Yazar "Sözen, Adnan" seçeneğine göre listele
Listeleniyor 1 - 18 / 18
Sayfa Başına Sonuç
Sıralama seçenekleri
Öğe Calculation for the thermodynamic properties of an alternative refrigerant (R508b) using artificial neural network(Pergamon-Elsevier Science Ltd, 2007) Sözen, Adnan; Özalp, Mehmet; Arcaklioğlu, ErolThis study proposes a alternative approach based on artificial neural networks (ANNs) to determine the thermodynamic properties - specific volume, enthalpy and entropy - of an alternative refrigerant (R508b) for both saturated liquid-vapor region (wet vapor) and superheated vapor region. In the ANN, the back-propagation learning algorithm with two different variants, namely scaled conjugate gradient (SCG) and Levenberg-Marquardt (LM), and Logistic Sigmoid transfer function were used to determine the best approach. The most suitable algorithm and with appropriate number of neurons (i.e. 7) in the hidden layer is found to be the LM algorithm which has provided the minimum error. For wet vapor region, R-2 values - which are errors known as absolute fraction of variance - are 0.983495, 0.969027, 0.999984, 0.999963, 0.999981, and 0.999975, for specific volume, enthalpy and entropy for training and testing, respectively. Similarly, for superheated vapor, they are: 0.995346, 0.996947, 0.999996, 0.999997, 0.999974, and 0.999975, for training and testing, respectively. According to the regression analysis results, R-2 values are 0.9312, 0.9708, 0.9428, 0.9343, 0.967 and 0.9546 for specific volume, enthalpy and entropy for wet vapor region and superheated vapor, respectively. The comparisons of the results suggest that, ANN provided results comfortably within the acceptable range. This study, deals with the potential application of the ANNs to represent PVTx (pressure-specific volume-temperature-vapor quality) data. Therefore, reducing the risk of experimental uncertainties and also removing the need for complex analytic equations requiring long computational time and efforts. (c) 2006 Elsevier Ltd. All rights reserved.Öğe Comparative study of mathematical and experimental analysis of spark ignition engine performance used ethanol-gasoline blend fuel(Pergamon-Elsevier Science Ltd, 2007) Serdar Yücesu, H.; Sözen, Adnan; Topgul, Tolga; Arcaklioğlu, ErolThis study consists of two cases: (i) The experimental analysis: Ethanol obtained from biomass can be used as a fuel in spark ignition engines. As renewable energy source ethanol, due to the high octane number, low emissions and high engine performance is preferred alternative fuel. First stage of this study, ethanol-unleaded gasoline blends (E10, E20, E40 and E60) were tested in a single cylinder, four-stroke spark ignition and fuel injection engine. The tests were performed by varying the ignition timing, relative air-fuel ratio (RAFR) and compression ratio at a constant speed of 2000 rpm and at wide open throttle (WOT). Effect of ethanol-unleaded gasoline blends and tests variables on engine torque and specific fuel consumption were examined experimentally. (ii) The mathematical modeling analysis: The use of ANN has been proposed to determine the engine torque and specific fuel consumption based on the ignition timing, RAFR and compression ratio at a constant speed of 2000 rpm and at WOT for different fuel densities using results of experimental analysis. The back-propagation learning algorithm with two different variants and logistic sigmoid transfer function were used in the network. In order to train the neural network, limited experimental measurements were used as training and test data. The best fitting training data set was obtained Levenberg-Marquardt (LM) algorithm with five neurons in the hidden layer, which made it possible to the engine torque and specific fuel consumption with accuracy at least as good as that of the experimental error, over the whole experimental range. After training, it was found the RZ values are 0.999996 and 0.999991 for, the engine torque and specific fuel consumption, respectively. Similarly, these values for testing data are 0.999977 and 0.999915, respectively. As seen from the results of mathematical modeling, the calculated engine torque and specific fuel consumption are obviously within acceptable uncertainties. (c) 2006 Elsevier Ltd. All rights reserved.