Meeting the energy requirements with imported fuels leads to economic and political problems in the countries. Therefore, renewable energy investments continue to grow globally as a sustainable and increasingly economically viable alternative to conventional sources of energy. This study aims to reduce the share of imported fuels in Turkey's electricity generation and to estimate the employment gain to be provided by renewable energy investments to be established instead. Approximately 900,000 jobs are created during the production, construction, operational, and maintenance phases of additional 49,448 MW capacity renewable power plants to be installed. While analyzing, the decision on how much to invest in which renewable resource is determined with respect to multi-criteria decision making (MCDM) model.
As informal activities are considered as a crime, that kind of activities are being carried out secretly and their detection is difficult in most cases. Along with difficulties in determining the size of informal economy exactly, recently developed models and opportunities to reach reliable data enable making realistic estimations in regard to shadow economy. This study benefits from 11 different studies estimating informality in European countries and Turkey by using physical input, currency demand, DYMIMIC, and MIMIC methods. Common conclusion acquired from these studies is that informality rate in Turkey is higher than EU15 countries and EU13 countries –except for Hungary, Cyprus, Latvia, Croatia and Bulgaria. In addition to the comparison of these data, the reasons of the emergence of informal economy, measuring methods, and policy proposals in order to hamper informality in Turkey are also discussed.
In order to be presented to consumers at reasonable prices, electricity consumption should be predicted before it is generated. This prediction gained more importance with the enactment of the Electricity Market Law No. 4628 and 6446, which liberalized the electricity market. There are many data analysis methods for the prediction of demand. Some of these models are Artificial Neural Networks, Autoregressive Moving Average and Simple/Multiple Regression. Electricity demand forecast for the success of the program has been developed and tested with a variety of methods used in the study data. studies on this issue for smart grids, which is building the network of the future are important. In this study, an electricity demand forecast program is developed by applying regression model, which uses the past data for deriving a conclusion. In addition, simple regression and multiple regressions demand forecasts are presented while investigating the effects of some factors (Gross Domestic National Product, Average Life Expectancy, and Internet Usage) on electricity consumption.
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