Yazar "Bilgili, Mehmet" seçeneğine göre listele

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    A comparison of the performance characteristics of large 2 MW and 3 MW wind turbines on existing onshore wind farms
    (Techno-Press, 2021) Bilgili, Mehmet; Ekinci, Firat; Demirdelen, Tugce
    The aim of the current study is to compare the performance of large 2 MW and 3 MW wind turbines operating on existing onshore wind farms using Blade Element Momentum (BEM) theory and Angular Momentum (AM) theory and illustrate the performance characteristic curves of the turbines as a function of wind speed (U-infinity). To achieve this, the measurement data obtained from two different Wind Energy Power Plants (WEPPs) located in the Hatay region of Turkey was used. Two different horizontal-axis wind turbines with capacities of 2 MW and 3 MW were selected for evaluation and comparison. The hub-height wind speed (U-D), turbine power output (P), atmospheric air temperature (T-atm) and turbine rotational speed (Omega) data were used in the evaluation of the turbine performance characteristics. Curves of turbine power output (P), axial flow induction factor (a), turbine rotational speed (omega), turbine power coefficient (C-P), blade tip speed ratio (lambda), thrust force coefficient (C-T) and thrust force (T) as a function of U-infinity were obtained for the 2 MW and 3 MW wind turbines and these characteristic curves were compared. Results revealed that, for the same wind speed conditions, the higher-capacity wind turbine (3 MW) was operating at higher turbine power coefficient rates, while rotating at lower rotational speed ratios than the lower capacity wind turbine (2 MW).
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    A new method for generating short-term power forecasting based on artificial neural networks and optimization methods for solar photovoltaic power plants
    (Springer Verlag, 2019) Demirdelen, Tugce; Ozge Aksu, Inayet; Esenboga, Burak; Aygul, Kemal; Ekinci, Firat; Bilgili, Mehmet
    In recent times, solar PV power plants have been used worldwide due to their high solar energy potential. Although the PV power plants are highly preferred, the main disadvantage of the system is that the output power characteristics of the system are unstable. As PV power plant system is connected to the grid side, unbalanced power flow effects all systems controls. In addition, the load capacitys is not exactly known. For this reason, it has become an important issue to be known correctly in PV output power and their time-dependent changes. The main aim of this work is to eliminate power plant instability due to the output power imbalance. For the short-term, power prediction is estimated by real-time data of 1 MW PV power plant in use. Estimation power data are compared with real-time data and precision of the proposed method is demonstrated. In the first phase, traditional artificial intelligence algorithms are used. Then, these algorithms are trained with swarm based optimization methods and the performance analyses are presented in detail. Among all the algorithms used, the algorithm with the lowest error is determined. Thus, this study provides useful information and techniques to help researchers who are interested in planning and modeling PV power plants. © Springer Nature Singapore Pte Ltd. 2019.
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    A novel hybrid metaheuristic optimization method to estimate medium-term output power for horizontal axis wind turbine
    (Sage Publications Ltd, 2019) Ekinci, Firat; Demirdelen, Tugce; Aksu, Inayet Ozge; Aygul, Kemal; Esenboga, Burak; Bilgili, Mehmet
    The increasing damage caused by fossil fuels has made it a necessity for new and clean energy sources. In recent years, the use of wind energy from renewable energy sources has increased, which is a new and clean energy source. Wind energy is everywhere in nature. The wind speed changes depending on time. Thus, the wind power is unstable. In order to keep this disadvantage at a minimum level, future power estimation studies have been carried out. In these studies, different methods and algorithms are applied to estimate short and medium term in wind power. In this study, artificial neural network, particle swarm optimization and firefly algorithm (FA) as a new method are used for the first time in predicting wind power. As input data, temperature, wind speed and rotor speed the data recorded in the SCADA in wind turbines are used to predict medium-term wind speed and also wind power. Each method is compared in detail and their performances are revealed.
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    A Strategic Solution to Turkey’s Intermediate Goods Problem: Ceyhan Energy Specialized Industrial Zone
    (2019) Ekinci, Fırat; Bilgili, Mehmet
    There is an on-going demand for energy from hydrocarbon compounds and intermediate goods in meetingthe need for both production and intermediate products. The European Union and Turkey’s need for energyand intermediate goods are provided by petroleum and natural gas imported from geographically nearestenergy regions through pipelines. In this study, a strategic solution to demand for energy in Turkey isproposed. Furthermore, information about the energy status, the chemical sector and intermediate goodsimports of Turkey is provided. Considerations are proposed on the capabilities of petrochemical plants tobe established in Ceyhan Energy Specialized Industrial Zone (CESIZ) as an outlet of petroleum fromAzerbaijan and Iraq and a potential logistic energy base in the Eastern Mediterranean region. CeyhanEnergy Specialized Industrial Zone, as the first energy zone in Turkey and the nearby region, is introducedand the investors are briefed on the petrochemical facilities planned to be established in the zone. Theimportance of intermediate chemical products produced by petrochemical plants to be established inCeyhan Energy Specialized Industrial Zone in reducing the country’s foreign trade deficit is revealed. Dueto lack of studies conducted on CESIZ in the literature, this study would contribute to potential investorsand academicians.
