• Wind and Structures
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• v.22 no.2
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• pp.235-252
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• 2016

A wind velocity power spectrum (WVPS) with high fidelity is extremely important for accurate prediction of structural buffeting response. WVPS heavily depends on the geographical locations, local terrains and topographies. Hence, field measurement of wind characteristics may be the unique way to obtain the accurate WVPS for a specific region. In this paper, a systematic analysis and discussions of existing WVPSs were performed. Six recorded strong wind data from the structural health monitoring systems (SHMS) of Runyang Suspension Bridge (RSB) and Sutong Cable-stayed Bridge (SCB) in Jiangsu Province of China were selected for analysis. The measured and pre-processed wind velocity data was first transformed from time domain to frequency domain to obtain the measured spectrum. The spectrum for each strong wind was then fitted using the nonlinear least square method and compared with both the fitted spectrum from statistical analysis and the recommended spectrum in specifications. The modified Kaimal spectrum was proved to be the "best" choice for the coastal area of East Jiangsu Province. Finally, a suitable WVPS formula fit for the coastal area of East Jiangsu Province was presented based on the modified Kaimal spectrum. Results in this study provide a more accurate and reliable WVPS for wind-resistant design of engineering structures in the coastal area of East Jiangsu Province.

• The Korean Journal of Applied Statistics
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• v.23 no.2
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• pp.345-356
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• 2010

In this paper, we introduce the linear regression model taking the parametric spatial association structure into account and employ it to five-year averaged wind speed data measured at 460 meteorological monitoring stations in South Korea. From the prediction map obtained by the model with spatial association parameters, we can see that inland area has smaller wind speed than coastal regions. When comparing the spatial linear regression model with classical one by using one-leave-out cross-validation, the former outperforms the latter in terms of similarity between the observations and the corresponding predictions and coverage rate of 95% prediction intervals.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2009.06a
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• pp.449-451
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• 2009

Sea level rise and increase of the typhoon/hurricane intensity due to global warming have threaten coastal areas for residential and industrial and have been widely studied. In this study we showed our recent efforts on sea wind which is one of critical factors for safe maritime traffic and prediction for storm surges and waves. Currently, most of numerical weather models in korea do not have sufficient spatial and temporal resolutions, therefore we set up a find grid(about 9km) sea wind prediction system that predicts every 12 hours for three day using Weather Research and Forecasting(WRF). This system covers adjacent seas around korean peninsula Comparisons of two observed data, Ieodo Ocean Research station(IORS) and Yellow Sea Buoy(YSB), showed reasonable agreements and by data assimilation we will improve better accurate sea winds in near future.

• Plant Journal
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• v.17 no.4
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• pp.41-52
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• 2021

In this study, the PV output and module temperature values were predicted using the Meteorological Agency data and compared with actual data, weather, solar radiation, ambient temperature, and wind speed. The forecast accuracy by weather was the lowest in the data on a clear day, which had the most data of the day when it was snowing or the sun was hit at dawn. The predicted accuracy of the module temperature and the amount of power generation according to the amount of insolation decreased as the amount of insolation increased, and the predicted accuracy according to the ambient temperature decreased as the module temperature increased as the ambient temperature increased and the amount of power generated lowered the ambient temperature. As for wind speed, the predicted accuracy decreased as the wind speed increased for both module temperature and power generation, but it was difficult to define the correlation because wind speed was insignificant than the influence of other weather conditions.

• Journal of the Society of Disaster Information
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• v.16 no.2
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• pp.223-236
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• 2020

Purpose: In this study, a strong wind damage prediction function was developed in order to be used as a contingency during disaster management (preventive-preventive-response-recovery). Method: The predicted strong wind damage function proposed in this study took into account the re-enactment boy power, weather data and local characteristics at the time of damage. The meteorological data utilized the wind speed, temperature, and damage history observed by the Korea Meteorological Administration, the disaster year, and the recovery costs, population, vinyl house area, and farm water contained in the disaster report as factors to reflect the regional characteristics. Result: The function developed in this study reflected the predicted weather factors and local characteristics based on the history of strong wind damage in the past, and the extent of damage can be predicted in a short time. Conclusion: Strong wind damage prediction functions developed in this study are believed to be available for effective disaster management, such as decision making by policy-makers, deployment of emergency personnel and disaster prevention resources.

