• 한국농공학회논문집
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• 제63권5호
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• pp.63-71
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• 2021

Windbreak forests, which have a windproof effect against strong winds, are known to be effective in reducing the spread of odors and dust emitted from livestock farms. The effect of reducing the spread of odors and dust can be estimated through numerical models such as computational fluid dynamics, which require aerodynamic coefficients of the windbreaks for accurate prediction of their performance. In this study, we aimed to evaluate the aerodynamic coefficients, Co, C₁, C₂, and α, of two windbreaks, Thuja occidentalis and a mesh net, through wind tunnel experiments. The aerodynamic coefficients were derived by the relation between the incoming wind speed and the pressure loss due to the windbreaks which was measured by differential pressure sensors. In order to estimate the change in the aerodynamic coefficient concerning various leaf density, the experiments were conducted repeatedly by removing the leaves gradually in various stages. The results showed that the power law regression model more suitable for coefficient evaluation compared to the Darcy-Forchheimer model.

• 한국해양공학회지
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• 제37권5호
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• pp.181-189
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• 2023

The fuel consumption of marine diesel engines holds paramount importance in contemporary maritime transportation and shapes energy efficiency strategies of ocean-going vessels. Nonetheless, a noticeable gap in knowledge prevails concerning the influence of ship hull conditions and propeller roughness on fuel consumption. This study bridges this gap by utilizing artificial intelligence techniques in Matlab, particularly convolutional neural networks (CNNs) to comprehensively investigate these factors. We propose a time-series prediction model that was built on numerical simulations and aimed at forecasting ship hull and propeller conditions. The model's accuracy was validated through a meticulous comparison of predictions with actual ship-hull and propeller conditions. Furthermore, we executed a comparative analysis juxtaposing predictive outcomes with navigational environmental factors encompassing wind speed, wave height, and ship loading conditions by the fuzzy clustering method. This research's significance lies in its pivotal role as a foundation for fostering a more intricate understanding of energy consumption within the realm of maritime transport.

• 한국CDE학회논문집
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• 제17권1호
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• pp.45-53
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• 2012

In this paper, oil spreading simulation model is proposed for analyzing the oil spreading phenomenon rapidly when the ocean is polluted by the oil from a stranded ship. The space occupied by the ocean is converted into the latticed cell, and the each cell contains the information, such as the quantity of the oil, the temperature of the ocean, and the direction of current and wind. Two states, such as "clean" and "polluted" are defined in the each cell, and the oil in the cell spreads to the neighbor cells by the spreading rules. There are three spreading rules. First, the oil in the certain cell only spreads to the neighbor cells that contain larger oil than the certain cell. Second, the oil evaporates in proportion to the temperature of the ocean at the every time step. Third, the oil spreading property is affected by the direction and the speed of the current and the wind. The oil spreading simulation model of the each cell is defined by using the combined discrete event and discrete time simulation model architecture with the information and the spreading rules in the cell. The oil spreading simulation is performed when the oil of 10,000 kL is polluted in the ocean environment of 300 m by 300 m with various current and wind.

• 한국산업융합학회 논문집
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• 제26권4_2호
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• pp.715-724
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• 2023

Solar energy forecasting is essential for (1) power system planning, management, and operation, requiring accurate predictions. It is crucial for (2) ensuring a continuous and sustainable power supply to customers and (3) optimizing the operation and control of renewable energy systems and the electricity market. Recently, research has been focusing on developing solar energy forecasting models that can provide daily plans for power usage and production and be verified in the electricity market. In these prediction models, various data, including solar energy generation and climate data, are chosen to be utilized in the forecasting process. The most commonly used climate data (such as temperature, relative humidity, precipitation, solar radiation, and wind speed) significantly influence the fluctuations in solar energy generation based on weather conditions. Therefore, this paper proposes a hybrid forecasting model by combining the strengths of the Prophet model and the GRU model, which exhibits excellent predictive performance. The forecasting periods for solar energy generation are tested in short-term (2 days, 7 days) and medium-term (15 days, 30 days) scenarios. The experimental results demonstrate that the proposed approach outperforms the conventional Prophet model by more than twice in terms of Root Mean Square Error (RMSE) and surpasses the modified GRU model by more than 1.5 times, showcasing superior performance.

