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Solar potential in Turkey
Affiliation:1. Department of Mechanical Education, Technical Education Faculty, Gazi University, Teknikokullar, 06503 Ankara, Turkey;2. Department of Mechanical Engineering, Engineering Faculty, Kırıkkale University, 71450 Kırıkkale, Turkey;1. University of Napoli “Parthenope”, Centro Direzionale Isola C4, 80143 Napoli, Italia;2. University of Roma “Tor Vergata”, Via del Politecnico 1, 00133 Roma, Italia;3. University of Tuscia, Largo dell’Università snc, 01100 Viterbo, Italia;4. University of Roma “Sapienza”, Via Eudossiana 18, 00184 Roma, Italia;1. Faculty of Physics and Nuclear Engineering, Shahrood University of Technology, P.O. Box 3619995161, Shahrood, Iran;2. Nuclear Science and Technology Research Institute (NSTRI), Plasma and Nuclear Fusion Research School, Tehran, Iran;3. Nuclear Science and Technology Research Institute (NSTRI), Reactor & Nuclear Safety School, Tehran, Iran
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.
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