• The Journal of the Korea Contents Association
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• v.22 no.7
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• pp.55-62
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• 2022

The perovskite solar cell is an active part of research in renewable energy fields such as solar energy, wind, hydroelectric power, marine energy, bioenergy, and hydrogen energy to replace fossil fuels such as oil, coal, and natural gas, which will gradually disappear as power demand increases due to the increase in use of the Internet of Things and Virtual environments due to the 4th industrial revolution. The perovskite solar cell is a solar cell device using an organic-inorganic hybrid material having a perovskite structure, and has advantages of replacing existing silicon solar cells with high efficiency, low cost solutions, and low temperature processes. In order to optimize the light absorption layer thin film predicted by the existing empirical method, reliability must be verified through device characteristics evaluation. However, since it costs a lot to evaluate the characteristics of the light-absorbing layer thin film device, the number of tests is limited. In order to solve this problem, the development and applicability of a clear and valid model using machine learning or artificial intelligence model as an auxiliary means for optimizing the light absorption layer thin film are considered infinite. In this study, to estimate the light absorption layer thin-film optimization of perovskite solar cells, the regression models of the support vector machine's linear kernel, R.B.F kernel, polynomial kernel, and sigmoid kernel were compared to verify the accuracy difference for each kernel function.

• Smart Media Journal
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• v.12 no.10
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• pp.63-70
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• 2023

Recently, energy consumption for heating costs, which is 35% of smart farm energy costs, has increased, requiring energy consumption efficiency, and the importance of new and renewable energy is increasing due to concerns about the realization of electricity bills. Renewable energy belongs to hydropower, wind, and solar power, of which solar energy is a power generation technology that converts it into electrical energy, and this technology has less impact on the environment and is simple to maintain. In this study, based on the greenhouse heat storage tank and heat pump data, the factors that affect the heat storage tank are selected and a heat storage tank supply temperature prediction model is developed. It is predicted using Long Short-Term Memory (LSTM), which is effective for time series data analysis and prediction, and XGBoost model, which is superior to other ensemble learning techniques. By predicting the temperature of the heat pump heat storage tank, energy consumption may be optimized and system operation may be optimized. In addition, we intend to link it to the smart farm energy integrated operation system, such as reducing heating and cooling costs and improving the energy independence of farmers due to the use of solar power. By managing the supply of waste heat energy through the platform and deriving the maximum heating load and energy values required for crop growth by season and time, an optimal energy management plan is derived based on this.

• Journal of the Korean Geographical Society
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• v.48 no.1
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• pp.87-111
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• 2013

Regional disparities between eastern and western regions is the most of serious problem for balanced regional development in Turkey. The Southeastern Anatolia Project (GAP) is being implemented to eliminate these regional development disparities. The work that was initially planned as predominantly for hydraulic energy production to utilize water resources of the Tigris and Euphrates rivers more effectively was later transformed into an integrated multi-sector regional development project. This study noted that this region had very limited cash crop production because of the constraints of semi-arid climate of the southeastern region, however, later, it has changed Turkey's major cotton producing region since Southeastern Anatolia Project carried out. Therefore, this study investigated background, process, and content of the Southeastern Anatolia Project with respect to high cotton productivity in this region and examined the dynamic changes of cotton productivity in this region. In addition, Sanliurfa prefecture is one of the main development axes of the Southeastern Anatolia Project, because government investments are concentrated on this prefecture. Therefore, this study examined the background and process of cotton farming growth in this prefecture. In 2011, Sanliurfa prefecture produced 37.6% of Turkey's total cotton production. This is mainly due to agricultural infrastructure expansion such as land consolidation, irrigation, roads and farm roads. Also, it is one of the main factor that subsidies paid to farmers for cotton cultivation. The introduction of irrigation has dramatically changed the direction of seasonal migration of this area. Prior to irrigation, this area had a serious social issue about out-migration for seasonal labor to other areas. However, the introduction of irrigation made this area that changed to in-migration and intramigration for cotton cultivation. Irrigation water is supplied to farmers through the WUAs (Water User Associations) that handed over irrigation water management, operation from DSI (General Directorate of State of Hydraulic Works). However, the WUAs are under the influence of Ashiret, a traditional feudal social structure. Because of this reason, it does not have an efficient management for farmers. Also, it is one of the reasons that this area does not have autonomous farmer organization.