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    Current practices, potentials, challenges, future opportunities, environmental and economic assumptions for Turkiye's clean and sustainable energy policy: A comprehensive assessment
    (Elsevier, 2023) Agbulut, Umit; Yildiz, Gokhan; Bakir, Huseyin; Polat, Fikret; Bicen, Yunus; Ergun, Alper; Gurel, Ali Etem
    In today's world, most countries including Turkiye have met their electricity demand at a dominant rate by burning fossil-based fuels in thermal power plants. However, fossil-fuel reserves have been rapidly depleted, resulting in high volatility in these fuels' markets, as well as alarming environmental, and economic problems for the governments. In recent years, many governments have started to face these problems and have rapidly transitioned to renewable and alternative carbon-free energy sources in their electricity production variety. However, these belated steps have failed to mitigate the increment in global greenhouse gas emissions against the rapid growth of population and energy demand. In recent years, Turkiye has put a noteworthy challenge to mitigate its dominant use of fossil fuels, reducing its energy dependence, sustaining its economic development, and mitigating the carbon footprint. From this point of view, it is witnessed that many power plants have been established, many of them are currently under construction, especially to produce more electricity in a sus-tainable way. Accordingly, the present study aims to comprehensively discuss Turkiye's energy production policy, energy potential and reserves, challenges, future opportunities, and the impacts of the energy sector on the economic and environmental issues for the country. In this framework, it is well-noticed that the country's future energy production policy has been reasonably changed in order to achieve positive economic and envi-ronmental outcomes in the medium and long term.
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    A state of art review on estimation of solar radiation with various models
    (Cell Press, 2023) Gurel, Ali Etem; Agbulut, Umit; Bakir, Huseyin; Ergun, Alper; Yildiz, Gokhan
    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 radi-ation with low errors. Accordingly, it is noticed that various types of models have been contin-uously 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. Accord-ingly, 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.