Abstract

Recent issues of climate changes and natural disasters have brought many changes in world energy utilization. Especially due to the Japan's earthquake and tsunami, potential of nuclear power have made negative. And thus many countries are looking for a new renewable energy that can replace. Of which solar energy has emerged as a useful alternative. Under these circumstances, it is highly desirable that detailed information about the availability of solar radiation on the surface is essential for the optimum design and study of solar energy systems. And its components at a given location are very essential. Hence the solar radiation data is one of the key parameters required to be monitored at any meteorological station. But solar radiation measurements are not easily available due to the cost and maintenance requirements of the measuring equipment. Therefore, solar resource modeling or mapping is one of the essential tools for proper design, planning, maintenance and pricing of solar energy system. In this study, the feasibility of a regression model using image fusion for the prediction of solar energy potential in Republic of Korea was investigated. Meteorological and geographical data of 22 cities in South Korea for period of 10 years (2001–2011) were used. Meteorological and geographical data (latitude, longitude, altitude, month, sunshine duration, temperature, and relative humidity) were used as inputs to the model, while the regional solar radiation was used as the output of the model. The model for evaluating the spatial and temporal solar radiation was executed for South Korea. The annual mean solar radiation estimates in South Korea vary from a minimum of 5.48 MJ/m 2 /day to a maximum of 19.51 MJ/m 2 /day. Our proposed annual mean solar radiation is 13.5 MJ/m 2 /day. These compare favorably with the observed data as expected. This study has shown that a simple method can accurately predict solar radiation potential in South Korea.

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