Öğe Determination of residual stresses based on heat treatment conditions and densities on a hybrid (FLN2-4405) powder metallurgy steel using artificial neural network(Elsevier Sci Ltd, 2007) Kafkas, Firat; Karataş, Çetin; Sözen, Adnan; Arcaklioglu, Erol; Saritaş, SüleymanThis paper presents a new approach based on artificial neural networks (ANNs) to determine the residual stresses in PM steel based nickel (FLN2-4405). This study consists of two cases: (i) The experimental analysis: The measurements of residual stresses were carried out by electrochemical layer removal technique. The values and distributions of residual stresses occurring in PM steel processed under various densities (6.8, 7.05, 7.2 and 7.4 g/cm(3)) and heat treatment conditions (sintered at 2050 degrees F, sintered at 2300 degrees F, quenching-tempered, and sinter-hardened) were determined. In most of the experiments, tensile residual stresses were recorded in surface of samples. The residual stress distribution on the surface of the PM steels is affected by the heat treatment conditions and density. Maximum values of residual stresses on the surface were observed sinter hardened condition and 7.4 g/cm(3) density. (ii) The mathematical modeling analysis: The use of ANN has been proposed to determine the residual stresses based on heat treatment conditions and densities in PM steel using results of experimental analysis. The back propagation learning algorithm with two different variants and logistic sigmoid transfer function were used in the network. In order to train the neural network, limited experimental measurements were used as training and test data. The best fitting training data set was obtained with four and five neurons in the hidden layer, which made it possible to predict residual stress with accuracy at least as good as that of the experimental error, over the whole experimental range. After training, it was found the R 2 values are 0.999244, 0.999025, 0.999664 and 0.999322 for sintered at 2050 degrees F, sintered at 2300 degrees F, quenching-tempered, and sinter-hardened, respectively. Similarly, these values for testing data are 0.998354, 0.99706, 0.999607 and 0.999205, respectively. As seen from the results of mathematical modeling, the calculated residual stresses are obviously within acceptable uncertainties. (c) 2006 Elsevier Ltd. All rights reserved.Öğe Estimation of GHG Emissions in Turkey Using Energy and Economic Indicators(Taylor & Francis Inc, 2009) Sözen, Adnan; Gülseven, Zafer; Arcaklıoğlu, ErolThe greenhouse gas emissions (total greenhouse gas, CO2, CO, SO2, NO2, E (emissions of non-methane volatile organic compounds)) covered by the Kyoto Protocol are weighted by their global warming potentials and aggregated to give total emissions in CO2 equivalents. The subject in this study is to obtain equations to predict the greenhouse gas emissions of Turkey using energy and economic indicators by the artificial neural network approach. In this study, three different models were used in order to train the artificial neural network. In the first of them sectoral energy consumption (Model 1), in the second of them gross domestic product (Model 2), and in the third of them gross national product (Model 3) are used input layer of the network. The greenhouse gas emissions are in the output layer for all models. The aim of using different models is to estimate the greenhouse gas emissions with high confidence to make correct investments in Turkey. The obtained equations are used to determine the future level of the greenhouse gas emissions and take measures to control the share of sectors in total emission. According to artificial neural network results, the maximum mean absolute percentage errors for Model 1 were found to be 0.147151, 0.066716, 0.181901, 0.105146, 0.124684, and 0.158157 for greenhouse gas, SO2, NO2, CO, E, and CO2, about training data with Levenberg-Marquardt algorithm by eight neurons, respectively. Similarly, for Model 2 these values were found to be 0.487212, 0.701938, 0.718754, 0.232667, 0.272346, and 0.575421, respectively. And finally, for Model 3, these values were found to be 0.126728, 0.115135, 0.069296, 0.214888, 0.080358, and 0.179481, respectively. R2 values are obtained very close to 1 for all models. The artificial neural network approach shows greater accuracy for estimating the greenhouse gas emissions.