Electricity Consumption In Turkey Research Articles

Analyzing Determinants of Spatial Patterns in Total and Industrial Electricity Consumption in Turkey

This research investigates the spatial correlation among per capita electricity consumption, per capita industrial electricity consumption, and economic growth by employing various regression models, including linear regression, geographically weighted regression, and multi-stage geographically weighted regression. The primary goal is to illustrate the presence of spatial effects in the connection between electricity consumption and economic growth. In this context, this study made for Turkey distinguishes itself from previous research by utilizing the multi-stage spatially weighted regression model to examine this relationship. The findings reveal that the multi-scale spatial regression model is the most effective in explaining the relation between economic growth at the provincial level and per capita electricity consumption and per capita industrial electricity consumption. Moreover, the study emphasizes that per capita Gross Domestic Product emerges as the most influential regional economic indicator when assessing its impact on per capita electricity consumption and per capita industrial electricity consumption.

An advanced framework for net electricity consumption prediction: Incorporating novel machine learning models and optimization algorithms

In recent years, the escalating demand for electric energy has underscored the need for robust prediction models capable of accurately anticipating consumption patterns. The imperative lies in enabling utilities and policymakers to optimize resource allocation, strategically plan infrastructure development, and ensure the stability and efficiency of the power grid. This study undertakes a comprehensive comparative analysis of machine learning techniques employed in predicting net electricity consumption in Turkey. The primary goal is to augment the accuracy and performance of electricity load forecasting, thereby contributing to effective energy management and fostering sustainable development within the power sector. Two machine learning models, including CatBoost and Extreme Gradient Boosting (XGBoost), are strategically integrated with optimization algorithms such as Sparrow Search Algorithm (SSA), Phasor Particle Swarm Optimization (PPSO), and Hybrid Grey Wolf Optimization (GWO). The core analysis centers on evaluating the performance of these integrated models based on key accuracy metrics and runtime efficiency. Notably, the results underscore that the XGBoost-SSA model emerges as the superior performer, exhibiting heightened accuracy and superior performance in predicting electricity consumption. This model showcases the highest coefficient of determination (R2) value and demonstrates lower errors during the testing phase, thereby presenting a promising and effective approach for electricity consumption prediction in the specific context of Turkey.

Time-Stratified Analysis of Electricity Consumption: A Regression and Neural Network Approach in the Context of Turkey

This study aims to apply seasonality and temporal effects in the analysis of electricity consumption in Turkey as a case mixed with regression and neural network methodologies. The study goal is to increase knowledge about the features and trending forces behind electricity usage which provide informed recommendations for smart energy planning and regulation. Comparing and contrasting the regression and neural network models makes it possible to carry out a thorough analysis of the merits and demerits of each model. Moreover, the examination of the limits of the models and their performance in forecasting electricity consumption patterns over the long term is done. The results of this study have a significant impact on power forecasting techniques, and they have meaningful effects on the policymakers, planners and utilities in Turkey. Understanding the story of the use of electricity around the world is very important for the development of sustainable energy policies, resource provision, and the maintenance of reliable and smart energy networks in the country.

THE SOCIAL COST OF CARBON EVALUATION BASED ON CARBON CAPTURE AND STORAGE TECHNOLOGIES FOR POWER GENERATION PLANTS

Power plants produce electricity using high amounts of fossil fuels such as coal. Using these fossil fuels releases very high emissions into the atmosphere. Carbon capture and storage (CCS) applications have been developed to reduce these emissions. Thus, by capturing 96% of the CO<sub>2</sub> formed from the burning of fuels in power plants, the global warming potentials emitted by conventional electricity generation can be reduced by up to 80%. The study investigated applications of CCS options to reduce emissions during electricity generation, CO<sub>2</sub> emissions, pre-combustion, post-combustion, and oxyfuel coupled global warming potentials. The reCiPe midpoint method has been taken into account for CO<sub>2</sub> emission. CCS options calculate the reduction in global warming potential. The study accepts that hard and brown coal is used as fuel in power plants that produce electricity. Finally, for the three cities with the highest electricity consumption in Turkey, the global warming potential emission in conventional electricity generation power plants burning brown coal is calculated as 43.4 kilotons for plants burning brown coal. It has been calculated as 6.2 kilotons in power plants using oxyfuel CCS and burning hard coal. Ten-year life for Istanbul, for hard coal burning and pre-combustion related discount rate of 1% and 3 $/ton social cost of carbon; The social cost of carbon is calculated as $102.8 million.