• 대한원격탐사학회지
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• 제38권6_1호
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• pp.1395-1406
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• 2022

본 연구는 대구 국채보상운동기념 공원 일대를 대상으로 수목이 주변 흐름과 기온에 미치는 영향을 조사하였다. 이를 위하여, 전산유체역학(computational fluid dynamics, CFD) 모델에 수목의 항력 효과와 냉각 효과를 반영하였고 현장 측정 결과와 비교하였다. 기상 유입 경계 조건은 기상청 국지예보모델(local data assimilation and prediction system, LDAPS) 자료를 사용하였다. 수목 유무에 따른 대기 흐름과 기온 분포를 조사하기 위하여, 수목이 존재하는 현재 상태와 수목이 존재하지 않는다고 가정한 상황에 대하여 수치 실험을 수행하였다. 수목이 없는 경우, 공원 내부에서는 장애물 영향이 작아 강한 풍속과 단조로운 흐름이 형성되었다. 기온은 풍속에 반비례하여 풍속이 강한(약한) 지역에서는 낮게(높게) 모의되었다. 반면, 수목이 존재하는 경우, 수목 항력(냉각) 효과는 식재 밀도가 높은 공원 내부의 풍속(기온)을 40 (5)% 이상 감소시켰고 공원 외부 지역까지 영향을 미쳤다. 또한 공원 내부에는 매우 복잡한 흐름이 형성된다. 도로변 근처에서도 가로수에 의해 풍속과 기온이 전체적으로 감소하지만, 수목에 의한 흐름 패턴 변화로 인해 오히려 풍속과 기온이 증가하는 지역도 발생했다. 수목 냉각 효과에 의한 기온 감소는 주간에는 4-6%였지만, 야간에는 1% 미만으로 미비했다. 수목 항력 효과에 의한 풍속 감소는 주·야간 모두 40% 이상 크게 나타났다.

• 대기
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• 제22권1호
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• pp.47-55
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• 2012

The characteristics of flow and pollutant dispersion for fire scenarios in an urban area are numerically investigated. A computational fluid dynamics (CFD) model coupled to a mesoscale weather research and forecasting (WRF) model is used in this study. In order to more accurately represent the effect of topography and buildings, the geographic information system (GIS) data is used as an input data of the CFD model. Considering prevailing wind, firing time, and firing points, four fire scenarios are setup in April 2008 when fire events occurred most frequently in recent five years. It is shown that the building configuration mainly determines wind speed and direction in the urban area. The pollutant dispersion patterns are different for each fire scenario, because of the influence of the detailed flow. The pollutant concentration is high in the horse-shoe vortex and recirculation zones (caused by buildings) close to the fire point. It thus means that the potential damage areas are different for each fire scenario due to the different flow and dispersion patterns. These results suggest that the accurate understanding of the urban flow is important to assess the effect of the pollutant dispersion caused by fire in an urban area. The present study also demonstrates that CFD model can be useful for the assessment of urban environment.

• 융합정보논문지
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• 제11권3호
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• pp.7-13
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• 2021

본 연구는 딥러닝(Deep Learning) 알고리즘 GAN 모델을 기반으로 초미세먼지(PM2.5) 인공지능 예측시스템을 개발한다. 실험 데이터는 시계열 축으로 생성된 온도, 습도, 풍속, 기압의 기상변화와 SO2, CO, O3, NO2, PM10와 같은 대기오염물질 농도와 밀접한 관련이 있다. 데이터 특성상, 현재시간 농도가 이전시간 농도에 영향을 받기 때문에 반복지도학습(Recursive Supervised Learning) 예측 모델을 적용하였다. 기존 모델인 CNN, LSTM의 정확도(Accuracy)를 비교분석을 위해 관측값(Observation Value)과 예측값(Prediction Value)간의 차이를 분석하고 시각화했다. 성능분석 결과 제안하는 GAN이 LSTM 대비 평가항목 RMSE, MAPE, IOA에서 각각 15.8%, 10.9%, 5.5%로 향상된 것을 확인하였다.

