**19. A case study**

**18. Comparison of different methods**

**Surfaces**

**Model Type Output**

228 New Developments in Renewable Energy

LP Deterministic Prediction Fairly

RBF Deterministic Prediction Fairly

Stochastic Prediction.

Standard Error, Probability, Quintile

Standard Error, Probability, Quintile

**Table 3.** Summarized properties of the interpolators

Kriging Stochastic Prediction.

Cokrigin g

\*by compus.esri.com

will lead to large amount of uncertainty in output results.

**Speed Exact**

IDW Deterministic Prediction Fast Yes Little flexibility, few

GP Deterministic Prediction Fast No Little flexibility, few

fast

fast

Fairly fast

Fairly fast

Yes without measurement error, No with measurement error

Yes without measurement error, No with measurement error

**Interpolation**

In above sections, all methods are separately presented but their comparison is very important to choose the most appropriate method for analysis. However, a wrong selection in this step

parameter decision

parameter decision

more parameter decision

parameter decision

Very flexibility, assess spatial autocorrelation, obtain standard errors, many decisions more parameter decision

Very flexibility, assess spatial autocorrelation, obtain standard errors, very many decisions

No Some flexibility,

Yes Flexibility, more

**Flexibility Advantage Disadvantages Assumptions**

Few decisions NO assessment of

Few decisions NO assessment of

Flexible NO assessment of

Flexible NO assessment of

Flexible with modeling tools; prediction standard errors

Flexible with modeling tools; prediction standard errors prediction errors, bull's-eyes around data location

prediction errors, may be too smooth, edge points have large influence

prediction errors, may be hard to choose a good local neighborhood

prediction errors, may be too automatic

Many decision on transformations trends, models, parameters, and neighborhoods

Many decision on transformations trends, models, parameters, and neighborhoods

None

None

None

None

Stationary, some methods require a normal data distribution

Stationary, some methods require a normal data distribution

To make the above discussions more clear, a case study including all steps needed for assessment of climate change effects on positioning of wind power plant station is briefly presented. Figure 2 shows these steps applied in the assessment.

**Figure 2.** Methodology of wind power positioning under climate change conditions

For this purpose, a region in Southern Khorasan, Iran is chosen. Five synoptic stations are considered as reference stations as displayed in Figure 3.

values corresponding to each return period were derived. Then, geostatistics method was

Wind Speed Regionalization Under Climate Change Conditions

http://dx.doi.org/10.5772/55985

231

As the average wind speed was the single used parameter, only one map was drawn. In the case of many existed maps, final positioning could be possible by weighting, according to importance of the maps. Figure 4 shows precedence of potential locations to install wind power station in the region according to long duration of historical records. It is clear from the Figure that the regions with the highest and lowest potential of wind power plant construction are

To investigate the effect of climate change on positioning process, first the output of HADCM3 model under A2 emission scenario is derived from IPCC website and downscaled by using statistical downscaling techniques. In this study, linear regression method is used and by making wind speed time series of under study stations for near future period (2010-2039), the

As presented in Figure 6, climate change impacts on wind power plant construction in the regions with low priority will be negligible but in high priority ones, the best points are

frequency analysis is performed again and the future map is presented.

applied for local interpolation and final map for the historical period was prepared.

respectively located in the south eastern and north western parts.

**Figure 5.** Final historical wind map

concentrated in the north eastern part.

**Figure 3.** Under study region

Considering high level of sensitivity of the results to different sources of uncertainty in this study, uncertainty analysis was applied with bootstrap method at 95% confidence interval on the results after downscaling steps (Efron, 1993; Khan, 2006; Fakhry, 2012a; Fakhry, 2012b). Figure 3 shows the results of uncertainty analysis of downscaled data.

**Figure 4.** Uncertainty band before and after downscaling

After preparing average wind speed parameter for each station, frequency analysis is per‐ formed and the Weibull distribution is selected using L-moments method. Finally, the quantile values corresponding to each return period were derived. Then, geostatistics method was applied for local interpolation and final map for the historical period was prepared.

