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Load forecasting is very important to manage the electrical power systems. Load forecasting can be analyzed in three different ways as short-term, medium-term and long-term. Long-term load forecasting (LTLF) is in need to plan and carry on future energy demand and investment such as size of energy plant. LTLF is affected by energy consumption, national incoming per year, rates of civilization, increasing population rates and moreover economical parameters. Some of the forecasting models use mathematical formulas and statistical models such as correlation and regression analysis. In this study, a new effective expert-supported dynamic programming algorithm (ESDP) has been improved. Additionally, adaptive neuro-fuzzy inference system (ANFIS) and mathematical modeling (MM) are used to forecast long term energy demand. ANFIS is one of the famous artificial intelligence and has widely used to solve forecasting problems in literature. In addition to numerical inputs, ANFIS has linguistics inputs. The results obtained from ESDP, ANFIS and MM are compared to show availability. In order to show error levels mean absolute percentage error (MAPE) and (MAE) are used. The obtained results show that the proposed algorithms are available.

Load forecasting is very important to manage the electrical power systems. Load forecasting can be analyzed in three different ways as short-term, medium-term and long-term. Long-term load forecasting (LTLF) is in need to plan and carry on future energy demand and investment such as size of energy plant. LTLF is affected by energy consumption, national incoming per year, rates of civilization, increasing population rates and moreover economical parameters. Some of the forecasting models use mathematical formulas and statistical models such as correlation and regression analysis. In this study, a new effective expert-supported dynamic programming algorithm (ESDP) has been improved. Additionally, adaptive neuro-fuzzy inference system (ANFIS) and mathematical modeling (MM) are used to forecast long term energy demand. ANFIS is one of the famous artificial intelligence and has widely used to solve forecasting problems in literature. In addition to numerical inputs, ANFIS has linguistics inputs. The results obtained from ESDP, ANFIS and MM are compared to show availability. In order to show error levels mean absolute percentage error (MAPE) and (MAE) are used. The obtained results show that the proposed algorithms are available.

In this study, a comprehensive exergy analysis was applied to a solar power system (SPS) located in the city of Van, Turkey. Van was always known as a sun city throughout history with the name of TUSBA. It has an average of 300 sunny days a year, an altitude of 1727 m, and an average daily temperature of 9.85°C. Considering how the need for sustainable and renewable energy sources - and knowledge over them - constantly increases, this SPS was built to reveal the true solar potential of the campus area of Yuzuncu Yil University of the city of Van, hopefully as a precursor for further large-scale SPS constructions in the region. A daily analysis considering energy production and global radiation was applied to the system in 2013 for 365 days. It was found that the yearly exergy efficiency of the system was 10% and total electrical energy production was 31,038.95 kWh, averaging 1763.5 kWh/kWp yearly.

In this study, a comprehensive exergy analysis was applied to a solar power system (SPS) located in the city of Van, Turkey. Van was always known as a sun city throughout history with the name of TUSBA. It has an average of 300 sunny days a year, an altitude of 1727 m, and an average daily temperature of 9.85°C. Considering how the need for sustainable and renewable energy sources - and knowledge over them - constantly increases, this SPS was built to reveal the true solar potential of the campus area of Yuzuncu Yil University of the city of Van, hopefully as a precursor for further large-scale SPS constructions in the region. A daily analysis considering energy production and global radiation was applied to the system in 2013 for 365 days. It was found that the yearly exergy efficiency of the system was 10% and total electrical energy production was 31,038.95 kWh, averaging 1763.5 kWh/kWp yearly.

Beyond the anticipated experience associated with tourism destinations, the United Nations World Tourism Organization (UNWTO) has further tasked (especially the destination countries) on the importance of tourism to achieving the 2030 Sustainable Development Goals (SDGs). From this dimension, this study employed the ecological footprint of the 10 most visited countries (France, Spain, United States, China, Italy, Mexico, United Kingdom, Turkey, Germany, and Thailand) over the period 1995-2016. Specifically, the study employed an econometric approach and found that increase in tourism arrivals and globalization is detrimental to the attainment of sustainable environmental quality in a long term. Precisely, a 1% increase in international arrivals and globalization is responsible for a 0.18 and 0.89% increase in ecological footprint in the long-run. These impacts of tourism activities and globalization are detrimental to the environmental quality of the destination countries. Meanwhile, the real income per capita and biocapacity in the destination countries improve the environmental quality of the panel of destination countries in the long-run. In addition, the study found significant evidence of Granger causality from tourism and real income to ecological footprint without feedback, the globalization-ecological footprint Granger causality nexus is with feedback. Moreover, potentially effective policies for government and other stakeholders especially toward attaining Global goals were proffered in the study.

Beyond the anticipated experience associated with tourism destinations, the United Nations World Tourism Organization (UNWTO) has further tasked (especially the destination countries) on the importance of tourism to achieving the 2030 Sustainable Development Goals (SDGs). From this dimension, this study employed the ecological footprint of the 10 most visited countries (France, Spain, United States, China, Italy, Mexico, United Kingdom, Turkey, Germany, and Thailand) over the period 1995-2016. Specifically, the study employed an econometric approach and found that increase in tourism arrivals and globalization is detrimental to the attainment of sustainable environmental quality in a long term. Precisely, a 1% increase in international arrivals and globalization is responsible for a 0.18 and 0.89% increase in ecological footprint in the long-run. These impacts of tourism activities and globalization are detrimental to the environmental quality of the destination countries. Meanwhile, the real income per capita and biocapacity in the destination countries improve the environmental quality of the panel of destination countries in the long-run. In addition, the study found significant evidence of Granger causality from tourism and real income to ecological footprint without feedback, the globalization-ecological footprint Granger causality nexus is with feedback. Moreover, potentially effective policies for government and other stakeholders especially toward attaining Global goals were proffered in the study.

With the development of technology, some important developments occur in electrical energy systems. Especially smart grids and micro grids will be an important part of energy systems in the following years. With the increasing use of micro grids, more renewable power plants will be included in the system. However, the production of renewable energy sources varies according to the weather conditions. Therefore, the generated energy is not known beforehand. Increasing single-phase loads (such as electric vehicle charging stations), unpredictable renewable generation and uncertainty of consumption can lead to problems of voltage unbalance in the micro grid. This problem can damage the network in many ways. In this study, the problem of voltage unbalance occurring in the microgrid was tried to be reduced by demand side management. The comfort of the customer should not be affected when demand side management is performed. For this reason, the most suitable load for demand side management is thermostatically controlled loads (TCL). An algorithm has been proposed to reduce voltage unbalance by using these loads. This algorithm was tested with the help of PSCAD/EMTDC program.

With the development of technology, some important developments occur in electrical energy systems. Especially smart grids and micro grids will be an important part of energy systems in the following years. With the increasing use of micro grids, more renewable power plants will be included in the system. However, the production of renewable energy sources varies according to the weather conditions. Therefore, the generated energy is not known beforehand. Increasing single-phase loads (such as electric vehicle charging stations), unpredictable renewable generation and uncertainty of consumption can lead to problems of voltage unbalance in the micro grid. This problem can damage the network in many ways. In this study, the problem of voltage unbalance occurring in the microgrid was tried to be reduced by demand side management. The comfort of the customer should not be affected when demand side management is performed. For this reason, the most suitable load for demand side management is thermostatically controlled loads (TCL). An algorithm has been proposed to reduce voltage unbalance by using these loads. This algorithm was tested with the help of PSCAD/EMTDC program.