An Artificial Neural Network Model for Estimating Daily Solar Radiation in Northwest Nigeria

Salisu Aliyu, Aminu S Zakari, Muhammad Ismail, Mohammed A Ahmed

Abstract


Solar energy has attracted enormous attention as it plays an essential role in meeting the ever growing sustainable and environmental friendly energy demand of the world. Due to the high cost of calibration and maintenance of designated measuring instruments, solar radiation data are limited not only in Nigeria but in most parts of the world. The optimal design of solar energy systems requires accurate estimation of solar radiation. Existing studies are focused on the analysis of monthly or annual solar radiation with less attention paid to the determination of daily solar radiation. Estimating daily solar radiation envisages high quality and performance of solar systems. In this paper, an Artificial Neural Network data mining model is proposed for estimating the daily solar radiation in Kano, Kaduna and Katsina, North West Nigeria. Daily Solar radiation data for 21years collected from the Nigerian Metrological Agency were used as training/testing data while developing the model. Two statistical indicators: coefficient of determination (R2) and the root mean square error (RMSE) were used to evaluate the model. An RMSE of 0.47 and 0.48 was obtained for the training and testing dataset respectively, while an R2 of 0.78 was obtained for both the training and testing dataset. The overall results showed that artificial neural network exhibits excellent performance for the estimation of daily solar radiation.

Keywords— Artificial Neural Network, Data mining, Solar Radiation 


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DOI: http://dx.doi.org/10.46792/fuoyejet.v5i2.563

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