COVID-19 karantinasının elektrik tüketimine ve üretimine etkisi: Türkiye örneği

In order to reduce the negative impact of the coronavirus (COVID-19), which first emerged in Wuhan, China measures such as distance education and curfews were taken by governments to reduce the negative impact on human health. These precautions had an impact on electricity consumption. Turkey was one of the countries adversely affected by COVID-19. Therefore, the objective of this paper analyze the variation of electricity consumption and generation in Turkey depending on the COVID-19 outbreake. For this purpose, residential, industrial, business and total electricity consumption of six provinces was analyzed in order to examine the variation in electricity consumption in Turkey. Considering the ten months of the year, with March when the first case occurred in 2020 compared to 2019, it was observed that residential electricity consumption increased in the six provinces studied. Industrial electricity consumption decreased in March and the following months, but increased in the following months. On the other hand, it was observed that there was a decrease in the business electricity consumption in almost all ten months. In the per capita electricity generation estimation study in Turkey, it was calculated that the electricity generated in 2020 is 5.89% more than the estimated value obtained by the curve fitting method. In addition, according to the study of coverage the electricity consumption and generation per capita for the years 2019 and 2020, it was calculated that generation could not coverage the consumption by 0.22% in 2019 and 0.27% in 2020.

Assessing the potential of solar power generation in Turkey: A PESTLE analysis and comparative study of promising regions using PVsyst software

Renewable energy sources have a tremendous amount of potential in Turkey. In the previous year, 43.2% of the country's electricity was generated from renewable energy sources. Solar photovoltaic (PV) energy accounted for 4.7% of the electricity generation and the installed capacity was 9.425 GW with 9353 solar power plants of various types. This paper provides an overview of the current state of solar PV potential in Turkey, evaluates its capacity to meet the country’s energy demand, and discusses its future prospects. The PESTLE analysis is performed based on political (P), economic (E), social (S), technological (T), legal (L), and environmental (E) factors to study the sustainable development of solar energy potential in Turkey. Furthermore, a comparative analysis of the solar energy potential between the Konya and Diyarbakır regions which are recognized as promising areas for harnessing the country's abundant solar resources has been conducted using the PVsyst software. The electricity consumption in Turkey will be 380.2 TWh in 2025, 455.3 TWh in 2030, and 510.5 TWh in 2035, according to the Turkey National Energy Plan. Hence, it is essential to maximize the use of solar energy capacity in the production of electricity tomeet the increased energy demand. The main objective of this study is to help strategic and systematic evaluation of the solar energy resource potential affecting large and small-scale solar power projects in Turkey.

The Dynamic Simulation Analysis of the Impact of Urbanization and Globalization on Environmental Quality

This study aims to analyze the effect of urban population and economic globalization on air quality in Turkey for the period 1970–2017, including GDP and electricity consumption as control variables. This paper is the first attempt to apply the dynamic autoregressive-distributed lag (DARDL) approach to explore the association between carbon emissions, urbanization, economic globalization, GDP, and electricity consumption in Turkey. The analysis results suggested the existence of a cointegration relationship between all series in the long run. DARDL results revealed that while urbanization has a statistically significant effect on carbon emissions in the short or long run, economic globalization has a positive impact in the long run. That is, economic globalization increases carbon emissions by about 0.15 percent. Thus, it can be said that globalization is more critical than urbanization in terms of environmental costs. In addition, it was determined that GDP and electricity consumption increase carbon emissions in both the long and short run. Also, the response of CO2 emissions to all shocks in the explanatories was determined. All future shocks of electricity consumption lead to no change in CO2 emissions. The negative shocks of both urbanization and globalization decrease CO2 emissions in the short run, but the response of CO2 is stable in the long run. The effects of a shock in GDP are exactly the opposite.

Energy and Economic Analysis of a Hydroelectric Power Plant: A Case Study

In this study, the electricity production and energy costs of hydroelectric power plant is analyzed by using actual power plants data. Using long term actual data, the capacity utilization rates of the hydroelectric power plant were estimated on an hourly basis. Economic analysis of the power plant and factors affecting the energy production costs (initial investment cost, operating-maintenance costs, etc.) were researched and the energy production costs of the power plant were assessed in detail. Additionally, for the first time in this study, a dimensionless number was defined to denote the variation of electricity consumption in the country on an hourly, monthly and yearly basis. This number was called the hourly electricity consumption coefficient (HECC). A detailed investigation of the electricity consumption in Turkey was analyzed.