As the average wind speed was the single used parameter, only one map was drawn. In the case of many existed maps, final positioning could be possible by weighting, according to importance of the maps. Figure 4 shows precedence of potential locations to install wind power station in the region according to long duration of historical records. It is clear from the Figure that the regions with the highest and lowest potential of wind power plant construction are respectively located in the south eastern and north western parts.

**Figure 5.** Final historical wind map

**Figure 3.** Under study region

230 New Developments in Renewable Energy

Considering high level of sensitivity of the results to different sources of uncertainty in this study, uncertainty analysis was applied with bootstrap method at 95% confidence interval on the results after downscaling steps (Efron, 1993; Khan, 2006; Fakhry, 2012a; Fakhry, 2012b).

After preparing average wind speed parameter for each station, frequency analysis is per‐ formed and the Weibull distribution is selected using L-moments method. Finally, the quantile

Figure 3 shows the results of uncertainty analysis of downscaled data.

**Figure 4.** Uncertainty band before and after downscaling

To investigate the effect of climate change on positioning process, first the output of HADCM3 model under A2 emission scenario is derived from IPCC website and downscaled by using statistical downscaling techniques. In this study, linear regression method is used and by making wind speed time series of under study stations for near future period (2010-2039), the frequency analysis is performed again and the future map is presented.

As presented in Figure 6, climate change impacts on wind power plant construction in the regions with low priority will be negligible but in high priority ones, the best points are concentrated in the north eastern part.

**Author details**

ran University, Ahwaz, Iran

sity of New York, USA

Stockholm, Sweden.

Statistical Science, , 12(3), 162-176.

change, Solar Energy , 57(3), 239-248.

elling with GIS, 1st Ed. Pergamon Press, Oxford, UK.

newable and Sustainable Energy Reviews.13. , 933-955.

2560-2573.

1388-413.

**References**

Masoomeh Fakhry1\*, Mohammad Reza Farzaneh2

saeid@cc.iut.ac.ir; rnazari@citytech.cuny.edu

, Saeid Eslamian3

\*Address all correspondence to: Masoomeh.fakhri@gmail.com; m.farzaneh@modares.ac.ir;

1 Department of Hydraulic Structure, Faculty of Water Science Engineering, Shahid Cham‐

4 Department of Construction Management and Civil Engineering Technology, City Univer‐

[1] Ackermann Th(2005). Wind power in power system, Royal Institute of Technology,

[2] Akday, S. A, Bagiorgas, H. S, & Mihalakakou, G. (2010). Use of Two-component wei‐ bull mixtures in the analysis of wind speed in the eastern, Applied Energy: ,

[3] Aldrich, John ((1997). R. A. Fisher and the making of maximum likelihood 1912-1922.

[4] Asif, M, & Muneer, T. (2007). Energy supply, its demand and security issues for de‐ veloped and emerging economies, Renewable and Sustainable Energy Reviews,, 11,

[5] Bogardi, I, & Matyasovszky, I. (1996). Estimating daily wind speed under climate

[6] Bonham-carter, G. F. (1994). Geographic Information Systems for Geoscientists: Mod‐

[7] Breslow, P. B, & Sailor, D. J. (2002). Vulnerability of wind power resources to climate

[8] Carta, J. A, & Ramirez, P. Vela´zquez, S. ((2009). A review of wind speed probability distributions used in wind energy analysis Case studies in the Canary Islands. Re‐

change in the continental United States, Renewable Energy: , 27, 585-598.

2 Department of Water Engineering, Tarbiat Modares University, Tehran, Iran

3 Department of Water Engineering, Isfahan University of Technology, Isfahan, Iran

and Rouzbeh Nazari4

http://dx.doi.org/10.5772/55985

233

Wind Speed Regionalization Under Climate Change Conditions

**Figure 6.** Final future wind map under climate change conditions
