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    Daily total global solar radiation modeling from several meteorological data
    (SPRINGER WIEN, 2011) Bilgili, Mehmet; Ozgoren, Muammer
    This paper investigates the modeling of the daily total global solar radiation in Adana city of Turkey using multi-linear regression (MLR), multi-nonlinear regression (MNLR) and feed-forward artificial neural network (ANN) methods. Several daily meteorological data, i.e., measured sunshine duration, air temperature and wind speed and date of the year, i.e., monthly and daily, were used as independent variables to the MLR, MNLR and ANN models. In order to determine the relationship between the total global solar radiation and other meteorological data, and also to obtain the best independent variables, the MLR and MNLR analyses were performed with the "Stepwise" method in the Statistical Packages for the Social Sciences (SPSS) program. Thus, various models consisting of the combination of the independent variables were constructed and the best input structure was investigated. The performances of all models in the training and testing data sets were compared with the measured daily global solar radiation values. The obtained results indicated that the ANN method was better than the other methods in modeling daily total global solar radiation. For the ANN model, mean absolute error (MAE), mean absolute percentage error (MAPE), correlation coefficient (R) and coefficient of determination (R (2)) for the training/testing data set were found to be 0.89/1.00 MJ/m(2) day, 7.88/9.23%, 0.9824/0.9751, and 0.9651/0.9508, respectively.
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    Estimation of global solar radiation using ANN over Turkey
    (PERGAMON-ELSEVIER SCIENCE LTD, 2012) Ozgoren, Muammer; Bilgili, Mehmet; Sahin, Besir
    The main objective of the present study is to develop an artificial neural network (ANN) model based on multi-nonlinear regression (MNLR) method for estimating the monthly mean daily sum global solar radiation at any place of Turkey. For this purpose, the meteorological data of 31 stations spread over Turkey along the years 2000-2006 were used as training (27 stations) and testing (4 stations) data. Firstly, all independent variables (latitude, longitude, altitude, month, monthly minimum atmospheric temperature, maximum atmospheric temperature, mean atmospheric temperature, soil temperature, relative humidity, wind speed, rainfall, atmospheric pressure, vapor pressure, cloudiness and sunshine duration) were added to the Enter regression model. Then, the Stepwise MNLR method was applied to determine the most suitable independent (input) variables. With the use of these input variables, the results obtained by the ANN model were compared with the actual data, and error values were found within acceptable limits. The mean absolute percentage error (MAPE) was found to be 5.34% and correlation coefficient (R) value was obtained to be about 0.9936 for the testing data set. Crown Copyright (C) 2011 Published by Elsevier Ltd. All rights reserved.
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    Hourly Performance Prediction of Ammonia-water Solar Absorption Refrigeration
    (Pergamon-elsevier Science Ltd, 2012) Özgören, Muammer; Bilgili, Mehmet; Babayiğit, Osman
    This paper deals with the hourly performance investigation of solar absorption refrigeration (SAR) system with evacuated tube collector and ammonia water (NH3-H2O) solution. The SAR system is presented to simulate the system characteristic variations using hourly atmospheric air temperature and solar radiation data for Adana province in Turkey. The evaluation is performed for the maximum temperature occurrence day on July 29. First, the variations of various parameters, such as absorption refrigeration machine efficiency, condenser capacity and heat transfer rate in the generator and absorber during the day, are calculated for different cooling capacities and generator temperatures. Later, the minimum evacuated tube collector surface area is determined. According to the obtained results, the SAR system is considerably suitable for home/office-cooling purposes between the hours 09:00 and 16:00 in the southern region of Turkey such as Adana province. The most suitable performance of the absorption cooling system is calculated for the generator temperature values equal to or higher than 110 degrees C. The performance coefficient of the cooling (COPcooling) varies in the range of 0.243-0.454 while that of the heating (COPheating) changes from 1.243 to 1.454 during the day. Evacuated tube collector area for a 3.5 kW cooling load capacity is found to be 35.95 m(2) for the region at 16:00 whereas it is 19.85 m(2) at 12:00.