Öğe Estimation of Net Energy Consumption in Turkey Using Different Indicators(Taylor & Francis Inc, 2009) Sözen, Adnan; Arcaklıoğlu, Erol; Tekiner, ZaferThe main subject in this study is to obtain equations to predict net energy consumption of Turkey using energy sources and economic indicators by artificial neural network approach in order to determine the 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 artificial neural network. In the first model (Model 1), energy sources (e.g., natural gas, lignite, coal, hydraulic); in the second model (Model 2), gross national product; and in the third model (Model 3), gross domestic product, are used for the input layer of the network. The net energy consumption is in the output layer for all models. In order to train the neural network, economic and energy data for the last 37 years (1968-2005) is used in network for all models. The aim of using different models is to estimate the net energy consumption with high confidence to plan for future projections. The maximum mean absolute percentage error was found to be 1.992262, 1.110525, and 1.122048 for Model 1, Model 2, and Model 3, respectively. R2 values are obtained (0.999558, 0.999903, and 0.999903 for training data of Model 1, Model 2, and Model 3, respectively). The artificial neural network approach shows greater accuracy for evaluating net energy consumption based on economic indicators. Also, obtained results in this study were compared with results of similar studies using various techniques.Öğe Exergy analysis of an ejector-absorption heat transformer using artificial neural network approach(Pergamon-Elsevier Science Ltd, 2007) Sözen, Adnan; Arcaklioğlu, ErolThis paper proposes artificial neural networks (ANNs) technique as a new approach to determine the exergy losses of an ejector-absorption heat transformer (EAHT). Thermodynamic analysis of the EAHT is too complex due to complex differential equations and complex simulations programs. ANN technique facilitates these complicated situations. This study is considered to be helpful in predicting the exergetic performance of components of an EAHT prior to its setting up in a thermal system where the working temperatures are known. The best approach was investigated using different algorithms with developed software. The best statistical coefficient of multiple determinations (R-2-value) for training data equals to 0.999715, 0.995627, 0.999497, and 0.997648 obtained by different algorithms with seven neurons for the non-dimensional exergy losses of evaporator, generator, absorber and condenser, respectively. Similarly these values for testing data are 0.999774, 0.994039, 0.999613 and 0.99938, respectively. The results show that this approach has the advantages of computational speed, low cost for feasibility, rapid turnaround, which is especially important during iterative design phases, and easy of design by operators with little technical experience. (c) 2006 Elsevier Ltd. All rights reserved.Öğe Experimental and theoretical investigations of mouldability for feedstocks used in powder injection moulding(2007) Karataş, Çetin; Sözen, Adnan; Arcaklıoğlu, Erol; Ergüney, SamiExperimental and theoretical analyses of mouldability for feedstocks used in powder injection moulding are performed. This study covers two main analyses. (i) The experimental analysis: the barrel temperature, injection pressure, and flow rate are factors for powder injection moulding (PIM). Powder-binder mixture used as feedstock in PIM requires a little more attention and sensitivity. Obtaining the balance among pressure, temperature, and especially flow rate is the most important aspect of undesirable conclusions such as powder-binder separation, sink marks, and cracks in moulded party structure. In this study, available feedstocks used in PIM were injected in three different cavities which consist of zigzag form, constant cross-section, and stair form (in five different thicknesses) and their mouldability is measured. Because of the difference between material and binder, measured lengths were different. These were measured as 533 mm, 268 mm, 211 mm, and 150 mm in advanced materials trade marks Fe-2Ni, BASF firm Catamould A0-F, FN02, and 316L stainless steel, respectively. (ii) The theoretical analysis: the use of artificial neural network (ANN) has been proposed to determine the mouldability for feedstocks used in powder injection moulding using results of experimental analysis. The back-propagation learning algorithm with two different variants and logistic sigmoid transfer function were used in the network. In order to train the neural network, limited