters were determined by the WEKA data mining software program among nine parameters. Randomly selected 80% of historical data for 47 years, from 1970 to 2016, were used for the training of the algorithm, and the remains were used in the testing stage of the models. The performances of the models were evaluated according to six different statistical criteria. Transportation energy demand forecasts by 2035 were carried out using three different scenarios using the TED estimation models. According to the scenarios, it is predicted that the transportation energy demand in Turkey will have doubled by 2035 in comparison with 2016. The FPA approach has been successfully applied in the development of the TED estimation models. The most important impact of this study is to help the creation of strategic action plans for energy policies in the transport sector and to contribute to the more efficient use of limited energy resources in the country.