Estimating potential wind energy from sparsely located stations in a mountainous coastal region

Abstract

For the spatial planning of a potential wind power plant (WPP) site, the efficient assessment of the wind power potential is important. However, observations are lacking for most potential sites. Here, a statistical three-step approach is presented to estimate the spatial distribution of potential wind power in a region in which observational stations are sparsely located. First, multiple linear regression is used to fill the gaps in the wind data collected at the stations. Second, spatial interpolation with hybrid Kriging is used to construct the grid-based data based on the station-based, gap-filled data. Third, the wind power potential is estimated according to the theoretical relationship between wind speed and power. As a case study, the spatial distribution of wind power potential is estimated over Jeju Island, where WPPs currently exist. Initially, the selected regression model in this case study showed correlation co-efficients ranging from 0.48 to 0.82. In a second step, the spatially interpolated data with the gap-filled data reasonably captured the expected spatial distribution of wind speed than those with the observed data. Finally, the results showed that the coastal region might be the best region for WPPs rather than the high elevation region far from the coast and that all the existing WPPs are located in regions with high potential wind power. The proposed statistical approach is simple and efficient for preliminary spatial planning. Furthermore, it can be applied to other regions with sparsely distributed wind speed data, including mountainous coastal regions.

Potencial de geração de energia eólica no Submédio São Francisco

“…Em comparação com estudo de energia eólica potencial na Corea do Sul, onde há presença de parques eólicos, Um & Kim (2017) Os picos de velocidades observados são ideais para a utilização de pequenos sistemas isolados, ou seja, para a geração de energia para residência ou para fazendas com sistemas mecânicos para bombeamento de água (Rossi & Oliveira, 2015).…”

Spatial and Temporal Variations in Reference Crop Evapotranspiration in a Mountainous Island, Jeju, in South Korea

“…The different estimates based on EXT and OBS (Figures 4b and 5b) show the effects of gap filling before spatial interpolation via hybrid Kriging in this study. Previous studies [16][17][18] show that the relatively large difference between EXT and OBS in the highlands corresponds to the fact that most of the observational stations are located in the lowlands, and maps based on OBS data cannot reasonably capture the spatial variability in the meteorological variables.…”

“…The observed and extended/gap-filled data are denoted as OBS and EXT, respectively, hereafter. Previous studies that included temperature, wind speed and relative humidity [16][17][18] suggest that gap filling is appropriate for analyzing the spatial and temporal patterns for corresponding meteorological data. However, note that the data used in this study are not identical to those in the authors' previous study in terms of data length.…”

“…However, note that the data used in this study are not identical to those in the authors' previous study in terms of data length. Note that the monthly data for relative humidity and wind speed are used for efficiently developing the regression model as depicted in [17,18], respectively.…”