• Journal of Electrical Engineering and Technology
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• v.13 no.5
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• pp.1874-1885
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• 2018

The customer side operation is getting more complex in a smart grid environment because of the adoption of renewable resources. In performing energy management planning or scheduling, it is essential to forecast non-controllable resources accurately and robustly. The PV system is one of the common renewable energy resources in customer side. Its output depends on weather and physical characteristics of the PV system. Thus, weather information is essential to predict the amount of PV system output. However, weather forecast usually does not include enough solar irradiation information. In this study, a PV system power output prediction model (PPM) under limited weather information is proposed. In the proposed model, meteorological radiation model (MRM) is used to improve cloud cover radiation model (CRM) to consider the seasonal effect of the target region. The results of the proposed model are compared to the result of the conventional CRM prediction method on the PV generation obtained from a field test site. With the PPM, root mean square error (RMSE), and mean absolute error (MAE) are improved by 23.43% and 33.76%, respectively, compared to CRM for all days; while in clear days, they are improved by 53.36% and 62.90%, respectively.

• KIISE Transactions on Computing Practices
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• v.21 no.3
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• pp.247-256
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• 2015

Since the Gulf War, air power has played a key role. However, the effect of high-tech weapons, such as laser-guided bombs and electronic optical equipment, drops significantly if they do not match the weather conditions. So, aircraft that are assigned to carry laser-guided bombs must replace these munitions during bad weather conditions. But, there are no objective criteria for when weapons should be replaced. Therefore, in this paper, we propose an algorithm to predict the hit rate of laser-guided bombs using cloud image processing. In order to verify the accuracy of the algorithm, we applied the weather conditions that may affect laser-guided bombs to simulated flight equipment and executed simulated weapon release, then collected and analyzed data. Cloud images appropriate to the weather conditions were developed, and applied to the algorithm. We confirmed that the algorithm can accurately predict the hit rate of laser-guided bombs in most weather conditions.

• Journal of Navigation and Port Research
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• v.35 no.5
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• pp.387-392
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• 2011

The value of Photovoltaic as an independent power supply is small, but the city's carbon emissions reduction and for the reduction of fossil fuel use distributed power is the power source to a very high value. However, according to the weather conditions for solar power generation by power fluctuations because of the size distribution to be effective, the big swing for effectively controlling real-time monitoring should be made. But that depends on solar power generation solar radiation forecasts from the National Weather Service does not need to predict it, and this study, the diffuse sky radiation in the history of the solar radiation in the darkness of the clouds, thick and weather forecasts can be inferred from the atmospheric transmittance to announce this value is calculated to represent each weather forecast solar radiation and solar radiation predicted by substituting the expression And the measured solar radiation and CRM (Cloud Cover Radiation Model) technique with an expression of Kasten and Czeplak irradiation when compared to the calculated predictions were verified.

• Journal of the Economic Geographical Society of Korea
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• v.15 no.2
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• pp.304-313
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• 2012

Uncertainty increases as a result of environment change and change of individual decision-making in extreme weather. This study consider individual decision-making which has been not covered until now. The purpose of this study is making Agent-Based Model to predict it more accurate that how much change travel demand in heavy rain and heavy snow. Through this model, it can be utilized to forecast travel demand, changes in travel behavior and traffic patterns. It will be also possible to predict discomfort index and risk of accidents.

• Journal of the Korean Solar Energy Society
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• v.33 no.6
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• pp.62-69
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• 2013

In this paper, Korea Institute of Energy Research, building integrated renewable energy monitoring system that utilizes solar power generation forecast data forecast model is proposed. Renewable energy integration of real-time monitoring system based on monitoring data were building a database and the database of the weather conditions and to study the correlation structure was tailoring. The weather forecast cloud cover data, generation data, and solar radiation data, a data mining and time series analysis using the method developed models to forecast solar power. The development of solar power in order to forecast model of weather forecast data it is important to secure. To this end, in three hours, including a three-day forecast today Meteorological data were used from the KMA(korea Meteorological Administration) site offers. In order to verify the accuracy of the predicted solar circle for each prediction and the actual environment can be applied to generation and were analyzed.

