Author/Authors :
Sozen، Adnan نويسنده , , Arcaklioglu، Erol نويسنده ,
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/m^2 day, and the total yearly radiation period is not, vert, similar2610 h. In this study, a new formulation based on meteorological and geographical data was developed to determine the solarenergy 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 (C,anakkale, Kars, Hakkari, Sakarya, Erzurum, Zonguldak, Balikesir, Artvin, C,orum, Konya, Siirt, and TekirdaImage) 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.
Keywords :
Insulation thickness , optimization , Freeze protection , Tube