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    Electricity consumption forecast using machine learning regression models in Turkey
    (Institute of Electrical and Electronics Engineers Inc., 2022) Şentürk, Ümit; Beken, Murat; Eyecioğlu, Önder
    In today's energy crisis, countries need to know their energy consumption and make their energy investments accordingly. The variability of end users demanding energy makes it difficult to estimate energy needs. In this article, it has been tried to forecast the future consumption from the electrical energy data consumed in Turkey between the years 2016-2022. After the electricity consumption data was converted into daily data, electrical energy consumption estimations were made with machine learning methods such as linear regression, tree regression, voting regression, XGB regression and Artificial neural network (ANN) methods. Estimation results were evaluated with Mean Square Error (MSE) and R2 (coefficient of determination) performance metrics. As a result of the evaluations made with the test data, MSE=0.006 (0-1 min-max normalization dataset) and R2= 82.7 performances, voting regression obtained the best result among the methods used. Accurate estimation of energy consumption will enable energy production to be made at the optimum level. © 2022 IEEE.
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    Estimating the effect of renewable energy policies on the Republic of Turkey's gross national product by using artificial Intelligence
    (Institute of Electrical and Electronics Engineers Inc., 2022) Beken, Murat; Kurt, Nursaç; Eyecioğlu, Önder
    Today, most of the states in the energy field, especially the developed countries in the world, continue to increase the rate of use of renewable energy. The Republic of Turkey is one of these states. This study aimed to examine the effect of the renewable energy transformation process on the gross national product with artificial intelligence in the action plan of the Republic of Turkey in the process of moving away from fossil fuel-based energy sources. We deployed artificial neural networks to estimate Turkey's gross national product. As a result of the study, because of the policy of increasing the use of renewable energy sources by 7% annually and reducing the use of fossil derivative fuels by 8% annually in the next twenty years, the energy distributions in 2043 and their effect on Turkey's gross national product are shown. © 2022 IEEE.
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    Estimation of gas emission values on highways in Turkey with machine learning
    (IEEE, 2021) Kurt, Nursaç; Öztürk, Oktay; Beken, Murat
    Due to its geographical location, Turkey has been home to many civilizations for centuries. It has always acted as a bridge between west and east and will continue to do so. The development of road networks in Turkey and the difference in transportation methods are increasing the number of national and international traveling vehicles day by day. In this study, gas emission (CO2, CH4, N2O) value changes have been predicted according to vehicle types of vehicle mobility on highways using machine learning (Linear Regression, Bayesian Ridge, Random Forest Regressor, MLP Regressor, SVR) algorithms. Based on these results, the gas emission value and environmental impact that may occur in the future are estimated-each method evaluated with MAE, MSE, RMSE, and R2 statistical metrics. As a result, we obtain R square scores of 0.963231 for CO2, 0.9856 for CH4, and 0.982404 for N2O from the random forest regressor, random forest regressor, and MLP regressor, respectively.
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    Forecasting the stability of A 4-node architecture smart grid using machine learning
    (Ieee, 2022) Hangün, Batuhan; Eyecioğlu, Önder; Beken, Murat
    Smart grid stability is one of the most important factors that can be used as a criterion for assessing the usability of smart grid architecture, so testing and predicting stability under various circumstances hold great importance. As a result of the increase in residential and industrial structures, and the integration of renewable energy into the smart grids, some intelligent solutions to predict stability to prevent unwanted instabilities in a future smart grid architecture is needed. In this study, we used various machine learning methods to predict smart grid stability. We approached the problem as a classification problem, we used a 4-node architecture smart grid dataset, and applied some well-known classification methods to classify the dataset into two classes which are stable and unstable. For the classification part, we used k-Nearest Neighbour (kNN), neural networks (NN), a support vector machine (SVM), and a decision tree. All four methods were tested under different hyper parameters. Finally, the ones with the best results were reported.
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    Investigating the energy production trends of countries and its relationship between economic development
    (Institute of Electrical and Electronics Engineers Inc., 2022) Hangün, Batuhan; Eyecioğlu, Önder; Beken, Murat
    There are three main energy resources for energy production that is currently used by the countries can be listed as fossil fuels, renewable energy, and nuclear energy. Since the industrial revolution, the fundamental energy resource of the world is fossil sources. Due to their problems such as greenhouse gas emissions, production cost, etc., fossil fuels are not sustainable sources in terms of world ecology and economy. Developed countries of the world focused on using alternative energy resources like renewable energy and nuclear energy to produce green and sustainable energy for a better future. An assumption might be made about the relationship between the types of energy resources used by a country and its development level. In this study, we investigate the connection between the economic development of the countries and energy consumption patterns using a machine learning approach. We used a custom data set created by the combination of two separate data sets to deduce that relation by using the clustering method. It can be seen that developed countries are prone to cease the usage of fossil fuels and increase the energy produced by renewable and nuclear energy while underdeveloped or developing countries still rely mostly on fossil fuels. This trend among the developed countries has increased by the year. Additionally, we also wanted to see where Turkey stands among the selected countries and make an observation on its energy and economic development progress. © 2022 IEEE.
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    Strain energy prediction of single wall carbon nanotubes using general regression neural network and adaptive neuro - Fuzzy Inference System
    (Taylor & Francis Inc, 2023) Eyecioğlu, Önder; Kayışlı, Korhan; Beken, Murat
    Single Wall Carbon Nanotubes (SWCNTs) play crucial roles in the field of nanotechnology research and applications. Due to their quantum mechanical nature and the intricate structure of SWCNTs, performing direct or indirect experimental processes can be exceedingly challenging. Simulation methods like Tight-Binding Molecular Dynamics (TBMD) can serve as viable alternatives to experimentation. However, it's worth noting that these methods often demand extensive computational runtime. To address this computational time challenge, artificial intelligence algorithms such as the General Regression Neural Network (GRNN) and the Adaptive Neuro Fuzzy Interface System (ANFIS) have been proposed in this study. These models aim to calculate the energetic properties of SWCNTs more efficiently, offering practical and quicker predictive methods with reduced computational workloads. The study's findings demonstrate a strong correlation between the predicted energy values of SWCNTs using GRNN and ANFIS models and the results obtained through TBMD simulations. Consequently, it is believed that these models can be suitable and effective approaches for computing the energetic properties of SWCNTs.