• 한국항행학회논문지
• /
• 제22권3호
• /
• pp.233-239
• /
• 2018

태양광 발전은 기상 상태에 따라 간헐적이기 때문에 태양광 발전의 효율과 경제성 향상을 위해 정확한 발전량 예측이 요구된다. 본 연구는 목포 기상대에서 예보하는 기상 데이터와 영암 태양광 발전소의 발전량 데이터를 이용하여 태양광 발전량 단기 딥러닝 예측모델을 제안하였다. 기상청은 기온, 강수량, 풍향, 풍속, 습도, 운량 등의 기상요소를 3일간 예보한다. 그러나 태양광 발전량 예측에 가장 중요한 기상요소인 일조 및 일사 일사량 예보하지 않는다. 제안 모델은 예보 기상요소를 이용하여, 일조 및 일사 일사량을 예측 하였다. 또한 발전량은 기상요소에 예측된 일조 및 일사 기상요소를 추가하여 예측하였다. 제안 모델의 발전량 예측 결과 DNN의 평균 RMSE와 MAE는 0.177과 0.095이며, RNN은 0.116과 0.067이다. 또한, LSTM은 가장 좋은 결과인 0.100과 0.054이다. 향후 본 연구는 다양한 입력요소의 결합으로 보다 향상된 예측결과를 도출할 수 있을 것으로 기대된다.

• 한국환경과학회지
• /
• 제27권6호
• /
• pp.425-436
• /
• 2018

To establish initial response scenarios for nuclear accidents around the Kori nuclear power plants, the potential for radionuclide diffusion was estimated using numerical experiments and statistical techniques. This study used the numerical model WRF (Weather Research and Forecasting) and FLEXPART (Flexible Particle dispersion model) to calculate the three-dimensional wind field and radionuclide dispersion, respectively. The wind patterns observed at Gijang, near the plants, and at meteorological sites in Busan, were reproduced and applied to estimates of seasonally averaged wind fields. The distribution of emitted radionuclides are strongly associated with characteristics of topography and synoptic wind patterns over nuclear power plants. Since the terrain around the power plants is complex, estimates of radionuclide distribution often produce unexpected results when wind data from different sites are used in statistical calculations. It is highly probable that in the summer and autumn, radionuclides move south-west, towards the downtown metropolitan area. This study has clear limitations in that it uses the seasonal wind field rather than the daily wind field.

• 한국컴퓨터정보학회논문지
• /
• 제16권11호
• /
• pp.245-253
• /
• 2011

화석 연료의 무분별한 사용으로 환경이 심각하게 오염되고, 화석 연료의 고갈에 대한 문제가 대두됨에 따라서 화석 연료에 대한 문제를 해결 할 수 있는 대체 에너지원에 대해 관심이 집중되기 시작하였다. 현재 신재생 에너지 중에서 가장 각광을 받고 있는 에너지는 중에 하나가 풍력에너지이다. 풍력에너지 발전단지와 기존의 전력 발전소는 소비되는 전력에 대한 생산의 균형을 맞춰야하며, 풍력에너지단지에서 균형적인 생산을 하기 위해서는 풍력에너지에 대한 분석 및 예측이 필요하다. 이를 위해서 데이터마이닝 분야의 예측 기법이 활용 될 수 있다. 본 논문에서는 풍력 데이터를 이용하여 발전 패턴을 예측하기 위해 SOM(Self-Organizing Feature Map) Clustering 기법과 의사결정나무(decision tree)를 이용한 연구를 진행하였다. 즉, 1) 풍력 데이터의 누락된 데이터와 이상치 데이터를 처리하기 위하여, 전처리 과정을 수행하였고, 이 과정에서 특징 벡터를 추출하였다. 2) 전처리 단계를 거쳐 정제되고 정규화된 데이터 집합을 MIA(Mean Index Adequacy) 척도와 SOM Clustering 기법에 적용하여 대표 발전 패턴을 찾아내고 각각의 데이터에 해당하는 대표 패턴을 클래스 레이블로 할당하도록 하였다. 3) 의사결정나무 기반의 분류 기법에 데이터 집합을 적용시켜 새로운 풍력에너지에 대한 분석 및 예측 모델을 생성하였다. 실험 결과, 의사결정나무를 통한 풍력에너지 발전 패턴을 예측하기 위한 모델을 구축하였다.

