Prediction of solar direct irradiance in Iraq by using artificial neural network

  • zana Saleem Lafargeholcim, Kurdistan region of Iraq, Sulaymaniyah.
  • Gzing Adil Mohammed Department of Oil, Gas and Energy administration, Public Administration and Natural Resources, Charmo University.
Keywords: Renewable energy, Solar system, Artificial neural network, Prediction.


     Global solar irradiance is one of the main significant factors for designing and considering the volume of any solar station beside of it is usage in agricultural and building issue. Due of lack a precise information about the irradiance in Iraq metrological organization and seismology, this study is aimed to adopt the historical global data, build numerical analysis via using artificial neural network and predicting hourly irradiance. The test is applied over three locations Erbil, Bagdad, and Basra for being references to their closest locations. A foreword neural network (FNN) is the learning algorithm that is used in this study with relying on seven input variables consisting of Temperature, Precipitation, Humidity, Wind speed, Wind direction Sunshine duration and Date. After normalizing and standardizing data, an iteration method is used for determining the optimum number of neuron(s) in a hidden layer. It yields a least Root Mean square error (RMSE) between 2.5 to 3. The computed correlation coefficients are between 0.94 -0.96 for the mentioned locations.


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How to Cite
Saleem, zana and Adil Mohammed, G. (2021) “Prediction of solar direct irradiance in Iraq by using artificial neural network”, Zanco Journal of Pure and Applied Sciences, 33(5), pp. 43-50. doi: 10.21271/ZJPAS.33.5.5.
Mathematics ,Physics and Engineering Researches