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    ENERGY AND EXERGY ANALYSIS OF A VAPOR ABSORPTION REFRIGERATION SYSTEM IN AN INTERCITY BUS APPLICATION
    (2019) Kurtulmuş, Nazım; Bilgili, Mehmet; Şahin, Beşir
    A Vapor Absorption Refrigeration (VAR) system driven by the exhaust gas waste heat received from the internal combustion engine of an intercity bus is modeled and analyzed for air conditioning the intercity bus cabin under different operating parameters. Initially, the hourly comfort cooling load of the intercity bus is calculated for a cooling season spanning five months between May and October in Turkey. After determining the capacity of heat source sufficiency for air conditioning the intercity bus, energy and exergy analyses of the VAR system are conducted, then designed and compared with the vapor compression refrigeration system in respect to the effect of fuel consumption. The results show that approximately 4,489 kg/year of fuel can be saved by using the VAR system driven by an exhaust gas waste heat in an intercity bus. The maximum coefficient of performance (COP) of the VAR system is obtained as 0.78 at 5 a.m. in May, and the maximum total exergy destruction for the VAR system is obtained as 15.25 kW at 4 p.m. in July. Lastly, the specific time is selected to investigate the effect of operating and environmental parameters on the VAR system.
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    Long short-term memory (LSTM) neural network and adaptive neuro-fuzzy inference system (ANFIS) approach in modeling renewable electricity generation forecasting
    (Taylor & Francis Inc, 2021) Bilgili, Mehmet; Yildirim, Alper; Ozbek, Arif; Celebi, Kerimcan; Ekinci, Firat
    Renewable energy sources are developing rapidly worldwide because they are unlimited and permanent, available in every country and also eliminate foreign dependency. In this respect, accurate renewable electricity generation (REG) forecasting is essential in a country's energy planning in relation to its development. In this study, two different data-driven methods such as adaptive neuro-fuzzy inference system (ANFIS) with fuzzy c-means (FCM) and long short-term memory (LSTM) neural network were applied to perform one-day ahead short-term REG forecasting. In addition, short-term hydropower electricity generation (HEG), geothermal electricity generation (GEG), and bioenergy electricity generation (BEG) forecasting were also made using these methods. The correlation coefficient (R), root-mean-square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) were used as evaluation criteria. The values predicted by the ANFIS-FCM and LSTM models were compared with the actual values by evaluating their errors. According to the test results obtained in terms of MAPE evaluation criteria, the best estimation model was obtained for GEG. The lowest MAPE values were found to be 7.20%, 7.46%, 1.63%, and 2.46% for REG, HEG, GEG, and BEG estimates, respectively. The results showed that both ANFIS and LSTM models presented satisfying performances in daily REG prediction, and the ANFIS and LSTM models gave almost identical results.
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    Modelling of wind turbine power output by using ANNs and ANFIS techniques
    (Institute of Electrical and Electronics Engineers Inc., 2017) Ekinci, Firat; Demirdelen, Tu?çe; Bilgili, Mehmet
    In this study, artificial neural network (ANN) and adaptive neuro fuzzy inference system (ANFIS) were applied to estimate the wind turbine power output of a horizontal axis wind turbine. Hub-height wind speed, atmospheric air temperature and rotational speed values obtained from an operating wind power plant (WPP) were employed as input data in the model. According to the derived results, the mean absolute percentage error (MAPE) and correlation coefficient (R) values for the ANN model were determined as 4.41% and 0.9850, respectively, whereas the corresponding values for the ANFIS model were found as 2.19% and 0.9971, respectively. The obtained results showed that ANN and ANFIS models can be used to predict wind turbine power output in a simple, reliable and accurate way. © 2017 IEEE.
