• Advances in environmental research
• /
• v.4 no.4
• /
• pp.219-231
• /
• 2015

In recent years, raising air pollutants has become as a big concern, especially in metropolitan cities such as Tehran. Therefore, forecasting the level of pollutants plays a significant role in air quality management. One of the forecasting tools that can be used is an artificial neural network which is able to model the complicated process of air pollution. In this study, we applied two different methods of artificial neural networks, the Multilayer Perceptron (MLP) and Radial Basis Function (RBF), to predict the hourly air concentrations of toluene in Tehran. Hourly temperature, wind speed, humidity and $NO_x$ were selected as inputs. Both methods had acceptable results; however, the RBF neural network produced better results. The coefficient of determination ($R^2$) between the observed and predicted data was 0.9642 and 0.99 for MLP and RBF neural networks, respectively. The results of the mean bias errors (MBE) were 0.00 and -0.014 for RBF and MLP, respectively which indicate the adequacy of the models. The index of agreement (IA) between the observed and predicted data was 0.999 and 0.994 in the RBF and the MLP, respectively which indicates the efficiency of the models. Finally, sensitivity analysis related to the MLP neural network determined that temperature was the most significant factor in air concentration of toluene in Tehran which may be due to the volatile nature of toluene.

• Journal of Advanced Navigation Technology
• /
• v.21 no.6
• /
• pp.643-650
• /
• 2017

Photovoltaic generation which has unlimited energy sources are very intermittent because they depend on the weather. Therefore, it is necessary to get accurate generation prediction with reducing the uncertainty of photovoltaic generation and improvement of the economics. The Meteorological Agency predicts weather factors for three days, but doesn't predict the sunshine and solar radiation that are most correlated with the prediction of photovoltaic generation. In this study, we predict sunshine and solar radiation using weather, precipitation, wind direction, wind speed, humidity, and cloudiness which is forecasted for three days at Meteorological Agency. The photovoltaic generation forecasting model is proposed by using predicted solar radiation and sunshine. As a result, the proposed model showed better results in the error rate indexes such as MAE, RMSE, and MAPE than the model that predicts photovoltaic generation without radiation and sunshine. In addition, DNN showed a lower error rate index than using SVM, which is a type of machine learning.

• Journal of the Korean Applied Science and Technology
• /
• v.32 no.1
• /
• pp.62-71
• /
• 2015

In order to deal with the globally increasing electric power demand and reduce $CO_2$ emission, complex thermoelectric power plants are being constructed in densely populated downtown areas. As the environmental regulations are continuously strengthened, various facilities like low NOx burner and SCR are being installed to reduce NOx emission. This study is applied using the TMS emission of $NO_2$ from combined heat and power plant located in Goyang-si Gyeonggi-do. Applying data to the computational fluid dynamics(CFD), and compared with the actual measurement results. It is judged that even though there might be differences between actual measurements and CFD results due to the instant changes of wind direction and wind speed according to measurement time during measurement period, modeling results and actual measurement results showed similar concentration at most forecasting areas and therefore, the forecasting concentration could be deducted which is close to actual measurement by calculating the contribution concentration considering the surrounding concentration in the future.

• Journal of Advanced Navigation Technology
• /
• v.22 no.3
• /
• pp.233-239
• /
• 2018

Since solar power generation is intermittent depending on weather conditions, it is necessary to predict the accurate generation amount of solar power to improve the efficiency and economical efficiency of solar power generation. This study proposes a short - term deep learning prediction model of solar power generation using meteorological data from Mokpo meteorological agency and generation data of Yeongam solar power plant. The meteorological agency forecasts weather factors such as temperature, precipitation, wind direction, wind speed, humidity, and cloudiness for three days. However, sunshine and solar radiation, the most important meteorological factors for forecasting solar power generation, are not predicted. The proposed model predicts solar radiation and solar radiation using forecast meteorological factors. The power generation was also forecasted by adding the forecasted solar and solar factors to the meteorological factors. The forecasted power generation of the proposed model is that the average RMSE and MAE of DNN are 0.177 and 0.095, and RNN is 0.116 and 0.067. Also, LSTM is the best result of 0.100 and 0.054. It is expected that this study will lead to better prediction results by combining various input.

• Nuclear Engineering and Technology
• /
• v.50 no.1
• /
• pp.68-79
• /
• 2018

The Ki-Jang research reactor (KJRR), a new research reactor in Korea, is being planned to fulfill multiple purposes. In this study, as an assessment of the environmental radiological impact, we characterized the atmospheric dispersion and deposition of radioactive materials released by an unexpected incident at KJRR using the weather research and forecasting-mesoscale model interface program-California Puff (WRF-MMIF-CALPUFF) model system. Based on the reproduced three-dimensional gridded meteorological data obtained during a 1-year period using WRF, the overall meteorological data predicted by WRF were in agreement with the observed data, while the predicted wind speed data were slightly overestimated at all stations. Based on the CALPUFF simulation of atmospheric dispersion (${\chi}/Q$) and deposition (D/Q) factors, relatively heavier contamination in the vicinity of KJRR was observed, and the prevailing land breeze wind in the study area resulted in relatively higher concentration and deposition in the off-shore area sectors. We also compared the dispersion characteristics between the PAVAN (atmospheric dispersion of radioactive release from nuclear power plants) and CALPUFF models. Finally, the meteorological conditions and possibility of high doses of radiation for relatively higher hourly ${\chi}/Q$ cases were examined at specific discrete receptors.

