Estimation of solar potential in Turkey by artificial neural networks using meteorological and geographical data


SÖZEN A. , ARCAKLIOĞLU E., Ozalp M.

ENERGY CONVERSION AND MANAGEMENT, cilt.45, ss.3033-3052, 2004 (SCI İndekslerine Giren Dergi) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 45
  • Basım Tarihi: 2004
  • Doi Numarası: 10.1016/j.enconman.2003.12.020
  • Dergi Adı: ENERGY CONVERSION AND MANAGEMENT
  • Sayfa Sayıları: ss.3033-3052

Özet

Turkey is located at the Mediterranean at 36degrees and 42degrees N latitudes and has a typical Mediterranean climate. The solar energy potential is very high in Turkey. The yearly average solar radiation is 3.6 kW h/m(2) day, and the total yearly radiation period is similar to2610 h. This study consists of two cases. Firstly, the main focus of this study is to put forward the solar energy potential in Turkey using artificial neural networks (ANNs). Secondly, in this study, the best approach was investigated for each station by using different learning algorithms and a logistic sigmoid transfer function in the neural network with developed software. In order to train the neural network, meteorological data for last three years (2000-2002) from 17 stations (Ankara, Samsun, Edirne, Istanbul-Goztepe, Van, Izmir, Denizli, Sanliurfa, Mersin, Adana, Gaziantep, Aydin, Bursa, Diyarbakir, Yozgat, Antalya and Mugla) spread over Turkey were used as training (11 stations) and testing (6 stations) data. 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 6.735% and R-2 values were found to be about 99.893% for the testing stations. However, these values were found to be 4.398% and 99.965% for the training stations. The trained and tested ANN models show greater accuracy for evaluating the solar resource possibilities in regions where a network of monitoring stations has not been established in Turkey. The predicted solar potential values from the ANN are given in the form of monthly maps. These maps are of prime importance for different working disciplines, like scientists, architects, meteorologists and solar engineers, in Turkey. The predictions from the ANN models could enable scientists to locate and design solar energy systems in Turkey and determine the best solar technology. (C) 2004 Elsevier Ltd. All rights reserved.