• Journal of Electrical Engineering and Technology
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• v.12 no.2
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• pp.969-980
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• 2017

Galloping is one of the most serious vibration problems in transmission lines. Power lines can be extensively damaged owing to aerodynamic instabilities caused by ice accretion. In this study, the accident probability induced by galloping phenomenon was analyzed using logistic regression analysis. As former studies have generally concluded, main factors considered were local weather factors and physical factors of power delivery systems. Since the number of transmission towers outnumbers the number of weather observatories, interpolation of weather factors, Kriging to be more specific, has been conducted in prior to forming galloping accident estimation model. Physical factors have been provided by Korea Electric Power Corporation, however because of the large number of explanatory variables, variable selection has been conducted, leaving total 11 variables. Before forming estimation model, with 84 provided galloping cases, 840 non-galloped cases were chosen out of 13 billion cases. Prediction model for accidents by galloping has been formed with logistic regression model and validated with 4-fold validation method, corresponding AUC value of ROC curve has been used to assess the discrimination level of estimation models. As the result, logistic regression analysis effectively discriminated the power lines that experienced galloping accidents from those that did not.

• The Korean Journal of Applied Statistics
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• v.30 no.6
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• pp.1005-1013
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• 2017

In this paper, we investigate the performances of time series models to forecast irradiance that consider weather variables such as temperature, humidity, cloud cover and Global Horizontal Irradiance. We first introduce the time series models and show that regression ARIMAX has the best performance with other models such as ARIMA and multiple regression models.

• Journal of Korean Society of Rural Planning
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• v.24 no.3
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• pp.25-31
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• 2018

This study analyzed factors influencing cultivation area of two major apple cultivars, Fuji and Hongro, applying the panel SUR model to survey data from farms. Characteristics of farms, distribution factors, and weather factors were the independent variables of the model. The analysis indicated that characteristics of farms, distribution factors, and weather factors influence the cultivation area of Hongro and Fuji. The independent variables were also found to have different levels of influence on increase and decrease of the cultivated area. Helping predict changes in cultivation area of Hongro and Fuji, the research results can be used as primary data to support efforts to prevent price fluctuations due to changes in supply.

• Proceedings of the Korea Water Resources Association Conference
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• 2015.05a
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• pp.479-482
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• 2015

The objective of this study is to develop adaptive neuro-fuzzy inference system (ANFIS) model for estimating daily solar radiation using limited weather variables at Champaign and Springfield stations in Illinois. The best input combinations (one, two, and three inputs) can be identified using ANFIS model. From the performance evaluation and scatter diagrams of ANFIS model, ANFIS 3 (three input) model produces the best results for both stations. Results obtained indicate that ANFIS model can successfully be used for the estimation of daily global solar radiation at Champaign and Springfield stations in Illinois. These results testify the generation capability of ANFIS model and its ability to produce accurate estimates in Illinois.

• Atmosphere
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• v.27 no.4
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• pp.385-398
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• 2017

Numerical weather prediction experiments were carried out by applying topographic effects to reduce or enhance the solar radiation by terrain. In this study, x and ${\kappa}({\phi}_o,\;{\theta}_o)$ are precalculated for topographic effect on high resolution numerical weather prediction (NWP) with 1 km spatial resolution, and meteorological variables are analyzed through the numerical experiments. For the numerical simulations, cases were selected in winter (CASE 1) and summer (CASE 2). In the CASE 2, topographic effect was observed on the southward surface to enhance the solar energy reaching the surface, and enhance surface temperature and temperature at 2 m. Especially, the surface temperature is changed sensitively due to the change of the solar energy on the surface, but the change of the precipitation is difficult to match of topographic effect. As a result of the verification using Korea Meteorological Administration (KMA) Automated Weather System (AWS) data on Seoul metropolitan area, the topographic effect is very weak in the winter case. In the CASE 1, the improvement of accuracy was numerically confirmed by decreasing the bias and RMSE (Root mean square error) of temperature at 2 m, wind speed at 10 m and relative humidity. However, the accuracy of rainfall prediction (Threat score (TS), BIAS, equitable threat score (ETS)) with topographic effect is decreased compared to without topographic effect. It is analyzed that the topographic effect improves the solar radiation on surface and affect the enhancements of surface temperature, 2 meter temperature, wind speed, and PBL height.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.704-706
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• 2022