• Communications for Statistical Applications and Methods
• /
• v.30 no.2
• /
• pp.119-133
• /
• 2023

With the rapid growth of the economy and fossil fuel consumption, the concentration of air pollutants has increased significantly and the air pollution problem is no longer limited to small areas. We conduct statistical analysis with the actual data related to air quality that covers the entire of South Korea using R and Python. Some factors such as SO₂, CO, O₃, NO₂, PM₁₀, precipitation, wind speed, wind direction, vapor pressure, local pressure, sea level pressure, temperature, humidity, and others are used as covariates. The main goal of this paper is to predict air quality index (AQI) spatio-temporal data. The observations of spatio-temporal big datasets like AQI data are correlated both spatially and temporally, and computation of the prediction or forecasting with dependence structure is often infeasible. As such, the likelihood function based on the spatio-temporal model may be complicated and some special modelings are useful for statistically reliable predictions. In this paper, we propose several methods for this big spatio-temporal AQI data. First, random effects with spatio-temporal basis functions model, a classical statistical analysis, is proposed. Next, neural networks model, a deep learning method based on artificial neural networks, is applied. Finally, random forest model, a machine learning method that is closer to computational science, will be introduced. Then we compare the forecasting performance of each other in terms of predictive diagnostics. As a result of the analysis, all three methods predicted the normal level of PM_2.5 well, but the performance seems to be poor at the extreme value.

• Atmosphere
• /
• v.22 no.3
• /
• pp.321-330
• /
• 2012

Generation mechanisms of the three moderate-or-greater (MOG)-level clear-air turbulence (CAT) encounters over South Korea are investigated using the Weather Research and Forecasting (WRF) model. The cases are selected among the MOG-level CAT events occurred in Korea during 2002-2008 that are categorized into three different generation mechanisms (upper-level front and jet stream, anticyclonic flow, and mountain waves) in the previous study by Min et al. For the case at 0127 UTC 18 Jun 2003, strong vertical wind shear (0.025 $s^{-1}$) generates shearing instabilities below the enhanced upper-level jet core of the maximum wind speed exceeding 50 m $s^{-1}$, and it induces turbulence near the observed CAT event over mid Korea. For the case at 2330 UTC 22 Nov 2006, areas of the inertia instability represented by the negative absolute vorticity are formed in the anticyclonically sheared side of the jet stream, and turbulence is activated near the observed CAT event over southwest of Korea. For the case at 0450 UTC 16 Feb 2003, vertically propagating mountain waves locally trigger shearing instability (Ri < 0.25) near the area where the background Richardson number is sufficiently small (0.25 < Ri < 1), and it induces turbulence near the observed CAT over the Eastern mountainous region of South Korea.

• International Journal of Advanced Culture Technology
• /
• v.6 no.4
• /
• pp.190-194
• /
• 2018

The runway visual range is one of the important factors that decide the possibility of taking offs and landings of the airplane at local airports. The runway visual range is affected by weather conditions like fog, wind, etc. The pilots and aviation related workers check a local weather forecast such as runway visual range for safe flight. However there are several local airfields at which no other forecasting functions are provided due to realistic problems like the deterioration, breakdown, expensive purchasing cost of the measurement equipment. To this end, this study proposes a prediction model of runway visual range for a local airport by applying convolutional neural network that has been most commonly used for image/video recognition, image classification, natural language processing and so on to the prediction of runway visual range. For constituting the prediction model, we use the previous time series data of wind speed, humidity, temperature and runway visibility. This paper shows the usefulness of the proposed prediction model of runway visual range by comparing with the measured data.

• Journal of the Korean Society for Geothermal and Hydrothermal Energy
• /
• v.14 no.3
• /
• pp.15-20
• /
• 2018

To study the effects of solar energy in a greenhouse, outdoor air temperature and wind speed on inside air temperature, a simulation model for forecasting the greenhouse air temperature was conducted on the basis of the energy and mass balance theory. Application of solar energy to the greenhouse is major area in the renewable energy research and development in order to save energy. Recently, considering the safety and efficiency of the heating of greenhouse, clean energy such as geothermal and solar energy has received much attention. The analysed greenhouse has $50m^2$ of ground area which located in jocheon-ri of Jeju Province. Experiments were carried out to collect data to validate the model. The results showed that the simulated air temperature inside a plastic greenhouse agreed well with the measured data.

• Journal of Ocean Engineering and Technology
• /
• v.37 no.5
• /
• pp.181-189
• /
• 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.