Evaluation of Electricity Consumption in Turkey via Cubic Regression Analysis and One Way Anova Test

Evaluation of Electricity Consumption in Turkey via Cubic Regression Analysis and One Way Anova Test

Club convergence analysis of city-level electricity consumption in Turkey

This study examines the presence of convergence clubs in per capita electricity consumption across cities in Turkey over the period 2000–2020. Using the Phillips-Sul club convergence test and the clustering algorithm, we explore whether total, industrial, and residential electricity consumption across regions converge to the same long-run equilibrium or diverge. We also analyze the drivers of the club formation after identifying convergence clubs using ordered logit regressions. The findings support the full convergence in residential consumption while separate clubs with different convergence dynamics and steady-state levels are identified in industrial and total electricity consumption. Results indicate that there are two convergence clubs in total electricity consumption which may be clustered as high and low consumption regions. Our analysis also identifies two clusters in industrial consumption with a dominating club. Results from the ordered logit analysis suggest that income, industrial activities, and international trade are positively associated with club formation in total electricity consumption. Overall, our findings suggest that energy policies need to be tailored and implemented on a regional and sectoral basis since energy user groups have heterogeneous convergence behavior.

Turkey's long-term electricity consumption forecast using regression analysis

Demand forecasting is essential primarily for planning. Although it is crucial in many sectors and issues, it has particular importance for electricity. Therefore, the issue of electricity consumption forecasting has recently become a prevalent topic. In light of the above, this study aimed to develop an appropriate model to estimate the long-term electricity consumption of Turkey. The study consists of three steps. In the first step, eight models were developed to separately investigate the effects of eight input variables frequently used in electricity consumption forecasting studies in the literature. In the second step of the study, two models consisting of input variables with high impact in the first step were developed, and the trained performances of the developed models were calculated by using the regression analysis. In the final step, the combined effect of eight variables on electricity consumption forecasting was investigated using regression analysis. It can be conclude that the model in the third step showed significant results, and the model performance was good. Finally, Turkey's electricity consumption forecast for the years 2020–2030 was performed using the model in the third step.

Water-energy nexus of Turkey’s municipalities: Evidence from spatial panel data analysis

The efficient use of water will make a notable contribution to ensure energy efficiency in Turkey to reach its National Energy Efficiency Action Plan goals. From the water for electricity perspective, this is the first study to investigate the effects of water consumption on electricity consumption in Turkey’s provinces over the period 2008–2018. We apply the Spatial Durbin and Spatial Autoregressive Models to address the spatial dependence and spillover effects among neighboring provinces. This study’s core findings reveal that: (1) A 1% increase in water consumption raises electricity consumption by more than 0.10% on average, which puts pressure on electricity consumption. (2) Water consumption has positive spatial effects. (3) Electricity prices are negatively related to electricity consumption. (4) Urbanization has a reducing effect on electricity consumption supports energy efficiency. Overall, this paper fills the water-energy nexus gap in Turkey. This study’s results can provide decision-makers a different perspective on the importance of efficient water use in improving energy efficiency.

Convergence of electricity consumption in Turkey: Spatial panel data analysis

The issue of convergence has been discussed in many theoretical and empirical studies. Because per capita electricity consumption is considered as an indication of economic development, this study aims to determine the presence of ?absolute and conditional beta (?) convergence? of per capita total electricity consumption across the provinces of Turkey between 1986 and 2013. This work is the first investigation of electricity consumption convergence in Turkey. Based on the annual balanced panel data and the spatial panel data model, our findings indicate absolute ? convergence of per capita electricity consumption across the provinces of Turkey. We conclude that regional policies are successful in reducing regional disparities in per capita electricity consumption among the provinces of Turkey. However, other indicators of economic development should be examined to determine the overall convergence.

Principal components based robust vector autoregression prediction of Turkey’s electricity consumption

A first order vector autoregression topology was used to model and predict Turkey’s net electricity consumption in the future. Input variables for the model were the annual values of electricity consumption along with four demographic and economic indicators such as, population, gross domestic product, imports and exports. Output variables were the one-step-ahead values of the same variables. First, polynomial regressions were used to determine and remove the trend components of all these five variables. Then, principal components regression method was applied to evaluate the coefficients of the vector autoregression model. Electricity consumption of Turkey was modeled using annual data from 1970 to 2016 and the model was used to predict future consumption values until year 2030. Singular value decomposition was used to determine the number of important dimensions in the data. This approach yielded a significant reduction in the dimensionality of the problem and thus provided robustness to the predictions. The results showed the feasibility of applying principal components regression method to vector autoregression model for electricity consumption prediction.

The Link Between Industry Value Added and Electricity Consumption

This study aims to analyse the link between industry value added and electricity consumption in Turkey for the years between 1970 and 2015. The sectoral distribution of net electricity consumption has been obtained from Turkey Electricity Distribution and Consumption Statistics on GWh basis while industry value added data have been obtained from World Development Indicators’ data bases on dollar basis with the fixed prices of the year 2012. The correlations between the variables have been examined by means of Johansen cointegration test and error correction model. According to the results of the study, it has been seen that there exists bidirectional relationship between the electricity consumption and value added variable in the long term. On the other hand, in terms of the Error Correction Model used for a short-term relationship, no short-term relationship has been identified between the variables.

