| dc.description.abstract | Most 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. | en_US |
