국내 지역별 일사량을 고려한 버스 탑재형 태양광 패널의 발전량 예측
Prediction of Power Generation of Bus-Mounted Solar Panels Considering Insolation by Region in Republic of Korea
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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.
Abstract
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.
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