• Water Engineering Research
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• v.3 no.2
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• pp.113-122
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• 2002

Accurate quantitative forecasting of rainfall for basins with a short response time is essential to predict flash floods. In this study, a Quantitative Flood Forecasting (QFF) model was developed by incorporating the evolving structure and frequency of intense weather systems and by using neural network approach. Besides using radiosonde and rainfall data, the model also used the satellite-derived characteristics of storm systems such as tropical cyclones, mesoscale convective complex systems and convective cloud clusters as input. The convective classification and tracking system (CCATS) was used to identify and quantify storm properties such as lifetime, area, eccentricity, and track. As in standard expert prediction systems, the fundamental structure of the neural network model was learned from the hydroclimatology of the relationships between weather system, rainfall production and streamflow response in the study area. All these processes stretched leadtime up to 18 hours. The QFF model will be applied to the mid-Atlantic region of United States in a forthcoming paper.

• Journal of the Korean Society for Aviation and Aeronautics
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• v.18 no.1
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• pp.89-99
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• 2010

The purpose of this study is analysis of Korean peninsula aircraft turbulence using the numerical weather prediction model, KWRF with the various turbulence index and pilot weather report data. Compared with the pilot weather report data and Calculated the turbulence index using the KWRF model result, many turbulence index show the similar horizontal distribution, except for the TUB2 and VWS. The analysis of vertical structure of turbulence, there are some difference each turbulence index respectively, but severe turbulence turn up in 15,000ft almost turbulence index. above 20,000ft height, intensity of turbulence vary each turbulence index. Through this turbulence study, It is founded on the research and development of the Korean peninsula aircraft turbulence

• Proceedings of the Korean Institute of Building Construction Conference
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• 2018.11a
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• pp.59-60
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• 2018

The purpose of this study is to predict and classify the accident types based on the KOSHA (Korea Occupational Safety & Health Agency) and weather data. We also have an effort to suggest an important management method according to accident types by deriving feature importance. We designed two models based on accident data and weather data (model(a)) and only weather data (model(b)). As a result of random forest method, the model(b) showed a lack of accuracy in prediction. However, the model(a) presented more accurate prediction results than the model(b). Thus we presented safety management plan based on the results. In the future, this study will continue to carry out real time prediction to occurrence types to prevent safety accidents by supplementing the real time accident data and weather data.

• Journal of information and communication convergence engineering
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• v.21 no.3
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• pp.225-232
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• 2023

Owing to advancements in intelligent transportation systems (ITS) and artificial-intelligence technologies, various machine-learning models can be employed to simulate and predict the number of traffic accidents under different weather conditions. Furthermore, we can analyze the relationship between weather and traffic accidents, allowing us to assess whether the current weather conditions are suitable for travel, which can significantly reduce the risk of traffic accidents. In this study, we analyzed 30000 traffic flow data points collected by traffic cameras at nearby intersections in Washington, D.C., USA from October 2012 to May 2017, using Pearson's heat map. We then predicted, analyzed, and compared the performance of the correlation between continuous features by applying several machine-learning algorithms commonly used in ITS, including random forest, decision tree, gradient-boosting regression, and support vector regression. The experimental results indicated that the gradient-boosting regression machine-learning model had the best performance.

• Korean Journal of Construction Engineering and Management
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• v.7 no.4 s.32
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• pp.100-108
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• 2006

Non-working days affected by weather conditions of the construction-sites have been estimated without proper data. They are usually estimated based on project engineer's own experience and intuition. As a result, they cause not only economic loss to time-adjustment but also conflicts among project participants. It becomes more difficult to predict the weather nowadays than before due to tendency of recently weather change. Therefore, this paper presents an improved estimation method for non-working days, which could minimize estimation errors. The estimation method is developed based on analysis of regional characteristics and weather conditions which affect project duration.