• 한국응용과학기술학회지
• /
• 제32권1호
• /
• pp.62-71
• /
• 2015

세계적으로 급증하는 전력수요에 대처하고, $CO_2$ 배출을 줄이고자 인구가 밀집되어 있는 도심지에 복합화력 발전소가 건설되고 있다. 환경규제가 계속적으로 강화됨에 따라 NOx 배출량을 줄이고자 저 NOx 버너, SCR 등 여러 가지 설비들을 설치하고 있다. 본 연구는 경기도 고양시 소재의 일산열병합발전소 1개소에서 배출되는 질소산화물을 TMS를 이용하여 배출계수를 산정하여 이를 전산유체동역학(CFD)에 적용하여 질소산화물의 거동을 살펴보고, 현장 실측 결과와 비교 검토하였다. 실측 기간 중 측정 시간에 따른 주 풍향 풍속의 순간적인 변화로 인해 실측 결과와 CFD 모델링 결과의 차이가 나타날 수 있으나, 모델링 결과와 실측 결과는 대부분 예측지점에서 유사한 농도로 나타났다. 향후 주변농도를 고려한 기여농도를 산출하여 실측농도에 가까운 예측농도 도출이 가능 할 것으로 판단된다.

• Journal of Electrical Engineering and Technology
• /
• 제12권5호
• /
• pp.1709-1718
• /
• 2017

This paper presents a hybrid stochastic deterministic multi-timescale scheduling (SDMS) approach for generation scheduling of a power grid. SDMS considers flexible resource options including conventional generation flexibility in a chance-constrained day-ahead scheduling optimization (DASO). The prime objective of the DASO is the minimization of the daily production cost in power systems with high penetration scenarios of variable generation. Furthermore, energy storage is scheduled in an hourly-ahead deterministic real-time scheduling optimization (RTSO). DASO simulation results are used as the base starting-point values in the hour-ahead online rolling RTSO with a 15-minute time interval. RTSO considers energy storage as another source of grid flexibility, to balance out the deviation between predicted and actual net load demand values. Numerical simulations, on the IEEE RTS test system with high wind penetration levels, indicate the effectiveness of the proposed SDMS framework for managing the grid flexibility to meet the net load demand, in both day-ahead and real-time timescales. Results also highlight the adequacy of the framework to adjust the scheduling, in real-time, to cope with large prediction errors of wind forecasting.

• Wind and Structures
• /
• 제20권6호
• /
• pp.763-791
• /
• 2015

"Wind and Ports" is a European project that has been carried out since 2009 to handle wind forecast in port areas through an integrated system made up of an extensive in-situ wind monitoring network, the numerical simulation of wind fields, the statistical analysis of wind climate, and algorithms for medium-term (1-3 days) and short term (0.5-2 hours) wind forecasting. The in-situ wind monitoring network, currently made up of 22 ultrasonic anemometers, provides a unique opportunity for detecting high resolution thunderstorm records and studying their dominant characteristics relevant to wind engineering with special concern for wind actions on structures. In such a framework, the wind velocity of thunderstorms is firstly decomposed into the sum of a slowly-varying mean part plus a residual fluctuation dealt with as a non-stationary random process. The fluctuation, in turn, is expressed as the product of its slowly-varying standard deviation by a reduced turbulence component dealt with as a rapidly-varying stationary Gaussian random process with zero mean and unit standard deviation. The extraction of the mean part of the wind velocity is carried out through a moving average filter, and the effect of the moving average period on the statistical properties of the decomposed signals is evaluated. Among other aspects, special attention is given to the thunderstorm duration, the turbulence intensity, the power spectral density and the integral length scale. Some noteworthy wind velocity ratios that play a crucial role in the thunderstorm loading and response of structures are also analyzed.

• Wind and Structures
• /
• 제36권2호
• /
• pp.121-131
• /
• 2023

Aiming at the problem that fatigue characteristics of metal roof rely on local physical tests and lacks the cyclic load sequence matching with regional climate, this paper proposed a method of constructing the fatigue load spectrum based on integration of wind load model, measured data of long-span metal roof and climate statistical data. According to the turbulence characteristics of wind, the wind load model is established from the aspects of turbulence intensity, power spectral density and wind pressure coefficient. Considering the influence of roof configuration on wind pressure distribution, the parameters are modified through fusing the measured data with least squares method to approximate the actual wind pressure load of the roof system. Furthermore, with regards to the wind climate characteristics of building location, Weibull model is adopted to analyze the regional meteorological data to obtain the probability density distribution of wind velocity used for calculating wind load, so as to establish the cyclic wind load sequence with the attributes of regional climate and building configuration. Finally, taking a workshop's metal roof as an example, the wind load spectrum is constructed according to this method, and the fatigue simulation and residual life prediction are implemented based on the experimental data. The forecasting result is lightly higher than the design standards, consistent with general principles of its conservative safety design scale, which shows that the presented method is validated for the fatigue characteristics study and health assessment of metal roof.