In this study, we analyzed the correlation between the amount of photovoltaic power generation and the factors of meteorological changes. A total of 52,561 data were used in the correlation analysis from January 2018 to January 2020, and the variables used in the correlation analysis were time, horizontal plane scattering solar radiation, direct solar radiation, wind velocity, and relative humidity. The temperature was used. Based on this data, we used the Google Colab platform to analyze the correlation, and the analysis revealed whether there was a correlation between solar power and meteorological change factors.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.63 no.5
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• pp.676-682
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• 2014

In this paper, precipitation echo(PRE) and non-precipitaion echo(N-PRE)(including ground echo and clear echo) through weather radar data are identified with the aid of neuro-fuzzy algorithm. The accuracy of the radar information is lowered because meteorological radar data is mixed with the PRE and N-PRE. So this problem is resolved by using RBFNN and judgement module. Structure expression of weather radar data are analyzed in order to classify PRE and N-PRE. Input variables such as Standard deviation of reflectivity(SDZ), Vertical gradient of reflectivity(VGZ), Spin change(SPN), Frequency(FR), cumulation reflectivity during 1 hour(1hDZ), and cumulation reflectivity during 2 hour(2hDZ) are made by using weather radar data and then each characteristic of input variable is analyzed. Input data is built up from the selected input variables among these input variables, which have a critical effect on the classification between PRE and N-PRE. Echo judgment module is developed to do echo classification between PRE and N-PRE by using testing dataset. Polynomial-based radial basis function neural networks(RBFNNs) are used as neuro-fuzzy algorithm, and the proposed neuro-fuzzy echo pattern classifier is designed by combining RBFNN with echo judgement module. Finally, the results of the proposed classifier are compared with both CZ and DZ, as well as QC data, and analyzed from the view point of output performance.

• Atmosphere
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• v.27 no.2
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• pp.235-249
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• 2017

This paper describes methodology verifying near-surface predictability of numerical weather prediction models against the surface synoptic weather station network (SYNOP) observation. As verification variables, temperature, wind, humidity-related variables, total cloud cover, and surface pressure are included in this tool. Quality controlled SYNOP observation through the pre-processing for data assimilation is used. To consider the difference of topographic height between observation and model grid points, vertical inter/extrapolation is applied for temperature, humidity, and surface pressure verification. This verification algorithm is applied for verifying medium-range forecasts by a global forecasting model developed by Korea Institute of Atmospheric Prediction Systems to measure the near-surface predictability of the model and to evaluate the capability of the developed verification tool. It is found that the verification of near-surface prediction against SYNOP observation shows consistency with verification of upper atmosphere against global radiosonde observation, suggesting reliability of those data and demonstrating importance of verification against in-situ measurement as well. Although verifying modeled total cloud cover with observation might have limitation due to the different definition between the model and observation, it is also capable to diagnose the relative bias of model predictability such as a regional reliability and diurnal evolution of the bias.

• Journal of Ecology and Environment
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• v.31 no.1
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• pp.83-87
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• 2008

The small-scale dynamic of moth populations in spring was examined in a coniferous forest of southwestern Korea. Moths were collected with one 22-watt light trap for 29 days in April 2007. A total of 450 individuals of 38 species in 5 families were collected. The most abundant species was an epiplemid moth, Epiplema plagifera. The relationship between these dominant moths and their host plants is briefly discussed. We also examined influence of weather factors on the number of species and individuals collected. Multiple regression analyses showed that the two-day temperature difference explained 18% of the variance in the number of species collected, while air and ground temperatures explained 51% of the variance in the log-transformed number of individuals collected. This suggests that temperature affects local population sizes in spring, but variables other than weather may also affect the diversity of local moth populations.

• Proceedings of the KIEE Conference
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• 2004.11b
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• pp.187-189
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• 2004

This paper investigates the effect of weather factors(such as winds, rain, snows, temperature, clouds and humidity) on transmission line outages. The result shows that weather variables have significant effects on the transmission line historical outages and the relationship between them is nonlinear. Multiple regression analysis using Logit model is proved to be appropriate in forecasting line failure rate in KEPCO systems. It could also provide system operators with useful informations about system operation and planing.