experimental measurements were used as training and test data. The best fitting training data set was obtained with three and four neurons in the hidden layer, which made it possible to predict yield length with accuracy at least as good as that of the experimental error, over the whole experimental range. After training, it was found that the R2 values are 0.999463, 0.999445, 0.999574, and 0.999593 for Fe-2Ni, BASF firm Catamould A0-F, FN02, and 316L stainless steel, respectively. Similarly, these values for testing data are 0.999129, 0.999666, 0.998612, and 0.997512, respectively. As seen from the results of mathematical modeling, the calculated yield lengths are obviously within acceptable uncertainties.Öğe Forecasting based on neural network approach of solar potential in Turkey(Pergamon-Elsevier Science Ltd, 2005) Sözen, Adnan; Arcakhoğlu, Erol; Özalp, Mehmet; Çağlar, NaciAs Turkey lies near the sunny belt between 36 and 42 degrees N latitudes, most of the locations in Turkey receive abundant solar energy. Average annual temperature is 18-20 degrees C on the south coast, falls down to 14-16 degrees C on the west coast, and fluctuates 4-18 degrees C in the central parts. The yearly average solar radiation is 3.6 kW h/m(2) day, and the total yearly radiation period is similar to 2610 h. The main focus of this study is put forward to solar energy potential in Turkey using artificial neural networks (ANN's). Scaled conjugate gradient (SCG), Pola-Ribiere conjugate gradient (CGP), and Levenberg-Marquardt (LM) learning algorithms and logistic sigmoid transfer function were used in the network. In order to train the neural network, meteorological data for last 4 years (2000-2003) from 12 cities (Canakkale, Kars, Hakkari, Sakarya, Erzurum, Zonguldak, Balikesir, Artvin, Corum, Konya, Siirt, Tekirdag) spread over Turkey were used as training (nine stations) and testing (three stations) data. Meteorological and geographical data (latitude, longitude, altitude, month, mean sunshine duration, and mean temperature) is used as input to the network. Solar radiation is the output. The maximum mean absolute percentage error was found to be less than 6.78% and R 2 values to be about 99.7768% for the testing stations. These values were found to be 5.283 and 99.897% for the training stations. The trained and tested ANN models show greater accuracy for evaluating solar resource posibilities in regions where a network of monitoring stations have not been established in Turkey. The predictions from ANN models could enable scientists to locate and design solar energy systems in Turkey and determine the best solar technology. (c) 2004 Elsevier Ltd. All rights reserved.Öğe Forecasting based on sectoral energy consumption of GHGs in Turkey and mitigation policies(Elsevier Sci Ltd, 2007) Sözen, Adnan; Gülseven, Zafer; Arcaklioglu, ErolRecently, global warming and its effects have become one of the most important themes in the world. Under the Kyoto Protocol, the EU has agreed to an 8% reduction in its greenhouse gas (GHG) emissions by 2008-2012. The GHG emissions (total GHG, CO2, CO, SO2, NO2, E (emissions of non-methane volatile organic compounds)) covered by the Protocol are weighted by their global warming potentials (GWPs) and aggregated to give total emissions in CO2 equivalents. The main subject in this study is to obtain equations by the artificial neural network (ANN) approach to predict the GHGs of Turkey using sectoral energy consumption. The equations obtained are used to determine the future level of the GHG and to take measures to control the share of sectors in total emission. According to ANN results, the maximum mean absolute percentage error (MAPE) was found as 0.147151, 0.066716, 0.181901, 0.105146, 0.124684, and 0.158157 for GHG, SO2, NO2, CO, E, and CO2, respectively, for the training data with Levenberg-Marquardt (LM) algorithm by 8 neurons. R-2 values are obtained very close to 1. Also, this study proposes mitigation policies for GHGs. (C) 2007 Elsevier Ltd. All rights reserved.