• Journal of The Korean Astronomical Society
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• v.37 no.4
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• pp.151-157
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• 2004

It is investigated quantitative relations between the magnetic storm magnitude and the solar wind parameters such as the Interplanetary Magnetic Field (hereinafter, IMF) magnitude (B), the southward component of IMF (Bz), and the dynamic pressure during the main phase of the magnetic storm with focus on the role of the interplanetary shock (hereinafter, IPS) in order to build the space weather fore-casting model in the future capable to predict the occurrence of the magnetic storm and its magnitude quantitatively. Total 113 moderate and intense magnetic storms and 189 forward IPSs are selected for four years from 1998 to 2001. The results agree with the general consensus that solar wind parameter, especially, Bz component in the shocked gas region plays the most important role in generating storms (Tsurutani and Gonzales, 1997). However, we found that the correlations between the solar wind parameters and the magnetic storm magnitude are higher in case the storm happens after the IPS passing than in case the storm occurs without any IPS influence. The correlation coefficients of B and $BZ_(min)$ are specially over 0.8 while the magnetic storms are driven by IPSs. Even though recently a Dst prediction model based on the real time solar wind data (Temerin and Li, 2002) is made, our correlation test results would be supplementary in estimating the prediction error of such kind of model and in improving the model by using the different fitting parameters in cases associated with IPS or not associated with IPS rather than single fitting parameter in the current model.

• Proceedings of the Korea Air Pollution Research Association Conference
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• 2008.10a
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• pp.177-178
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• 2008

• Journal of Power Electronics
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• v.15 no.4
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• pp.1047-1053
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• 2015

A time-domain simulation tool to predict the dynamic power output of wind turbines in an offshore wind farm was developed in this study. A wind turbine model consisting of first or second order transfer functions of various wind turbine elements was combined with the Ainslie's eddy viscosity wake model to construct the simulation tool. The wind turbine model also includes an aerodynamic model that is a look up table of power and thrust coefficients with respect to the tip speed ratio and pitch angle of the wind turbine obtained by a commercial multi-body dynamics simulation tool. The wake model includes algorithms of superposition of multiple wakes and propagation based on Taylor's frozen turbulence assumption. Torque and pitch control algorithms were implemented in the simulation tool to perform max-Cp and power regulation control of the wind turbines. The simulation tool calculates wind speeds in the two-dimensional domain of the wind farm at the hub height of the wind turbines and yields power outputs from individual wind turbines. The NREL 5MW reference wind turbine was targeted as a wind turbine to obtain parameters for the simulation. To validate the simulation tool, a Danish offshore wind farm with 80 wind turbines was modelled and used to predict the power from the wind farm. A comparison of the prediction with the measured values available in literature showed that the results from the simulation program were fairly close to the measured results in literature except when the wind turbines are congruent with the wind direction.

• Atmosphere
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• v.20 no.3
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• pp.319-332
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• 2010

Sensitivity experiments of WRF model using different planetary boundary layer (PBL) and land surface model (LSM) parameterizations are evaluated for prediction of wind fields within the surface layer. The experiments were performed with three PBL schemes (YSU, Pleim, MYJ) in combination with three land surface models (Noah, RUC, Pleim). The WRF model was conducted on a nested grid from 27-km to 1-km horizontal resolution. The simulations validated wind speed and direction at 10 m and 80 m above ground level at a 1-km spatial resolution over the South Korea. Statistical verification results indicate that Pleim and YSU PBL schemes are in good agreement with observations at 10 m above ground level, while the MYJ scheme produced predictions similar to the observed wind speed at 80 m above ground level. LSM comparisons indicate that the RUC model performs best in predicting 10-m and 80-m wind speed. It is found that MYJ (PBL) - RUC (LSM) simulations yielded the best results for wind field in the surface layer. The choice of PBL and LSM parameterization will contribute to more accurate wind predictions for air quality studies and wind power using WRF.

• Smart Structures and Systems
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• v.24 no.6
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• pp.733-744
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• 2019

Wind speed forecasting is critical for a variety of engineering tasks, such as wind energy harvesting, scheduling of a wind power system, and dynamic control of structures (e.g., wind turbine, bridge, and building). Wind speed, which has characteristics of random, nonlinear and uncertainty, is difficult to forecast. Nowadays, machine learning approaches (generalized regression neural network (GRNN), back propagation neural network (BPNN), and extreme learning machine (ELM)) are widely used for wind speed forecasting. In this study, two schemes are proposed to improve the forecasting performance of machine learning approaches. One is that optimization algorithms, i.e., cross validation (CV), genetic algorithm (GA), and particle swarm optimization (PSO), are used to automatically find the optimal model parameters. The other is that the combination of different machine learning methods is proposed by finite mixture (FM) method. Specifically, CV-GRNN, GA-BPNN, PSO-ELM belong to optimization algorithm-assisted machine learning approaches, and FM is a hybrid machine learning approach consisting of GRNN, BPNN, and ELM. The effectiveness of these machine learning methods in wind speed forecasting are fully investigated by one-year field monitoring data, and their performance is comprehensively compared.