Surface Wind Regionalization Based on Similarity of Time-series Wind Vectors

Kim, Jinsol; Kim, Hyun‐Goo; Park, Hyeong‐Dong

doi:10.5572/ajae.2016.10.2.080

Cited by 8 publications

(6 citation statements)

References 16 publications

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“…In fact, the wind turbine spacing at wind farms in mountainous Gangwon is much smaller than those at wind farms on the plains of Europe because the ridges mainly run from north to south while the main wind direction is westerly (Figure 9). In other words, when the wind turbines are laid out along ridges from north to south, they form a line running perpendicularly to the main wind direction, so wake loss can largely be avoided even when the wind turbine spacing is set to the minimum under such an arrangement (Kim et al, 2016).…”

Section: Resultsmentioning

confidence: 99%

Evaluation of wind resource potential in mountainous region considering morphometric terrain characteristics

2017

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In South Korea, where 64% of the national territory is mountainous, good wind resources in inland areas are mostly situated in high mountain regions. To develop a wind farm in a mountain region, the sloping of mountains and their shielding from wind by the surrounding topology should be considered. It is generally most advantageous to install wind turbines along a ridge that is open in all directions. This article presents a methodology for evaluating suitable sites for wind farm development by identifying suitable ridges using morphometric analysis, while excluding the geographical and social environmental exclusion factors and superposing them on the wind resource map to find the area having a specific level or higher wind power density. The result of the proposed suitable site analysis and existing wind farms was assessed to verify the feasibility of the method of analyzing suitable sites on ridges. The wind resource potential in Gangwon Province when calculated using a method based on conventionally suitable site and the method based on ridge analysis was 9 and 5 GW, respectively. The result confirmed that the conventional area-based potential calculation without consideration of the morphometric terrain characteristics overestimated wind resource potential by around 80% compared to the ridge analysis method of calculation presented in this article.

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Section: Resultsmentioning

confidence: 99%

Evaluation of wind resource potential in mountainous region considering morphometric terrain characteristics

2017

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“…According to the wind systems in the Korean Peninsula [14], a synoptic wind system is formed where WNW winds blow to the Seoul region along Han River and northwesterly winds approximate from the west sea.…”

Section: Discussionmentioning

confidence: 99%

Wind Resource Assessment for High-Rise BIWT Using RS-NWP-CFD

Kim

Jeon²,

Kim³

2016

Remote Sensing

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Abstract:In this paper, a new wind resource assessment procedure for building-integrated wind turbines (BIWTs) is proposed. The objective is to integrate wind turbines at a 555 m high-rise building to be constructed at the center of Seoul, Korea. Wind resource assessment at a high altitude was performed using ground-based remote sensing (RS); numerical weather prediction (NWP) modeling that includes an urban canopy model was evaluated using the remote sensing measurements. Given the high correlation between the model and the measurements, we use the model to produce a long-term wind climate by correlating the model results with the measurements for the short period of the campaign. The wind flow over the high-rise building was simulated using computational fluid dynamics (CFD). The wind resource in Seoul-one of the metropolitan cities located inland and populated by a large number of skyscrapers-was very poor, which results in a wind turbine capacity factor of only 7%. A new standard procedure combining RS, NWP, and CFD is proposed for feasibility studies on high-rise BIWTs in the future.

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“…The study area has a complex wind resource distribution, which is appropriate for statistical analysis. 26 Figure 2A shows the location and geography of the study area.…”

Section: High-resolution Wind Resource Datamentioning

confidence: 99%

Machine learning‐based statistical downscaling of wind resource maps using multi‐resolution topographical data

Lee

Kim

et al. 2022

Wind Energy

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The need for high-resolution wind resource maps is increasing with the increase in the supply and development of wind power. Many physical downscaling models have been developed and applied to make these maps. However, as the existing models require extensive computations and time, statistical models with higher efficiency are being studied. Statistical models such as regression and machine learning models can quickly calculate wind resource maps, but they have a problem of low accuracy. This study proposes a machine learning model with new topography-derived variables to interpret the physical characteristics of the wind. As the shape of topography, which was unable to be interpreted in previous studies, can be considered with new derived variables, a significant performance improvement was identified. The analysis was conducted using 1 km Weather Research and Forecasting (WRF) results and ERA5 reanalysis data from South Korea. Two Weibull distribution parameter maps were calculated and used as input and output data. Three collections of derived variables were devised and compared. Therefore, the multi-resolution topography data showed the highest improvements with approximately 15% reduction in root mean square error (RMSE) for both the linear regression and machine learning models. In particular, the land area showed a decrease of 20%. The best proposed models showed an RMSE of 7% and 8% for two Weibull parameters. The results are expected to serve as a reference for continuing research and utilization of statistical models.

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Surface Wind Regionalization Based on Similarity of Time-series Wind Vectors

Cited by 8 publications

References 16 publications

Evaluation of wind resource potential in mountainous region considering morphometric terrain characteristics

Evaluation of wind resource potential in mountainous region considering morphometric terrain characteristics

Wind Resource Assessment for High-Rise BIWT Using RS-NWP-CFD

Machine learning‐based statistical downscaling of wind resource maps using multi‐resolution topographical data

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