• Journal of Environmental Science International
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• v.19 no.8
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• pp.983-999
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• 2010

Wind power energy is one of the favorable and fast growing renewable energies. It is most important for exact analysis of wind to evaluate and forecast the wind power energy. The purpose of this study is to improve the performance of numerical atmospheric model by data assimilation over a complex coastal area. The benefit of the profiler is its high temporal resolution and dense observation data at the lower troposphere. Three wind profiler sites used in this study are inhomogeneously situated near south-western coastal area of Korean Peninsula. The method of the data assimilation for using the profiler to the model simulation is the three-dimensional variational data assimilation (3DVAR). The experiment of two cases, with/without assimilation, were conducted for how to effect on model results with wind profiler data. It was found that the assimilated case shows the more reasonable results than the other case compared with vertical observation and surface Automatic Weather Station(AWS) data. Although the effect of sonde data was better than profiler at a higher altitude, the profiler data improves the model performance at lower atmosphere. Comparison with the results of 4 June and 5 June suggests that the efficiency with hourly assimilated profiler data is strongly influenced by synoptic conditions. The reduction rate of Normalized Mean Error(NME), mean bias normalized by averaged wind speed of observation, on 4 June was 28% which was larger than 13% of 5 June. In order to examine the difference in wind power energy, the wind power density(WPD) was calculated and compared.

• Wind and Structures
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• v.16 no.4
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• pp.373-391
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• 2013

This is the first of two companion papers that analyse ten years of on-site monitoring data for the Confederation Bridge to determine the validity of the original wind speeds and wind loads predicted in 1994 when the bridge was being designed. The check of the original design values is warranted because the design wind speed at the middle of Northumberland Strait was derived from data collected at shore-based weather stations, and the design wind loads were based on tests of section and full-aeroelastic models in the wind tunnel. This first paper uses wind, tilt, and acceleration monitoring data to determine the static and dynamic responses of the bridge, which are then used in the second paper to derive the static and dynamic wind loads. It is shown that the design ten-minute mean wind speed with a 100-year return period is 1.5% less than the 1994 design value, and that the bridge has been subjected to this design event once on November 7, 2001. The dynamic characteristics of the instrumented spans of the bridge including frequencies, mode shapes and damping are in good agreement with published values reported by others. The on-site monitoring data show bridge response to be that of turbulent buffeting which is consistent with the response predicted at the design stage.

• Journal of Electrical Engineering and Technology
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• v.12 no.6
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• pp.2177-2186
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• 2017

The goal of this paper is to provide the specific forecasting steps and to explain how to design the forecasting architecture and training data sets to forecast very short-term wind power when the numerical weather prediction (NWP) is unavailable, and when the sampling periods of the wind power and training data are different. We forecast the very short-term wind power every 15 minutes starting two hours after receiving the most recent measurements up to 40 hours for a total of 38 hours, without using the NWP data but using the historical weather data. Generally, the NWP works as a predictor and can be converted to wind power forecasts through machine learning-based forecasting algorithms. Without the NWP, we can still build the predictor by shifting the historical weather data and apply the machine learning-based algorithms to the shifted weather data. In this process, the sampling intervals of the weather and wind power data are unified. To verify our approaches, we participated in the 2017 wind power forecasting competition held by the European Energy Market conference and ranked sixth. We have shown that the wind power can be accurately forecasted through the data shifting although the NWP is unavailable.

• Wind and Structures
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• v.9 no.6
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• pp.473-492
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• 2006

This paper presents numerical results of pedestrian-level wind environment around the base of a row of tall buildings by CFD. Four configurations of building arrangement are computed including a single square tall building. Computed results of pedestrian-level wind flow patterns and wind speeds are compared to previous wind tunnel measurement data to enable an assessment of CFD predictions. The CFD model uses the finite-volume method with RNG $k-{\varepsilon}$ model for turbulence closure. It is found that the numerical results can reproduce key features of pedestrian-level wind environment such as corner streams around corners of upwind building, sheltered zones behind buildings and channeled high-speed flow through a building gap. However, there are some differences between CFD results and wind tunnel data in the wind speed distribution and locations of highest wind speeds inside the corner streams. In locations of high ground-level wind speeds, CFD values match wind tunnel data within ${\pm}10%$.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.27 no.8
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• pp.60-66
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• 2013

The wind-speeds among geographically close wind-farms have high correlations seasonally. This paper presents a novel wind-speed sampling method which sincerely reflects the correlation among wind-speeds of different wind-farms. In the proposed method, the wind-speed samples are generated through the statistical data analysis of the measured past wind-speed data and are adequate to be applied to generation adequacy assessment based on random sampling. In the proposed method, the specific probability distribution need not to be assumed and sufficiently accurate wind-speed samples can be generated based only on the measured past data. The proposed method is applied to the two wind-farm problem to show its applicability.