• 한국환경과학회지
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• 제28권8호
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• pp.701-707
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• 2019

Highland farming is agriculture that takes place 400 m above sea level and typically involves both low temperatures and long sunshine hours. Most highland Chinese cabbages are harvested in the Gangwon province. The Ubiquitous Sensor Network (USN) has been deployed to observe Chinese cabbages growth because of the lack of installed weather stations in the highlands. Five representative Chinese cabbage cultivation spots were selected for USN and meteorological data collection between 2015 and 2017. The purpose of this study is to develop a weight prediction model for Chinese cabbages using the meteorological and growth data that were collected one week prior. Both a regression and random forest model were considered for this study, with the regression assumptions being satisfied. The Root Mean Square Error (RMSE) was used to evaluate the predictive performance of the models. The variables influencing the weight of cabbage were the number of cabbage leaves, wind speed, precipitation and soil electrical conductivity in the regression model. In the random forest model, cabbage width, the number of cabbage leaves, soil temperature, precipitation, temperature, soil moisture at a depth of 30 cm, cabbage leaf width, soil electrical conductivity, humidity, and cabbage leaf length were screened. The RMSE of the random forest model was 265.478, a value that was relatively lower than that of the regression model (404.493); this is because the random forest model could explain nonlinearity.

• 한국빅데이터학회지
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• 제3권2호
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• pp.101-112
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• 2018

본 연구는 기계학습의 하나인 신경망 분석과 음이항 회귀분석을 활용하여 경찰신고건수를 예측하고자 2016년 6월부터 2017년 5월까지 충남지방경찰청에 접수된 112신고 데이터를 이용하여 예측모델을 개발하였다. 모델을 개발하기 위해 경찰신고건수에 영향을 줄 수 있는 시간, 휴일, 휴일 전날, 계절, 기온, 강수량, 풍속, 관할면적, 인구, 외국인 수, 단독주택비율, 기타주택비율 변수 등을 활용하였다. 변수의 종류에 따라 몇몇은 경찰신고건수와 양의 상관관계 또는 음의 상관관계가 확인되었다. 사용된 두 개의 방법론을 비교한바, 신경망분석의 예측 결과는 예측 값과 실제 값의 상관계수 0.7702, RMSE 2.557이고, 음이항 회귀분석은 상관계수 0.7158, RMSE 2.831으로 나타났다. 신경망분석은 해석가능성은 낮지만, 음이항 회귀분석에 비해 예측력이 뛰어나다는 것이 확인되었다. 향후 경찰관서에서 본 연구의 예측모델을 기초로 하여 최적의 경찰력 배치를 할 수 있을 것으로 기대된다.

• 대한기계학회논문집B
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• 제40권4호
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• pp.201-212
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• 2016

부산지역 가덕도 일대에 30MW 규모의 육상 풍력발전단지개발을 위한 풍황자원분석과 풍력터빈 최적배치를 수행하였다. 후보지역에 설치되어 운용중인 AWS(KMA)에서 측정된 바람 데이터를 이용하였으며, 데이터 품질분석을 통한 신뢰성 검토를 수행하였다. 1년간 측정된 AWS 데이터는 MERRA 재해석 데이터와 선형희귀(Linear regression method) MCP 기법의 적용을 통해 30년으로 장기 보정되었고, 이를 이용한 풍력터빈 최적배치를 수행하였다. 3MW 풍력터빈을 적용하여 총 25 조건의 풍력터빈 배치에 대한 최적배치를 수행하였으며, 다양한 후류모델을 적용하여 발전량해석을 수행하였다. 단지효율은 97.6%~98.7%, 연간이용률은 37.9%~38.3%로 예측되었고, 후류영향이 고려된 연간발전량이 99,598.4 MWh~100,732.9 MWh로 예측됨에 따라, 우수한 경제성을 갖는 풍력발전단지개발이 가능한 지역임을 확인하였다.