Öğe Forecasting net energy consumption using artificial neural network(Taylor & Francis Inc, 2006) Sözen, Adnan; Akcayol, M. Ali; Arcaklıoğlu, ErolThe main goal of this study is to develop the equations for forecasting net energy consumption (NEC) using the artificial neural network (ANN) technique in order to determine the future level of the energy consumption in Turkey. Logistic sigmoid transfer function was used in the network. In order to train the neural network, population, and gross generation, installed capacity and years is used in input layer of network. The net energy consumption is in output layer. The input values in 1965, 1981, and 1997 are only used as test data to confirm this method. The statistical coefficient of multiple determinations (R-2-value) is equal to 0.9999 and 1 for training and test data, respectively. According to the results, the NEC using the ANN technique has been obviously predicted within acceptable errors. Apart from reducing the whole time required, the importance of the ANN approach is possible to find solutions that make energy applications more viable and thus more attractive to potential users. It is also expected that this study will be helpful in developing highly applicable and productive planning for energy policies.Öğe Formulation based on artificial neural network of thermodynamic properties of ozone friendly refrigerant/absorbent couples(Pergamon-Elsevier Science Ltd, 2005) Sözen, Adnan; Arcaklıoğlu, Erol; Özalp, MehmetThis paper presents a new approach based on artificial neural networks (ANNs) to determine the properties of liquid and two phase boiling and condensing of two alternative refrigerant/absorbent couples (methanol/LiBr and methanol/LiCl). These couples do not cause ozone depletion and use in the absorption thermal systems (ATSs). ANN's are able to learn the key information patterns within multidimensional information domain. ANNs operate such as a 'black box' model, requiring no detailed information about the system. On the other hand, they learn the relationship between the input and the output. In order to train the neural network, limited experimental measurements were used as training data and test data. In this study, in input layer, there are temperatures in the range of 298-498 K, pressures (0.1-40 MPa) and concentrations of 2%, 7%, 12% of the couples; specific volume is in output layer. The back-propagation learning algorithm with three different variants, namely scaled conjugate gradient (SCG), Pola-Ribiere conjugate gradient (CGP), and Levenberg-Marquardt (LM), and logistic sigmoid transfer function were used in the network so that the best approach can find. The most suitable algorithm and neuron number in the hidden layer are found as SCG with 8 neurons. For this number level, after the training, it is found that maximum error is less than 3%, average error is about 1% and R-2 value are 99.999%. As seen from the results obtained the thermodynamic equations for each pair by using the weights of network have been obviously predicted within acceptable errors. This paper shows that values predicted with ANN can be used to define the thermodynamic properties instead of approximate and complex analytic equations. (c) 2004 Elsevier Ltd. All rights reserved.Öğe Modelling of Turkey's net energy consumption using artificial neural network(2005) Sözen, Adnan; Arcaklioğlu, Erol; Özkaymak, MehmetThe main goal of this study is to develop the equations for forecasting net energy consumption (NEC) using artificial neural network (ANN) technique in order to determine the future level of the energy consumption in Turkey. Two different models were used in order to train the neural network: (i) Population, gross generation, installed capacity and years are used in input layer of network (Model 1). (ii) Energy sources are used in input layer of network (Model 2). The NEC is in output layer for two models. R2 values for training data are equal to 0.99944 and 0.99913, for Model 1 and Model 2, respectively. Similarly, R2 values for testing data are equal to 0.997386 and 0.999558 for Model 1 and Model 2, respectively. According to the results, the NEC prediction using ANN technique will be helpful in developing highly applicable and productive planning for energy policies. Copyright © 2005 Inderscience Enterprises Ltd.