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    Estimating daily Global solar radiation with graphical user interface in Eastern Mediterranean region of Turkey
    (Pergamon-Elsevier Science Ltd, 2018) Yildirim, H. Basak; Celik, Ozgur; Teke, Ahmet; Barutcu, Burak
    It is important to have accurate knowledge on global solar radiation for optimum design of solar energy conversion systems. However, global solar radiation measurement is very rare in meteorological stations in all around the world. Hence, modeling global solar radiation is an crucial issue to fill the gaps in database and to estimate global solar radiation in places where global solar radiation measurement is not available. This paper presents a detailed description and analysis of various global solar radiation modeling methods. The efficiency and accuracy of ten models from different functions to estimate daily solar radiation in EMR are investigated. Also an optimized model based on Artificial Neural Network (ANN) method and Angstrom-Prescott model for the estimation of daily global solar radiation are presented. The essence of this paper is to investigate the performance of the ANN model and Angstrom-Prescott model in order to ensure the most feasible solution for estimating daily global solar radiation for Eastern Mediterranean Region (EMR) of Turkey. 11 years solar radiation data from 4 stations are utilized in training and testing of developed ANN model and parametric model which is based on Angstrm-Prescott method. In order to ensure a simple application of the model that most accurately predicts the desired target value, a new graphical user interface is developed with MATLAB GUI.
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    Estimating the monthly global solar radiation for Eastern Mediterranean Region
    (Pergamon-Elsevier Science Ltd, 2014) Teke, Ahmet; Yildirim, H. Basak
    Solar energy has an important role to achieve the goal of replacing fossil fuels and significant potential to reduce greenhouse gas emissions. Accurate information on solar radiation is very essential for engineers, architects and agriculturist to design the energy systems based on the solar source. The sunshine duration and air temperature are measured by most of the meteorological services in all over the world but global solar radiation measurements are very rare and some of the data are missing. At this point, estimation of solar radiation where stations are not available plays an important role. Different models have been developed in the literature to estimate solar radiation. Angstrom-Prescott sunshine based model is widely used one and also there are some other approaches based on Angstrom model in the literature. In this study, linear, quadratic and cubic empirical as a general equation for throughout the year are generated to estimate global solar radiation in Eastern Mediterranean Region (EMR) which covers the four main cities (Adana, Mersin, Antakya and Kahramanmaras) by using the meteorological data in the Turkish State Meteorological Services. Regression models were estimated for each month separately and annually by curve estimation techniques with MINITAB statistical program. The monthly linear, quadratic and cubic models for estimating monthly average global solar radiation are validated as well. Finally, a comparison between monthly models and general models is performed by statistical test methods such as R-2, MPE and MAPE. According to statistical test results, the use of cubic general model for EMR is recommended. (C) 2014 Elsevier Ltd. All rights reserved.
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    Evaluation and performance comparison of different models for the estimation of solar radiation
    (Pergamon-Elsevier Science Ltd, 2015) Teke, Ahmet; Yildirim, H. Basak; Celik, Ozgur
    The rapid depletion of energy resources, increasing energy demand and degeneration of ecological values need an urgent solution in this age. Solar energy as the most important energy resource has become part of the solution to the world's energy challenges. Solar radiation data that provides the information on how much energy strikes to the earth is needed for utilization, planning and designing of solar power plants. The measurement of solar radiation data is generally available in some specific areas due to difficulty in solar radiation measurements in terms of its initial and maintenance costs. Therefore, solar energy modelling techniques are becoming more and more important due to the increasing need for the design, performance evaluation and improvement of the solar energy applications. The primary aim of this paper is to overview solar radiation modelling techniques to identify optimum models available and to classify research fields in the literature. In this paper, the modelling techniques, data information, accuracy tests of models used in around 90 papers were reviewed and the most accurate models were suggested. (C) 2015 Elsevier Ltd. All rights reserved.
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    Evaluation of classical parametric models for estimating solar radiation in the Eastern Mediterranean region of Turkey
    (Pergamon-Elsevier Science Ltd, 2018) Yildirim, H. Basak; Teke, Ahmet; Antonanzas-Torres, Fernando
    Accurate information on global solar radiation is essential to design and operate the systems that are based on solar energy. However, global solar radiation measurement is very rare while the measurements of other meteorological parameters such as air temperature, relative humidity, sunshine duration and precipitation are common in meteorological stations all around the world. Therefore, modelling global solar radiation is an important issue to fill the gaps in database and to estimate global solar radiation in places where global solar radiation measurement is not available. There are many different approaches in the literature for modelling global solar radiation. Two new methodologies are presented in this paper to develop parametric models for estimation of daily global solar radiation based on sunshine duration and relative humidity as well as a review of fourteen different already exist parametric models which are based on air temperature, maximum temperature, minimum temperature, precipitation, sunshine duration and relative humidity. The proposed models improve the estimation results of the other fourteen models with average mean absolute error (MAE) of 0.947 MJ/m(2) for Adana station, 1.086 MJ/m(2) for Goksun station, 1.074 MJ/m(2) for Tarsus station and 1.060 MJ/m(2) for whole study area. Hence, the proposed models which significantly approximate to measurements from pyranometers can be useful for the modelling global solar radiation in Eastern Mediterranean Region.
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    The optimized artificial neural network model with Levenberg-Marquardt algorithm for global solar radiation estimation in Eastern Mediterranean Region of Turkey
    (Elsevier Sci Ltd, 2016) Celik, Ozgur; Teke, Ahmet; Yildirim, H. Basak
    An accurate knowledge on global solar radiation is particularly required for proper placement and design of solar energy conversion systems. While the meteorological data are measured at most of the weather stations, global solar radiation measurement is not always performed due to high cost of the measurement devices and their operation and maintenance requirements. Therefore, several linear, non-linear and soft computing models are developed to estimate the solar radiation owing to being more economical when compared to installing pyranometers and these models provide satisfactory results. However, it is crucial to choose the most appropriate model for a specific purpose and region. The primary objective of this study is to optimize the performance of the artificial neural network model in order to realize an efficient estimation of solar radiation for Eastern Mediterranean Region of Turkey. Estimation performances are discussed for different structures of neural network by taking into account the number and quality of input features, learning algorithms, number of hidden neurons, correlation between network outputs and targets, and statistical error analysis methods. The presented model indicates that the artificial neural network models illustrate promising in the estimation of monthly mean daily global solar radiation by using commonly available data. In order to indicate the superiority of the performance of the model, it is evaluated with various test years, which are not used for training stage of the model. The presented model provides superior relationship between the estimated and measured values. The test results showed that the coefficient of determination and mean absolute percentage error between the optimized artificial neural network estimations and measured values for testing datasets are higher than 99% and %5, respectively. 2015 Elsevier Ltd. All rights reserved.