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    Modelling of Wind Turbine Power Output by Using ANNs and ANFIS Techniques
    (IEEE, 2017) Ekinci, Firat; Demirdelen, Tugce; Bilgili, Mehmet
    In this study, artificial neural network (ANN) and adaptive neuro fuzzy inference system (ANFIS) were applied to estimate the wind turbine power output of a horizontal axis wind turbine. Hub-height wind speed, atmospheric air temperature and rotational speed values obtained from an operating wind power plant (WPP) were employed as input data in the model. According to the derived results, the mean absolute percentage error (MAPE) and correlation coefficient (R) values for the ANN model were determined as 4.41% and 0.9850, respectively, whereas the corresponding values for the ANFIS model were found as 2.19% and 0.9971, respectively. The obtained results showed that ANN and ANFIS models can be used to predict wind turbine power output in a simple, reliable and accurate way.
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    Performance Analysis of Solar Powered Absorption Refrigeration System for Mersin Province
    (2016) Şahin, Beşir; Bilgili, Mehmet; Çetingöz, Altan; Kurtulmuş, Nazım
    Bu çalışmada, Mersin iline ait saatlik atmosfer hava sıcaklığı ve güneş ışınım verileri kullanılarak güneş enerji destekli absorpsiyonlu soğutma (SPAR) sisteminin performans analizi yapılmıştır. Güneş enerji destekli absorpsiyonlu soğutma sisteminin tasarımında soğutucu-soğurgan çifti olarak amonyak-su (NH3-H2O) ve vakum tüplü güneş kollektörü tercih edilmiştir. İlk olarak, seçilen binanın saatlik soğutma yükü hesaplanmıştır. Sonra, bu soğutma yüküne göre sistemin termodinamik analizi yapılmış ve soğutulacak bölge için gerekli güneş kollektör alanı hesaplanmıştır. Elde edilen sonuçlara göre maksimum etkinlik katsayısı, (COP)Mayıs ayında, minimum etkinlik katsayısı, (COP) ise Temmuz ve Ağustos aylarında elde edilmiştir. Birim alana gelen maksimum güneş ışınım değeri, 23 Haziran günü saat 13.00'de 0,878 kW/m2olduğunda, COP değeri 0,434 olarak hesaplanmıştır. Sonuç olarak, 30 m2'lik soğutulacak bölge için optimum güneş kollektör alanı 50 m2 olarak hesaplanmıştır
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    The role of hydropower installations for sustainable energy development in Turkey and the world
    (Pergamon-Elsevier Science Ltd, 2018) Bilgili, Mehmet; Bilirgen, Harun; Ozbek, Arif; Ekinci, Firat; Demirdelen, Tugce
    Hydropower has the largest share among renewable energy sources in the world, supplying more than 16.6% of total global electricity to over 160 countries around the world. Global hydropower capacity increased to approximately 1096 GW with the addition of 25 GW of new hydropower capacity in 2016. With a 216 TWh per year generation capacity, Turkey's hydropower potential is the largest in Europe. The increased rate of installed capacity in Turkey was ranked 7th in the world in 2016 with an annual installed hydroelectric capacity of 0.8 GW. The main objective of this paper is to review the developments of hydropower installations around the world and in Turkey with an emphasis on the potential of small scale hydropower systems such as waterwheels in utilizing low head water flow for household electricity usage. In the first part of this study, the growth of worldwide hydropower capacity is reviewed and the countries with the largest installed and new built hydropower capacities are reported. In the second part of this study, the current status of Turkey's hydropower plants is discussed in detail with respect to annual regional rainfall, gross water mass flow and potential of Turkey's major water basins to demonstrate the potential energy output that can be harnessed from small-scale systems implemented in low-head water sources. In addition, the most recent information on Turkey's electricity generation and consumption rates are reported. (C) 2018 Elsevier Ltd. All rights reserved.
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    Thermodynamic Analysis of an Intercity Bus Air-Conditioning System Working with HCFC, HFC, CFC and HC Refrigerants
    (2019) Bilgili, Mehmet; Çardak, Ediz; Aktaş, Arif Emre; Ekinci, Fırat
    The purpose of this study is to perform thermodynamic analysis of an intercitybus air-conditioning (ICBAC) system working with hydrochlorofluorocarbon(HCFC), hydrofluorocarbon (HFC), chlorofluorocarbon (CFC) andhydrocarbon (HC) refrigerants. For this aim, an intercity bus with a busload of56 passengers, which is thought to motion in the Adana province of Turkey,was selected. R404A, R410A, R502, R507, R22, R290, R134a, R500 andR600a were selected as different HCFC/HFC/CFC/HC refrigerant types in theICBAC system. Useful and reversible works of compressor, coefficient ofperformance (COP), exergy efficiency and exergy destructions of the entireICBAC system and its each sub-unit were obtained. The results showed thatthe performance of the ICBAC system was significantly influenced bychanging the refrigerants. However, R600a refrigerant based on hydrocarbonwas found as a refrigerant to have minimum total exergy destruction,maximum exergy efficiency and maximum COP compared to otherrefrigerants used in the ICBAC system with the same amount of cooling loadand the same climatic condition.
