The government is implementing a policy to expand eco-friendly energy as a power source. However, the output of new and renewable energy is not constant. It is difficult to stably adjust the power supply to the power demand in the power system. Therefore, the government predicts day-ahead the amount of renewable energy generation to cope with the output volatility caused by the expansion of renewable energy. It is a system that pays a settlement amount if it transitions within a certain error rate the next day. In this paper, Machine Learning was used to study the prediction of power generation within the error rate.

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.

In order to clarify the characteristics of Photo-Volatic(PV) power generation over the Korean peninsula with complex terrain, special meteorological observation campaign was carried out for one year from 25 May 2011. Analysis is based on the comparison between observed meteorological elements and PV values generated at rated capacity 200 kW power plants. Solar radiation observed at 15° inclined surface is 11 % larger than that observed at horizontal surface due to low elevation angel of the sun during winter season. The PV power generation tend to be more similar the variation of inclined surface irradiance than horizontal surface irradiance. Increasing air temperature often induce disturbance of the PV power generation. However, the higher the air temperature in winter season, the higher PV power generation because the PV module may be more activated at higher air temperature. PV generating efficiency tends to be conversed the value of 15%.