Electricity consumption forecasting for Turkey with nonhomogeneous discrete grey model

ABSTRACTThe accuracy of forecasting is an essential issue for decision makers in terms of energy planning. During the recent years, several techniques have been used for electricity consumption forecasting in order to accurately predict the future demand. Although there are several forecasting techniques, selection of the most appropriate one is of paramount importance. In this study, three different grey forecasting models are built and used for modeling and predicting yearly net electricity consumption in Turkey. Additionally, these three models are compared to find the best model by using performance criteria. The best approach, Nonhomogeneous Discrete Grey Model (NDGM), is employed to forecast electricity consumption from 2014 to 2030. In addition, a comparison is made with recent studies proving the grey model (GM) proposed by this study delivers better forecasting performance.

A Multivariate Causality Analysis of Economic Growth and Electricity Consumption in Turkey

This study uses time series data to examine the long-run and short-run effects of electricity consumption, real GDP per capita, exports and financial development in Turkey between 1970-2011. The study adopted structural break unit root test, ARDL bounds tests methods to test cointegration to achieve robust results. Inter-variable causal relationships were determined using the Toda and Yamamoto (1995) framework. The findings were that electricity consumption, financial development and exports are cointegrated in the long run. Following this development, financial development was found to create a significant increase in both economic growth and electricity consumption in Turkey. The findings were that electricity consumption positively impacts on the economic growth in addition to the fact that bidirectional causality between economic growth and electricity consumption were established. These results also support that, feedback hypothesis is confirmed in Turkey.

Convergence of Electricity Consumption in Turkey: Spatial Panel Data Analysis

The issue of convergence has been discussed in many theoretical and empirical studies. Because per capita electricity consumption is considered as an indication of economic growth and development, this study aims to test the presence of “absolute and conditional beta convergence” of per capita electricity consumption among the provinces of Turkey between 1986 and 2013. Using the spatial panel data model, our findings show evidence of absolute beta-convergence of per capita electricity consumption among the provinces of Turkey. We conclude that regional policies are successful in reducing the regional disparities in per capita electricity consumption among the provinces of Turkey.

Forecasting the annual electricity consumption of Turkey using an optimized grey model

Energy demand forecasting is an important issue for governments, energy sector investors and other related corporations. Although there are several forecasting techniques, selection of the most appropriate technique is of vital importance. One of the forecasting techniques which has proved successful in prediction is Grey Modeling (1,1). Grey Modeling (1,1) does not need any prior knowledge and it can be used when the amount of input data is limited. However, the basic form of Grey Modeling (1,1) still needs to be improved to obtain better forecasts. In this study, total electric energy demand of Turkey is predicted for the 2013–2025 period by using an optimized Grey Modeling (1,1) forecasting technique called Optimized Grey Modeling (1,1). The Optimized Grey Modeling (1,1) technique is implemented both in direct and iterative manners. The results show the superiority of Optimized Grey Modeling (1,1) when compared with the results from literature. Another finding of the study is that the direct forecasting approach results in better predictions than the iterative forecasting approach in forecasting Turkey's electricity consumption. The supply values of primary energy resources in order to produce electricity have calculated for 2015, 2020 and 2025 by using the outputs of Optimized Grey Modeling (1,1).

Modeling Energy Consumption for Growth in an Open Economy: ARDL and Causality Analysis for Turkey

In a growing open economy, energy consumption plays a critical role. Insufficient energy may constrain output, while exogenous factors, such as shocks in world price, may limit the aggregate growth rate of the economy. This study investigates the long-run equilibrium of energy consumption in Turkey from a unique perspective, taking tourism expansion as a proxy for exogenous growth. The paper uses the Autoregressive Distributed Lag (ARDL) model to determine an econometric relationship between electricity consumption, foreign tourist arrivals, and the economic growth using data during 1960–2010. Granger causality test determines the direction of causality among these variables under conditional error correction model (CECM) supplemented, whenever required, by the Bounds test. Results reveal that foreign tourist arrivals and real gross domestic product (GDP) are significant determinants of a long-run equilibrium relationship with electricity consumption for Turkey. A positive significant relationship has been found between electricity consumption, foreign tourist arrivals, and economic growth in the long run. The estimated ARDL elasticity coefficients indicate that a 1% increase in the number of foreign tourist arrivals and GDP growth cause 0.045% and 0.74% increase in the total electricity consumption of Turkey, respectively, after adjustments consistent with standard procedures.