Kulchandra Panthee1, Ajay Kumar Jha2
Abstract— There is no doubt that information of the measured data of solar radiation is the best for designing any reliable solar energy systems but in Nepal the measured solar radiation data are not available for most of the sites due to high cost and requirement of daily maintenance of the measuring instruments. The alternative is to use the estimated data of solar radiation using any of available estimation models. In this study an Artificial Neural Network (ANN) was used to estimate the solar radiation in Kathmandu with the help of meteorological data of maximum and daily average temperature, relative humidity, rainfall amount, sunshine hour and solar radiation available for Tribhuvan International Airport. Data from 2002 to 2011 were used to train the Network and it was tested by using the data of 2012 and 2013. A multi-layer feed-forward neural network was devised using MATLAB programming. Five different models with different input combinations were modeled with Feed-Forward Multilayer Preceptors. The results of ANN model were compared with measured data on the basis of root mean square error (RMSE), mean bias error (MBE), mean percentage error (MPE) and Correlation Coefficients (CC) in order to check the performance of developed model. The obtained result indicate that the ANN based model for estimating solar radiation is precise in the selected location thus the model can be used anywhere in the Nepal having similar climate conditions where the meteorological data are available. The best prediction was from Model 1 as it exhibit minimum value of RMSE (0.2781) and maximum value of CC (0.9880).
Keywords— Artificial Neural Network, Correlation Coefficient, Global Solar Radiation, Mean Bias Error, Mean Percentage Error, Root Mean Square Error
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