• Journal of Industrial Technology
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• v.29 no.B
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• pp.173-180
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• 2009

Simulations using two well-known commercial codes, WAsP and WindSIM, were performed to predict the wind resources in complex terrains of Korea. The predictions from the codes were compared with the measured data. Cross predictions were performed for two closely located measurement sites. The results from WindSIM were found to be more accurate than those from WAsP. The predictions for wind velocity and direction in five different sites of complex terrain from WAsP and WindSIM were also compared. It was found that if the self prediction of the wind velocity and direction from WAsP is close to the measured wind data, the discrepancies between WAsP results and WindSIM results are also close.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.11 no.1
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• pp.33-38
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• 2008

To investigate the building wind characteristics of skyscraper nearby areas, two points were selected and the wind speed and the wind direction data were measured using 2-D ultrasonic anemometer and propeller type wind monitor during the winter time. The study site is Dohgok-dong, Seoul. After measurement, wind data whose speed is equal to or more than Beaufort level five were selected, classified and analyzed in terms of direction, velocity level and hourly difference. The prevailing wind point is higher than street canyon in terms of intensity and frequency. The main direction is also different. This study aims to figure out the phenomena of building wind impact and also to provide essential basic data for establishing proper guidelines in building wind impact assessment for skyscrapers in Korea.

• Wind and Structures
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• v.19 no.5
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• pp.467-480
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• 2014

The estimation of wind speed values used in codes and standards is an integral part of the wind load evaluation process. In a number of codes and standards, wind speeds outside of tropical cyclone prone regions are estimated using a single probability distribution developed from observed wind speed data, with no distinction made between the types of causal wind hazard (e.g., thunderstorm). Non-tropical cyclone wind hazards (i.e., thunderstorm, non-thunderstorm) have been shown to possess different probability distributions and estimation of non-tropical cyclone wind speeds based on a single probability distribution has been shown to underestimate wind speeds. Current treatment of non-tropical cyclone wind hazards in worldwide codes and standards is touched upon in this work. Meteorological data is available at a considerable number of United States (U.S.) stations that have information on wind speed as well as the type of causal wind hazard. In this paper, probability distributions are fit to distinct storm types (i.e., thunderstorm and non-thunderstorm) and the results of these distributions are compared to fitting a single probability distribution to all data regardless of storm type (i.e., co-mingled). Distributions fitted to data separated by storm type and co-mingled data will also be compared to a derived (i.e., "mixed") probability distribution considering multiple storm types independently. This paper will analyze two extreme value distributions (e.g., Gumbel, generalized Pareto). It is shown that mixed probability distribution, on average, is a more conservative measure for extreme wind speed estimation. Using a mixed distribution is especially conservative in situations where a given wind speed value for either storm type has a similar probability of occurrence, and/or when a less frequent storm type produces the highest overall wind speeds. U.S. areas prone to multiple non-tropical cyclone wind hazards are identified.

• Journal of Power System Engineering
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• v.21 no.6
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• pp.31-39
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• 2017

In order to examine if met-masts wind data can exchange each other for wind resource assessment, an investigation was carried out in Kimnyeong and Haengwon regions of Jeju Island. The two regions are both simple terrain and 4.31 km away from each other. The one-year wind speed data measured by 70 m-high anemometers of each met-mast of the two regions were analysed in detail. Measure-Correlate-Predict (MCP) method was applied to the two regions using the 10-year Automatic Weather System (AWS) wind data of Gujwa region for creating 10-year Wind Statistics by running WindPRO software. The two 10-year Wind Statistics were applied to the self-met mast point for self prediction of Annual Energy Production (AEP) and Capacity Factor (CF) and the each other's met mast point for mutual prediction of them. As a result, when self-prediction values were reference, relative errors of mutual prediction values were less than 1% for AEP and CF so that met masts wind data under the same condition of this study could exchange each other for estimating accurate wind resource.

• Journal of the Korean Solar Energy Society
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• v.32 no.3
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• pp.146-152
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• 2012

To install a wind power generator, the survey on the wind environment resources must be conducted in advance. The survey on the wind environment resources is to collect and analyze data regarding the wind speed and direction on a data logger. The data logger consists of a sensor, signal processing circuit and storage device. According to the analysis of the stored data, the amount of power generation by the types of generators can be predicted and the most optimal generator including safety grade can be selected, and in case of installing a generator in the future, it can be utilized as basic data regarding supporting base and foundation construction method of survey points. Data logger was developed for a small wind power generator that is suitable for the international standard(IEC 61400) by using DSP-F28335 micro controller in this paper. It was developed to measure the wind speed of 1 [m/s]~17 [m/s], the wind direction of 0 [$^{\circ}$]~359 [$^{\circ}$], and temperature of -30 [$^{\circ}C$]~50 [$^{\circ}C$], and the comparative experiment with other companies' data loggers was conducted, and an error was measured to be less than ${\pm}0.1$ [m/s] for wind speed and less than +1 [$^{\circ}$] for wind direction.