Spatial estimation of wind speed: a new integrative model using inverse distance weighting and power law

Özelkan, Emre; Chen, Gang; Ustundag, Burak Berk

doi:10.1080/17538947.2015.1127437

Cited by 19 publications

(8 citation statements)

References 32 publications

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“…The wind hazard map was developed using the Geographic Information Systems (GIS), which has the capability to interpolate the wind speed data via various statistical methods. The study also employed spatial interpolation methods for the prediction of V s at the investigated locations, and the spatial interpolation was generated using the Inverse Distance Weighting (IDW) techniques as used by previous studies [17][18][19]. The IDW method calculates the assigned values to the unknown points with the weighted average of the values available at the known points.…”

Section: Basic Wind Speed Mapmentioning

confidence: 99%

Development of Site-Specific Wind Hazard Map for Peninsular Malaysia via Spatial Modeling

et al. 2020

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The commonly used approach to predict and evaluate the wind risk in Peninsular Malaysia is to employ the basic wind speed (Vs) hazard map, which underestimates the real damage due to the limitation of factors considered by the current map. This paper aimed to develop a new wind hazard map for Peninsular Malaysia based on the site wind speed (Vsite) rather than the Vs using the Geographical Information System. The development of the Vsite map considered the effects of the Land Use Land Cover (LULC) and the topography conditions that were not taken into consideration by the Malaysian Standard during the development of the Vs map. The statistical analysis proved that the wind hazard in Peninsular Malaysia is directly proportional to the LULC and inversely proportional to the Hill Shape Multiplier. In addition, the results showed that the existing Vs map underrated the wind hazard in Peninsular Malaysia by almost 9.02% to 17.79% compared to the developed Vsite map. Therefore, the use of the newly developed map to evaluate the wind hazards will significantly enhance the assessment, and the new map has the potential to be incorporated into the Malaysian Standard for this purpose.

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Section: Basic Wind Speed Mapmentioning

confidence: 99%

Development of Site-Specific Wind Hazard Map for Peninsular Malaysia via Spatial Modeling

et al. 2020

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“…spatial interpolation) of wind, however, have only been performed in a limited number of studies (e.g. Palomino and Martín, ; Cellura et al ., ; Luo et al ., ; Hernandez‐Escobedo et al ., ; Joyner et al ., ; Ozelkan et al ., ). Cellura et al .…”

Section: Introductionmentioning

confidence: 97%

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

Kim

2017

Meteorological Applications

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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.

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“…However, erratic production of wind energy still remains as one of the main issues due to the variability of wind speed and direction over time (Fadare 2010;Jung and Tam 2013;Ozelkan, Chen, and Ustundag 2016). Hence, accurate estimation of wind energy potential is also directly involved with the wind patterns which explain the variability of wind behaviour.…”

Section: Introductionmentioning

confidence: 99%

Mapping frequent spatio-temporal wind profile patterns using multi-dimensional sequential pattern mining

Yusof

Zurita-Milla

2016

International Journal of Digital Earth

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Holistic understanding of wind behaviour over space, time and height is essential for harvesting wind energy application. This study presents a novel approach for mapping frequent wind profile patterns using multidimensional sequential pattern mining (MDSPM). This study is illustrated with a time series of 24 years of European Centre for Medium-Range Weather Forecasts European Reanalysis-Interim gridded (0.125°× 0.125°) wind data for the Netherlands every 6 h and at six height levels. The wind data were first transformed into two spatio-temporal sequence databases (for speed and direction, respectively). Then, the Linear time Closed Itemset Miner Sequence algorithm was used to extract the multidimensional sequential patterns, which were then visualized using a 3D wind rose, a circular histogram and a geographical map. These patterns were further analysed to determine their wind shear coefficients and turbulence intensities as well as their spatial overlap with current areas with wind turbines. Our analysis identified four frequent wind profile patterns. One of them highly suitable to harvest wind energy at a height of 128 m and 68.97% of the geographical area covered by this pattern already contains wind turbines. This study shows that the proposed approach is capable of efficiently extracting meaningful patterns from complex spatio-temporal datasets.ARTICLE HISTORY

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Spatial estimation of wind speed: a new integrative model using inverse distance weighting and power law

Cited by 19 publications

References 32 publications

Development of Site-Specific Wind Hazard Map for Peninsular Malaysia via Spatial Modeling

Development of Site-Specific Wind Hazard Map for Peninsular Malaysia via Spatial Modeling

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

Mapping frequent spatio-temporal wind profile patterns using multi-dimensional sequential pattern mining

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