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    Thermodynamic analysis of the human body in different climate regions of Turkey to determine the comfort conditions with exergy method
    (Inderscience Enterprises Ltd, 2018) Ekinci, Firat; Bilgili, Mehmet
    Thermodynamic analysis of human body is studied to determine the thermal comfort conditions. In this context, exergy and energy analysis in the air conditioning of buildings is necessary for efficient use of energy. Thermodynamic analysis of human body is carried out for seven different climate regions of Turkey based on the use of meteorological parameters such as minimum and maximum and average monthly temperatures, atmospheric pressure and average relative humidity by implementing energy and exergy analysis. Human body, which is the subject of this study, was in the light activity level quantified as 58.2 W/m(2). Analysis results have indicated that the major energy loss with 39.28 W/m(2) is due to heat transfer with radiation, convection and conduction. Furthermore, the energy loss rates by water vapour diffusion from the skin, respiration, temperature difference and sweating were determined as 11.13 W/m(2), 4.29 W/m(2), 0.73 W/m(2) and 0.02 W/m(2), respectively. The maximum exergy consumption rate by the human body was 2.33 W/m(2) for the cold and semi dry - less humid climate region (CR-7), while the minimum exergy consumption rate was obtained as 0.91 W/m(2) for the hot and semi dry climate region (CR-1). Information presented in this study is expected to contribute to the design of air conditioning systems in order to choose more efficient energy systems.
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    Thermodynamic analysis of the human body in different climate regions of Turkey to determine the comfort conditions with exergy method
    (Inderscience Publishers, 2018) Ekinci, Firat; Bilgili, Mehmet
    Thermodynamic analysis of human body is studied to determine the thermal comfort conditions. In this context, exergy and energy analysis in the air conditioning of buildings is necessary for efficient use of energy. Thermodynamic analysis of human body is carried out for seven different climate regions of Turkey based on the use of meteorological parameters such as minimum and maximum and average monthly temperatures, atmospheric pressure and average relative humidity by implementing energy and exergy analysis. Human body, which is the subject of this study, was in the light activity level quantified as 58.2 W/m2. Analysis results have indicated that the major energy loss with 39.28 W/m2 is due to heat transfer with radiation, convection and conduction. Furthermore, the energy loss rates by water vapour diffusion from the skin, respiration, temperature difference and sweating were determined as 11.13 W/m2, 4.29 W/m2, 0.73 W/m2 and 0.02 W/m2, respectively. The maximum exergy consumption rate by the human body was 2.33 W/m2 for the cold and semi dry - less humid climate region (CR-7), while the minimum exergy consumption rate was obtained as 0.91 W/m2 for the hot and semi dry climate region (CR-1). Information presented in this study is expected to contribute to the design of air conditioning systems in order to choose more efficient energy systems. Copyright © 2018 Inderscience Enterprises Ltd.
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    Time series analysis of sea surface temperature change in the coastal seas of Turkiye
    (Pergamon-Elsevier Science Ltd, 2024) Bilgili, Mehmet; Durhasan, Tahir; Pinar, Engin
    Sea surface temperature (SST) is a crucial geophysical parameter in assessing heat exchange between the air and sea surface. Changes in SST and its accurate prediction play a pivotal role in explaining the global heat balance, determining atmospheric circulations, and constructing global climate models. This work aims to reveal a model for one-month-ahead forecasting of SST time series data along the Turkiye coasts, encompassing the Mediterranean, Aegean, Marmara, and Black Seas, and their long-term future forecast. A long short-term memory (LSTM) neural network and seasonal autoregressive integrated moving average (SARIMA) models are used for this purpose. The ECMWF ERA5 (0.5(o)x0.5 degrees) monthly SST dataset spanning the years 1970-2023 is used for model development. The results obtained from the LSTM and SARIMA models show that there will be an increasing trend in SSTs along these seacoasts until 2050. The SST measurements of 23.4 degrees C, 20.2 degrees C, 17.0 degrees C, and 16.6 degrees C recorded along the Mediterranean, Aegean, Marmara, and Black Seas in 2023 are expected to rise to 25.1 degrees C, 21.9 degrees C, 18.1 degrees C, and 18.8 degrees C, respectively, by 2050. These figures indicate an increase of 7.3%, 8.4%, 6.5%, and 13.3% in the SST values across these coastal seas over the next quarter century.