Cross-correlation modeling of European windstorms: A cokriging approach for optimizing surface wind estimates

Joyner, T. Andrew; Friedland, Carol J.; Rohli, Robert V.; Treviño, Anna M.; Massarra, Carol; Paulus, Gernot

doi:10.1016/j.spasta.2015.05.003

Cited by 17 publications

(8 citation statements)

References 26 publications

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“…A recent application of this method to housing prices can be found in Kuntz and Helbich [31], who consider structural and neighborhood characteristics as auxiliary variables. D'Agostino et al [32], Fassò and Finazzi [33], and Joyner et al [34] used the cokriging method to perform spatio-temporal studies in the fields of geology, the environment, and climatology, respectively, and to obtain isovalue maps of the variable of interest. Cokriging has also been used to estimate the temporal change in spatially-correlated variables between only two time instants [35] and for data irregularly distributed in space and time [36].…”

Section: Introductionmentioning

confidence: 99%

Estimation of Housing Price Variations Using Spatio-Temporal Data

2019

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This paper proposes a hedonic regression model to estimate housing prices and the spatial variability of prices over multiple years. Using the model, maps are obtained that represent areas of the city where there have been positive or negative changes in housing prices. The regression-cokriging (RCK) method is used to predict housing prices. The results are compared to the cokriging with external drift (CKED) model, also known as universal cokriging (UCK). To apply the model, heterotopic data of homes for sale at different moments in time are used. The procedure is applied to predict the spatial variability of housing prices in multi-years and to obtain isovalue maps of these variations for the city of Granada, Spain. The research is useful for the fields of urban studies, economics, real estate, real estate valuations, urban planning, and for scholars.

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Section: Introductionmentioning

confidence: 99%

Estimation of Housing Price Variations Using Spatio-Temporal Data

2019

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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: 98%

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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“…A partir dos estudos mais recentes, tem-se que os métodos geoestatísticos, incluindo a krigagem comum, apresentaram ótimos desempenhos quando comparados com outros métodos, sendo utilizados até para cálculos das velocidades de rajada com suficiente precisão (Joyner et al, 2015).…”

Section: Introductionunclassified

Potencial de geração de energia eólica no Submédio São Francisco

Lopes

Júnior

Melo

et al. 2017

J. Env. Anal. Progr.

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A B S T R A C TThe major potential of wind power generation in the world and a possibility of electric energy production make it an alternative source to increase the diversity in the world energy matrix. The objective was to characterize a predominant velocity and direction of the wind for generation of wind power in the Submédio São ) of the São Francisco Submiddle. The highest values of wind power are highlighted for the municipalities of Dormentes, Lagoa Grande, Curaçá, Petrolina, Afrânio, Pedro Alexandre, part of the municipality of Santa Filomena, Santa Maria da Boa Vista, Chorrochó, Macururé, Paulo Afonso, and Juazeiro. The municipality of Curaçá, in the State of Bahia, show the most favorable installation of wind generators, instead of larger values and constant wind transmission. Consequently, it has the highest values of wind energy and a dominant frequency in the southeast direction of the winds throughout the day, being able to fix the towers in this geographical orientation. A large part of the Submédio São Francisco is suitable for the installation of wind energy generation centers.

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Cross-correlation modeling of European windstorms: A cokriging approach for optimizing surface wind estimates

Cited by 17 publications

References 26 publications

Estimation of Housing Price Variations Using Spatio-Temporal Data

Estimation of Housing Price Variations Using Spatio-Temporal Data

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

Potencial de geração de energia eólica no Submédio São Francisco

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