Global Solar Radiation Prediction Using Backward Propagation Artificial Neural Network for the City of Addis Ababa, Ethiopia
Ethiopia is located close to the equatorial belt that receives abundant solar energy. For Ethiopia, to achieve the optimum utilization of solar energy, it is necessary to evaluate the incident solar radiation over the countries of interest. Though, sophisticated and costly equipment are available but they are very limited for developing countries’ like Ethiopia. This paper is therefore tries to explore the use of artificial neural network method for predicting the daily global solar radiation in the horizontal surface using secondary data in the city of Addis Ababa. For this purpose, the meteorological data of 1195 days from one station in Addis Ababa along the years 1985–1987 were used for training testing and validating the model All independent variables (Min and Max Temperature, humidity, sunshine hour and wind speed were normalized and added to the model. Then, Back propagation (BP) Artificial Neural Network (ANN) method was applied for prediction and training respectively to determine the most suitable independent (input) variables. The results obtained by the ANN model were validated with the actual data and error values were found within acceptable limits. The findings of the study show that the Root Mean Square Error (RMSE) is found to be 0.11 and correlation coefficient (R) value was obtained 0.901 during prediction.
KeywordsGlobal solar radiation Artificial Neural Network Backpropagation Climatological parameters Multi-layer feedforward neural network
This work and the contribution were supported by project “Smart Solutions for Ubiquitous Computing Environments” FIM, University of Hradec Kralove, Czech Republic (under ID: UHK-FIM-SP-2016-2102).
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