Article ID Journal Published Year Pages File Type
10282136 Applied Energy 2005 11 Pages PDF
Abstract
Most of the locations in Turkey receive abundant solar-energy, because Turkey lies in a sunny belt between 36° and 42°N latitudes. Average annual temperature is 18 to 20 °C on the south coast, falls to 14-16 °C on the west coat, and fluctuates between 4 and 18 °C in the central parts. The yearly average solar-radiation is 3.6 kW h/m2 day, and the total yearly radiation period is ∼2610 h. 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 (Çanakkale, Kars, Hakkari, Sakarya, Erzurum, Zonguldak, Balikesir, Artvin, Çorum, Konya, Siirt, and Tekirdaǧ) 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 R2 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.
Related Topics
Physical Sciences and Engineering Energy Energy Engineering and Power Technology
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