Öğe Modelling of yield length in the mould of commercial plastics using artificial neural networks(Elsevier Sci Ltd, 2007) Karataş, Çetin; Sözen, Adnan; Arcaklioglu, Erol; Ergüney, SamiAccording to the yield length of a plastic, whether a mould is filled fully or not can be estimated. In this study, a new formula based on various injection parameters was developed to determine the yield length in plastic moulding of the commercial plastics, the most widely used ones are low-density polyethylene, high-density polyethylene, polystyrene and polypropylene, by artificial neural network (ANN). This study covers two main objectives: (i) the yield properties of plastics have been investigated at various injection parameters (cylinder temperature, injection pressure, injection flow rate and mould temperature) in a mould including spiral chutes as experimental; (ii) the yield length of the commercial plastics based on various measured injection parameters (cylinder temperature, injection pressure, injection flow rate and mould temperature) in a mould was described by ANN using experimental data. Some experimental data were used as test data, and these values were not used for training. Scaled conjugate gradient and Levenberg-Marquardt (LM) learning algorithms and logistic sigmoid transfer function were used in the network. The results show that the maximum mean absolute percentage error (MAPE) was found to be 1.574969% and R-2 values 0.99964. The best approach was found in the LM algorithm with six neurons. The MAPE and R-2 for testing data were 1.849 and 0.9995, respectively, similar to algorithm and neurons. This study is considered to be helpful in predicting the yield length in the mould whose injection parameters are known. The present method greatly reduces the time required by design engineers to find the optimum solution and in many cases reaches a solution that could not be easily obtained from simple modeling programs. (c) 2005 Elsevier Ltd. All rights reserved.Öğe Performance parameters of an ejector-absorption heat transformer(Elsevier Sci Ltd, 2005) Sözen, Adnan; Arcaklioğlu, Erol; Özalp, Mehmet; Yücesu, SerdarEjector-absorption heat transformers (EAHTs) are attractive for increasing a solar-pond's temperature and for recovering low-level waste-heat. Thermodynamic analysis of the performance of an EAHT is complicated due to the associated complex differential equations and simulation programs. This paper proposes the use of artificial neural-networks (ANNs) as a new approach to determine the performance parameters, as functions of only the working temperatures of the EAHT, which is used to increase the solar pond's temperature under various working conditions. Thus, this study is helpful in predicting the performance of an EAHT where the temperatures are known. Scaled conjugate gradient (SCG) and Levenberg-Marquardt (LM) learning algorithms and a logistic sigmoid transfer-function were used in the network. The best approach was investigated for performance parameters with developed software using various algorithms. The best statistical coefficients of multiple determinations (R-2-values) equal 0.99995, 0.99997 and 0.99995 for the coefficient of performance (COP), exergetic coefficient of performance (ECOP) and circulation ratio (F), respectively obtained by the LM algorithm with seven neurons. In the comparison of performances, results obtained via analytic equations and by means of the ANN, the COP, ECOP and F for all working situations differ by less than 1.05%, 0.7% and 3.07%, respectively. These accuracies are acceptable in the design of the EAHT. The ANN approach greatly reduces the time required by design engineers to find the optimum solution. Apart from reducing the time required, it is possible to find solutions that make solar-energy applications more viable and thus more attractive to potential users. Also, this approach has the advantages of high computational speed, low cost for feasibility, rapid turn-around, which is especially important during iterative design phases, and ease of design by operators with little technical experience. (C) 2004 Elsevier Ltd. All rights reserved.Öğe Prediction of net energy consumption based on economic indicators (GNP and GDP) in Turkey(Elsevier Sci Ltd, 2007) Sözen, Adnan; Arcaklıoğlu, ErolThe 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. R 2 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. (D 2007 Elsevier Ltd. All rights reserved.