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.

신·재생에너지 보급통계에 의하면 바이오매스 발전실적은 2013년 부터 급증하고 있으며 그 중에서 가장 급격하게 증가한 연료는 Wood pellet으로 2013년 696Gwh, 2014년 2,764Gwh, 2015년에는 2,512Gwh를 발전 하였고 국내 Wood pellet 총 소비량은 2015년 기준 148만톤이며 그 중 발전용으로 소비된 Wood pellet은 108만톤으로 약 73%를 차지하고 있다. 본 연구에서 Wood pellet 소요량을 예측한 결과 국내 발전용으로 필요한 Wood pellet 소요량은 2020년 261만톤, 2025년 685만톤, 2030년 1,139만톤이 필요하며, 최적 바이오매스 발전량 산정을 위하여 바이오매스 발전소에서 국내 생산 Wood pellet 사용량을 50% 사용한다는 가정하에 기허가 신청된 발전소를 가동하기 위해서는 2021년 226만 톤의 Wood pellet이 국내에서 생산되어야 한다는 결론이 도출되었다.

최근 WMO는 온실가스 배출량 시나리오(SRES)를 대신하여 대표농도경로(RCP)를 바탕으로 새로운 기후변화 시나리오를 생산하였으며 기상연구소는 RCP 시나리오를 바탕으로 한반도의 새로운 기후변화 시나리오를 생산하였다. 본 연구에서는 과거 관측값을 바탕으로 평년(1981-2010)의 애멸구의 우화시기와 세대수를 추정하였으며, RCP 8.5 시나리오를 바탕으로 2020년대(2015-2024), 2050년대(2045-2054)와 2090년대(2085-2094) 애멸구의 우화시기와 세대수를 예측하였다. 평년 애멸구 월동 1세대수의 우화일인 176.0±0.97일과 비교하여 2050년대에서는 13.2±0.18일(162.8±0.91일), 2090년대에는 32.1±0.61일(143.9±1.08일) 앞당겨질 것을 예측되었다. 그리고 애멸구의 연간 세대수는 2050년대에서는 현재보다 2.0±0.02세대, 2090년대에는 5.2±0.06세대 증가할 것으로 예측되었다.

This study was performed to prediction of generation and estimation of recycling value on waste artificial turf. The artificial turf consist of a different components by playground type, and combined of plastic, silica, and rubber materials. The weight per unit area of artificial turf is about 67.5% of the silica that is the highest, and infill rubber powder, pile, backing in order. As the result of investigation on artificial turf installation area from 2003 to 2012, the school playground is the largest portion because the development business plan of variety school grounds by government. And installed artificial turf will be discharge as the end of lifespan from 2011 to 2020. As the results of generation prediction by trend analysis, logarithmic function was estimated the most optimum method among the trend analysis. If 86.9% is recycled by Case II, the valuable materials of waste artificial turf was estimated that an annual average of about 2,990 tons of pile, about 2,177 tons of backing, about 52,803 tons of quartz sand, and about 20,241 tons of infill rubber powder in 2021 ~ 2040, respectively. It was evaluated to efficient recycling method of waste artificial turf that separated into the fabric and infill materials through first screening, and then infill materials separated into the silica and rubber powder through second screening.

This study was performed to improve water demand estimation and analize correlation between generation of domestic sewage and domestic water use. To improve the prediction of water demand estimation, new water demand equation was developed. The results is as follows. InQt = β0 + β₁InPt + β₂InYt + β₃InHt + εt By using the statistical analysis of the "generation of domestic sewage" and "domestic water use", the regression equation between them is formed. The result is as follows. ＿ Generation of domestic sewage = 0.8487 × Domestic water use + 684.57 (R² = 0.972)