ABSTRACT
In this study, the feasibility of an artificial neural network (ANN) based model for the prediction of
solar energy potential in Africa was investigated. Standard multilayered, feed-forward, backpropagation
neural networks with different architecture were designed using NeuroSolutions®.
Geographical and meteorological data of 172 locations in Africa for the period of 22 years (1983-
2005) were obtained from NASA geo-satellite database. The input data (geographical and
meteorological parameters) to the network includes: latitude, longitude, altitude, month, mean
sunshine duration, mean temperature, and relative humidity while the solar radiation intensity was
used as the output of the network. The results showed that after sufficient training sessions, the
predicted and the actual values of solar energy potential had Mean Square Errors (MSE) that
ranged between 0.002 - 0.004, thus suggesting a high reliability of the model for evaluation of
solar radiation in locations where solar radiation data are not available in Africa. The predicted
and actual values of solar energy potential were given in form of monthly maps. The solar
radiation potential (actual and ANN predicted) in northern Africa (region above the equator) and
the southern Africa (region below the equator) for the period of April – September ranged
respectively from 5.0 - 7.5 and 3.5 - 5.5 kW h/m2/day while for the period of October – March
ranged respectively from 2.5 – 5.5 and 5.5 - 7.5 kW h/m2/day. This study has shown that ANNbased
model can accurately predict solar radiation potential in Africa.
Fadare, D (2021). Modeling of solar energy potential in Africa using an artificial neural network. Afribary. Retrieved from https://tracking.afribary.com/works/modeling-of-solar-energy-potential-in-africa-using-an-artificial-neural-network
Fadare, D. "Modeling of solar energy potential in Africa using an artificial neural network" Afribary. Afribary, 22 Apr. 2021, https://tracking.afribary.com/works/modeling-of-solar-energy-potential-in-africa-using-an-artificial-neural-network. Accessed 26 Nov. 2024.
Fadare, D. . "Modeling of solar energy potential in Africa using an artificial neural network". Afribary, Afribary, 22 Apr. 2021. Web. 26 Nov. 2024. < https://tracking.afribary.com/works/modeling-of-solar-energy-potential-in-africa-using-an-artificial-neural-network >.
Fadare, D. . "Modeling of solar energy potential in Africa using an artificial neural network" Afribary (2021). Accessed November 26, 2024. https://tracking.afribary.com/works/modeling-of-solar-energy-potential-in-africa-using-an-artificial-neural-network