Overview of Computational Intelligence Application on Prediction of Global Solar Radiation Stephen Gbenga Fashoto Department of Computer Science University of Swaziland, Kwaluseni, Swaziland gbengafash@yahoo.com, sgfashoto@iniswa.sz Abstract - Computational Intelligence is not just about robots. It is also about understanding the nature of intelligent thought and action using computers as experimental devices. New applications using computational intelligence are still being developed, although computational intelligence is an established field. The essence of this keynote address is to give a general picture of the research directions which may give an insight into the future of this research area. Meanwhile, an attempt to comprehensively address how computational intelligence may enhance the progress of global solar radiation can be addressed in near future. Keywords: Computational Intelligence, Methodologies of Computational Intelligence, Global Solar Radiation I. INTRODUCTION As its history proves, computational intelligence is not just about robots. It is also about understanding the nature of intelligent thought and action using computers as experimental devices. The notion of Computational Intelligence was first used by the IEEE Neural Networks Council in 1990. This Council was originally founded in the 1980s by a group of researchers interested in the development of biological and artificial neural networks. On November 21, 2001, the IEEE Neural Networks Council became the IEEE Neural Networks Society, to become the IEEE Computational Intelligence Society two years later by including new areas of interest such as fuzzy systems and evolutionary computation, which they related to Computational Intelligence in 2011(Rao & Sarma, 2016). The first clear definition of Computational Intelligence was introduced by Bezdek in 1994: a system is called computationally intelligent if it deals with low-level data such as numerical data, has a pattern-recognition component and does not use knowledge in the Artificial Intelligence (AI) sense, and additionally when it begins to exhibit computational adaptive, fault tolerance, speed approaching human-like turnaround and error rates that approximate human performance. According to Bezdek (1994), Computational Intelligence is a subset of Artificial Intelligence. The artificial one is based on hard computing techniques and the computational one is based on soft computing methods, which enable adaptation to many situations. It has been the endeavour of scientists and technologists to investigate and design systems which perform like human beings. Even though, there is still no commonly accepted definition of computational intelligence. Computational intelligence (CI) is a set of nature- inspired computational methodologies and approaches to address complex real-world problems to which mathematical or traditional modeling can be useless for a few reasons: the processes might be too complex for mathematical reasoning, it might contain some uncertainties during the process, or the process might simply be stochastic in nature (Nazmul & Hojjat ,2013). Computational intelligence techniques and their applications are fast-growing with attention and tremendous effort by researchers over the years (Rao & Sarma,2016). Indeed, many real-life problems cannot be translated into binary language for computers to process, such as prediction of global solar radiation. Solar energy is one of the alternative energy sources for the future. A number of sub- Saharan African countries are blessed with a good quantum of solar radiation, which could jumpstart their clean energy needs of the 21 st century and beyond. Empirical models are mostly linear, and are unable to effectively deal with empirical irregularities, resulting from the dynamism of the measurement process that is riddled with noise. Computational Intelligence therefore provides solutions for such problems. II. METHODOLOGIES/PRINCIPLES OF COMPUTATIONAL INTELLIGENCE Computational Intelligence is an evolving collection of methodologies, which aims to exploit tolerance for imprecision, uncertainty, and partial truth to achieve robustness, tractability, and low