Solar radiation is free, and very useful input for most sectors such as heat, health, tourism, agriculture, and energy production, and it plays a critical role in the sustainability of biological, and chemical processes in nature. In this framework, the knowledge of solar radiation data or estimating it as accurately as possible is vital to get the maximum benefit from the sun. From this point of view, many sectors have revised their future investments/plans to enhance their profit margins for sustainable development according to the knowledge/estimation of solar radiation. This case has noteworthy attracted the attention of researchers for the estimation of solar radiation with low errors. Accordingly, it is noticed that various types of models have been continuously developed in the literature. The present review paper has mainly centered on the solar radiation works estimated by the empirical models, time series, artificial intelligence algorithms, and hybrid models. In general, these models have needed the atmospheric, geographic, climatic, and historical solar radiation data of a given region for the estimation of solar radiation. It is seen from the literature review that each model has its advantages and disadvantages in the estimation of solar radiation, and a model that gives the best results for one region may give the worst results for the other region. Furthermore, it is noticed that an input parameter that strongly improves the performance success of the models for a region may worsen the performance success of another region. In this direction, the estimation of solar radiation has been separately detailed in terms of empirical models, time series, artificial intelligence algorithms, and hybrid algorithms. Accordingly, the research gaps, challenges, and future directions for the estimation of solar radiation have been drawn in the present study. In the results, it is well-observed that the hybrid models have exhibited more accurate and reliable results in most studies due to their ability to merge between different models for the benefit of the advantages of each model, but the empirical models have come to the fore in terms of ease of use, and low computational costs.

Transportation is nearly entirely (<99.9) powered by internal combustion engines (ICEs) burning the fossil fuels like oil and liquefied petroleum gas. However, the world fossil fuels’ reserves are finite, and it is foreseen it will run out nearly 50 years for oil, 60 years for natural gas, and 80 years for coal. Thus, humankind has rapidly started to seek new, renewable and clean energy sources for particularly powering the vehicles. In this scope, the biggest alternative to classic internal combustion engines is undoubtedly electric vehicles (EVs) like in the past. In recent years, there has been an increasing interest in electric vehicles, and over the past decade, there has been a dramatic increase in EVs production of 1500%. On the other hand, Turkey has failed to keep pace with this increase, and seen 475 EVs by end of 2017. It is thought Turkish dependence on fossil fuels, and more greenhouse gas emissions than its undertaken in the Kyoto protocol as a country of signed Kyoto protocol, Turkey should rapidly turn its route to the electric vehicles to reduce fossil fuel consumption and greenhouse gas emissions. Additionally, as together with a tendency to electric vehicles, it will be useful tending to renewable and alternative energy sources for contributing to the countries’ economies depending on decreasing on fossilbased fuels. For this scope, this paper aims not only to compare the electric vehicles and internal combustion engines but also to give information about the influence over the ecological balance of the world, arising from these powering methods. Finally, the nuclear energy policy of Turkey was also evaluated as an alternative to the rapidly increasing electricity consumption of the country in this paper.