Öğe Solar potential in Turkey(Elsevier Sci Ltd, 2005) Sözen, Adnan; Arcaklıoğlu, ErolMost of the locations in Turkey receive abundant solar-energy, because Turkey lies in a sunny belt between 36degrees and 42 degreesN latitudes. Average annual temperature is 18 to 20 degreesC on the south coast, falls to 14-16 degreesC on the west coat, and fluctuates between 4 and 18 degreesC in the central parts. The yearly average solar-radiation is 3.6 kWh/m(2) day, and the total yearly radiation period is similar to2610 It. In this study, a new formulation based on meteorological and geographical data was developed to determine the solar-energy potential in Turkey using artificial neural-networks (ANNs). Scaled conjugate gradient (SCG), Pola-Ribiere conjugate gradient (CGP), and Levenberg-Marquardt (LM) learning algorithms and logistic sigmoid (logsig) transfer function were used in the networks. Meteorological data for last four years (2000-2003) from 12 cities (Canakkale, Kars, Hakkari, Sakarya, Erzurum, Zonguldak, Balikesir, Artvin, Corum, Konya, Siirt, and Tekirdag) spread over Turkey were used in order to train the neural-network. Meteorological and geographical data (latitude, longitude, altitude, month, mean sunshine-duration, and mean temperature) are used in the input layer of the network. Solar-radiation is in the output layer. The maximum mean absolute percentage error was found to be less than 3.832% and R-2 values to be about 99.9738% for the selected stations. The ANN models show greater accuracy for evaluating solar-resource posibilities in regions where a network of monitoring stations has not been established in Turkey. This study confirms the ability of the ANN to predict solar-radiation values accurately. (C) 2004 Elsevier Ltd. All rights reserved.Öğe Solar-energy potential in Turkey(Elsevier Sci Ltd, 2005) Sözen, Adnan; Arcaklıoğlu, Erol; Özalp, Mehmet; Kanıt, E. GalipIn this study, a new formula based on meteorological and geographical data was developed to determine the solar-energy potential in Turkey using artificial neural-networks (ANNs). Scaled conjugate gradient (SCG) and Levenberg-Marquardt (LM) learning algorithms and a logistic sigmoid transfer function were used in the network. Meteorological data for the last four years (2000 2003) from 18 cities (Bilecik, Kirsehir, Akhisar, Bingol, Batman, Bodrum, Uzunkopru", Sile, Bartin, Yalova, Horasan, Polath, Malazgirt, Koycegiz, Manavgat, Dortyol, Karatas and Birecik) spread over Turkey were used as data in order to train the neural network. Meteorological and geographical data (latitude, longitude, altitude, month, mean sunshine duration, and mean temperature) were used in the input layer of the network. Solar radiation is the output layer. One-month test data for each city was used, and these months data were not used for training. The results show that the maximum mean absolute percentage error (MAPE) was found to be 3.448% and the R-2 value 0.9987 for Polath. The best approach was found for Kirsehir (MAPE = 1.2257, R-2 = 0.9998). The MAPE and R-2 for the testing data were 3.3477 and 0.998534, respectively. The ANN models show greater accuracy for evaluating solar-resource possibilities in regions where a network of monitoring stations has not been established in Turkey. This study confirms the ability of the ANN to predict solar-radiation values precisely. (c) 2004 Elsevier Ltd. All rights reserved.Öğe Turkey's net energy consumption(Elsevier Sci Ltd, 2005) Sözen, Adnan; Arcaklıoğlu, Erol; Özkaymak, MehmetThe main goal of this study is to develop the equations for forecasting net energy consumption (NEC) using an artificial neural-network (ANN) technique in order to determine the future level of energy consumption in Turkey. In this study, two different models were used in order to train the neural network. In one of them, population, gross generation, installed capacity and years are used in the input layer of the network (Model 1). Other energy sources are used in input layer of network (Model 2). The net energy consumption is in the output layer for two models. Data from 1975 to 2003 are used for the training. Three years (1981, 1994 and 2003) are used only as test data to confirm this method. The statistical coefficients of multiple determinations (R-2-value) for training data are equal to 0.99944 and 0.99913 for Models 1 and 2, respectively. Similarly, R-2 values for testing data are equal to 0.997386 and 0.999558 for Models 1 and 2, respectively. According to the results, the net energy consumption using the ANN technique has been predicted with acceptable accuracy. Apart from reducing the whole time required, with the ANN approach, it is possible to find solutions that make energy applications more viable and thus more attractive to potential users. It is also expected that this study will be helpful in developing highly applicable energy policies. (c) 2004 Elsevier Ltd. All rights reserved.
