RETRACTED ARTICLE: Potential of support vector regression for solar radiation prediction in Nigeria
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Potential of support vector regression for solar radiation prediction in Nigeria Lanre Olatomiwa • Saad Mekhilef • Shahaboddin Shamshirband Dalibor Petkovic
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Received: 5 November 2014 / Accepted: 29 January 2015 Ó Springer Science+Business Media Dordrecht 2015
Abstract In this paper, the accuracy of soft computing technique in solar radiation prediction based on series of measured meteorological data (monthly mean sunshine duration, monthly mean maximum and minimum temperature) taking from Iseyin meteorological station in Nigeria was examined. The process, which simulates the solar radiation with support vector regression (SVR), was constructed. The inputs were monthly mean maximum temperature (Tmax), monthly mean minimum temperature (Tmin) and monthly mean sunshine duration ( n). Polynomial and radial basis functions (RBF) are applied as the SVR kernel function to estimate solar radiation. According to the results, a greater improvement in estimation accuracy can be achieved through the SVR with polynomial basis function compared to RBF. The SVR coefficient of determination R2 with the polynomial function was 0.7395 and with the radial basis function, the R2 was 0.5877. Keywords Nigeria
SVR Solar radiation Sunshine hour Soft computing methodologies
L. Olatomiwa S. Mekhilef Power Electronics and Renewable Energy Research Laboratory (PEARL), Department of Electrical Engineering, Faculty of Engineering, University of Malaya, 50603 Kuala Lumpur, Malaysia e-mail: [email protected] L. Olatomiwa Department of Electrical and Electronic Engineering, Federal University of Technology, PMB 65, Minna, Nigeria S. Shamshirband (&) Department of Computer System and Technology, Faculty of Computer Science and Information Technology, University of Malaya, 50603 Kuala Lumpur, Malaysia e-mail: [email protected] D. Petkovic Department for Mechatronics and Control, Faculty of Mechanical Engineering, University of Nisˇ, Aleksandra Medvedeva 14, 18000 Nis, Serbia
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Nat Hazards
Abbreviations ANFIS Adaptive neuro-fuzzy inference system GA Genetic algorithm ACO Ant colony optimization PSO Particle swarm optimization DE Differential evolution Monthly mean global solar radiation (MJ/m2/day) H n Monthly mean sunshine duration (h) RBF Radial basis function RMSE Root-mean-square error Coefficient of determination R2 SVR Support vector regression Monthly mean maximum temperature (°C) Tmax Monthly mean minimum temperature (°C) Tmin
1 Introduction Abundant energy potential from solar radiation incident on earth’s surfaces has been seen to play important role in meeting the ever-growing energy demand of the world (Ming et al. 2014; Akikur et al. 2013; Azoumah et al. 2011; Bajpai and Dash 2012; Hasan et al. 2012). Among the various available renewable resources of the earth, solar energy has attracted enormous attention not only because it sustainable, but also abundant and environmental friendly (Akikur et al. 2013). Its wide applications can results in abatement of prevalent global warming, because it does not emit CO2
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