• 한국환경과학회지
• /
• 제19권2호
• /
• pp.171-184
• /
• 2010

Accurate simulation of the meteorological field is very important to assess the wind resources. Some researchers showed that sea surface temperature (SST) plays a leading role on the local meterological simulation. New Generation Sea Surface Temperature (NGSST), Operational Sea Surface Temperature and Sea Ice Analysis (OSTIA), and Real-Time Global Sea Surface Temperature (RTG SST) have different spatial distribution near the coast and OSTIA shows the best accuracy compared with buoy data in the southeastern coast of the Korean Peninsula. Those SST products are used to initialize the Weather Research and Forecasting (WRF) Model for November 13-23 2008. The simulation of OSTIA shows better result in comparison with NGSST and RTG SST. NGSST shows a large difference with OSTIA in horizontal and vertical wind fields during the weak synoptic condition, but wind power density shows a large difference during strong synoptic condition. RTG SST shows the similar patterns but smaller the magnitude and the extent.

• Wind and Structures
• /
• 제37권4호
• /
• pp.315-329
• /
• 2023

We investigated the wind characteristics in the near-surface layer during the landfall of Typhoon Mujigae (2015) based on observations from wind towers in the coastal areas of Guandong province. Typhoon Mujigae made landfall in this region from 01:00 UTC to 10:00 UTC on October 4, 2015. In the region influenced by the eyewall of the tropical cyclone, the horizontal wind speed was characterized by a double peak, the wind direction changed by >180°, the vertical wind speed increased by three to four times, and the angle of attack increased significantly to a maximum of 7°, exceeding the recommended values in current design criteria. The vertical wind profile may not conform to a power law distribution in the near-surface layer in the region impacted by the eyewall and spiral rainband. The gust factors were relatively dispersed when the horizontal wind speed was small and tended to a smaller value and became more stable with an increase in the horizontal wind speed. The variation in the gust factors was the combined result of the height, wind direction, and circulation systems of the tropical cyclone. The turbulence intensity and the downwind turbulence energy spectrum both increased notably in the eyewall and spiral rainband and no longer satisfied the assumption of isotropy in the inertial subrange and the -5/3 law. This result was more significant in the eyewall area than in the spiral rainband. These results provide a reference for forecasting tropical cyclones, wind-resistant design, and hazard prevention in coastal areas of China to reduce the damage caused by high winds induced by tropical cyclones.

• 대기
• /
• 제34권2호
• /
• pp.177-185
• /
• 2024

As increasing global interest in renewable energy due to the ongoing climate crisis, there is a growing need for efficient technologies to manage such resources. This study focuses on the predictive skill of daily solar power generation using weather observation and forecast data. Meteorological data from the Korea Meteorological Administration and solar power generation data from the Korea Power Exchange were utilized for the period from January 2017 to May 2023, considering both inland (Daejeon) and coastal (Busan) regions. Temperature, wind speed, relative humidity, and precipitation were selected as relevant meteorological variables for solar power prediction. All data was preprocessed by removing their systematic components to use only their residuals and the residual of solar data were further processed with weighted adjustments for homoscedasticity. Four models, MLR (Multiple Linear Regression), RF (Random Forest), DNN (Deep Neural Network), and RNN (Recurrent Neural Network), were employed for solar power prediction and their performances were evaluated based on predicted values utilizing observed meteorological data (used as a reference), 1-day-ahead forecast data (referred to as fore1), and 2-day-ahead forecast data (fore2). DNN-based prediction model exhibits superior performance in both regions, with RNN performing the least effectively. However, MLR and RF demonstrate competitive performance comparable to DNN. The disparities in the performance of the four different models are less pronounced than anticipated, underscoring the pivotal role of fitting models using residuals. This emphasizes that the utilized preprocessing approach, specifically leveraging residuals, is poised to play a crucial role in the future of solar power generation forecasting.