Numerous studies have attempted to predict the energy output of solar-powered vehicles based on different parameters such as road conditions, driver characteristics, and weather. However, since these studies were conducted on stationary vehicles, they are limited in their accuracy when applied to driving vehicles. This study aimed to improve the accuracy of electric power prediction for a solar-powered bus by applying a technique that improves energy efficiency without affecting driving performance. A comparative analysis of power generation and solar irradiance data was conducted for the bus driven on different roads to forecast its power generation, and a high-accuracy power generation prediction equation was derived. A comparison with actual test results revealed that a power generation forecast accuracy of at least 90% was achieved, validating the equation used for forecasting. With this power generation prediction process, it is possible to forecast the amount of energy generated in advance when a solar bus is operated in a specific area.

Moon mineralogy mapper (M3)'s work proved that the moon is not completely dry but has some hydroxyl/water. M3’s data confirmed that the amount of hydroxyl on the lunar surface is inversely related to the measured signal brightness, suggesting the lunar surface is sensitive to temperature by solar insolation. We tested the effect of solar insolation on the local distribution of hydroxyl by using M3 data, and we found that most craters had more hydroxyl in shade areas than in sunlit areas. This means that the local distribution of hydroxyl is absolutely influenced by the amount of sunshine. We investigated the factors affecting differences in hydroxyl; we found that the higher the latitude, the larger the difference during daytime. We also measured the pyroxene content and found that pyroxene affects the amount of hydroxyl, but it does not affect the difference in hydroxyl between sunlit and shaded areas. Therefore, we confirmed that solar insolation plays a significant role in the local distribution of hydroxyl, regardless of surface composition.

In order to examine the effect on the insolation of cloud cover, we analyzed the data of the insolation, cloud (over and surface air pressure in Pusan during the period of 1991. 10 - 1993.1. At first, we investigate the atmospheric transmissivity A(t) using the Beer`s law at clear skies. The atmospheric transmissivity is characterized by cold season high and warm season low. From this atmospheric transmissivity, the empirical formula that shows the variation of the insolation due to the cloud cover is obtained. The result formula is I = I_0 A(t^n)(0.7 - 0.05 × m ). I is the insolation that reaches the surface when cloud cover is m and I_0 is solar constant. Although the result is some rough it seems meaningful that the estimation of insolation can be made only from the routine data.