1. Multi Criteria Evaluation using Weighted
Linear Combination for wind farm site
selection in Dobrogea, Romania
Student: Simona Petrisor
Supervisor: Douglas Brown
Lecturer in GIS and Human Geography
School of Geography, Geology and Environment
Kingston University
~2014~

2. O U T L I N E
 Introduction
 Justification of study
 Aims and objective
 Methodology and
methods
 Results and Discussions
 Conclusions
Multi Criteria Evaluation using Weighted Linear Combination for wind farm site selection in Dobrogea, Romania

3. INTRODUCTION
 Introduction
Renewable energy has
become more popular
throughout the world,
particularly wind energy.
 Advantages :
- Clean energy source
- Sustainable energy
source
- Low environmental
impact
- Wind resource widely
availableMulti Criteria Evaluation using Weighted Linear Combination for wind farm site selection in Dobrogea, Romania

4. INTRODUCTION
 Justification of Study
Wind energy has a fast growth rate in Romania but the essential issue is
the location.
This study evaluates the suitability for wind farm development in the
particular region of Dobrogea.
 Aims
The project aims to develop a
suitability model which will help
predict suitable areas across
Dobrogea.
 Objectives
- Suitability model
- Identify the factors influencing
the suitability
- Identify suitable areas
- Critically evaluate the results

5. METHODOLOGY AND METHODS
 Suitability Model
• WLC is a multi criteria evaluation
Method common to raster data.
• WLC is used because it can
incorporate
multiple criteria that influences the
model.
• WLC works by considering all
criteria-
factors of the analysis, standardized
to a common numeric range, and
then combines them using an
weighting value scale.
COMPARISON OF RESULTS
FINAL SUITABILITY MAPS
ALTERNATIVE SCENARIOS
COMBINING ATRIBUTE MAPS
WEIGHTED OVERLAY TECHNIQUE FUZZY OVERLAY TECHNIQUE
STANDARDISATION OF THE CRITERION MAPS
GENERATING THE STANDARDISED
MAPS
ASSIGIGNING WEIGHTS
WEIGHTED LINEAR COMBINATION
IDENTIFYING THE SET OF CRITERIA GENERATING CRITERION MAPS
 Flow Chart

6. METHODOLOGY AND METHODS
 Identifying the Set of Criteria
o Wind resource :
- for wind turbines efficient functioning,
wind speed in the area must be at
least 5.1 m/s.
- the project uses two datasets: first
one obtained from Romanian
National Meteorological Agency
and second one from MeteoBlue.
o Land use
o Slope
o Urban
o Roads
o SCA and SPA
o Hydrography
 Generating Factor Maps

7. METHODOLOGY AND METHODS
 Wind Speed Distribution Maps
 Maps were created by interpolating the wind speed data for both
datasets.
 For dataset 1, wind speed data was obtained from 15
meteorological station, whereas for Dataset 2 from 25 points across
the region.
 The interpolation technique used was Ordinary Kiriging .
 Ordinary Kriging works by assuming the constant mean unknown,
and creates a surface of the phenomenon by predicting the values
for each point location.

8. Wind Speed at 80 m height
Multi Criteria Evaluation using Weighted Linear Combination for wind farm site selection in Dobrogea, Romania

9.  Standardization of the factors
After the processing step, all criteria were transformed in factor maps,
standardised to a numeric range of 1to 5, where 1 represents the least suitable
suitable zones and 5 the highest suitability ones.
Criteria
Least
Suitable
1
2 3 4
Highly
Suitable
5
Wind Dataset1 4.4-4.9m/s 5-5.4 m/s 5.5-6.1 m/s 6.2-6.7 m/s 6.8-7.5 m/s
Wind Dataset2 3.7-4 m/s 4.1-4.4
m/s
4.5-4.9 m/s 5-5.4 m/s 5.5-6 m/s
Land Use Wetlands Water
bodies
Artificial
surfaces
Forest and
semi natural
areas
Agricultural
l areas
Slope 20-41% 15-20% 10-15% 5-10% 0-5 %
Urban 0-300m 300-500m 500-750m 750-1000m >1000m
Roads 0-300m 300-500m 500-750m 750-1000m 1000-
5000m
SCI 0-300m 300-500m 500-750m 750-1000m >1000m
SPA 0-300m 300-500m 500-750m 750-1000m >1000m
Rivers 0-300m 300-500m 500-750m 750-1000m >1000m
METHODOLOGY AND METHODS
o In order to
standardize
the factor maps, they
were reclassified
according to their
perceived importance,
as resulted from the
research literature,
as follows:

10. Reclassification of Slope and Land Use Maps according to the common value
scale

11. - Euclidian Distance from Roads and Urban
- Reclassified to the standardised numeric range

12. - Euclidian Distance from SPA and SCI
- Reclassified to the standardised numeric range

13. - Euclidian Distance from Rivers and Lakes
- Reclassified to the standardised numeric range

14. METHODOLOGY AND METHODS
 Assigning weights to factor maps
o Weighting of factor maps was done using a pairwise comparison
between factors. Comparing them in sets of two, the highest importance
between them was identified and the final ranking of the criteria
determined.
o Using the pairwise comparison, uncertainties regarding the overall
importance of each
factor were eliminated. Weight
Wind A 16
Land Cover B 13
Slope C 11
Urban D 13
SPA E 11
SCI F 11
Roads G 11
Rivers H 8
Lakes I 8
SUM 100
Wind Land Slope Urban SPA SCI Roads Rivers Lakes
A B C D E F G H I
Wind A - B A D A A A A A
Land B - B B B B G H I
Slope C - C C C C H I
Urban D - D D G D D
SPA E - EF E E E
SCI F - F F F
Roads G - G G
Rivers H - HI
Lakes I -

15. o Weighted overlay
It works by applying the set of weights determined
determined
earlier, adding the weights values across the image
image to create a composite map, representing a
representing a continuous surface of suitability.
METHODOLOGY AND METHODS
o Fuzzy overlay
Fuzzy Sum used within the project adds the fuzzy
values of standardised individual sets to the cell
location it belongs to, the output map
representing an increasing linear combination
function influenced by the number of factors used.
 Alternative Scenarios
The model developed uses two sets of wind
data. Therefore two alternative scenarios were
generated.
 Combining factor maps

16. RESULTS AND DISCUSSIONS

17. In scenario 1(Ro wind data), the percent of
the area that falls under ‘ideal’ suitability is
0.36% representing 56.75 sq km, whereas the
‘unsuitable’ areas represent 66.01 %
In scenario 2 (MeteoBlue data), the percent of ‘ideal’
suitability areas has increased to 1.36% representing
212.05 sq km,
whereas the ‘unsuitable’ areas represent 61.81 %

18. Using Fuzzy Overlay, the results are similar with the ones from Weighted Overlay, the
highest suitability
areas (bright red) being identified in the same regions of Dobrogea as in Weighted
Overlay.

19. DISCUSSIONS
 Similar ‘ideal’ location for both scenarios, and for both overlay
techniques.
 Suitability model is very sensitive to the wind resource criteria.
 Few suitable areas, despite that Dobrogea has the highest wind
potential of all country regions.
 Wind turbine with 80m hub height, therefore for different types of
turbines, the suitability areas could change.
 The analysis performed could go further by evaluating each
individual site location indicated as ‘ideal’ ,

20. CONCLUSIONS
 The project presented GIS based multi-criteria evaluation approach
for identifying potential site locations for wind farms in Dobrogea,
using WLC method.
 WLC allowed to develop a suitability model which can be used as
a planning tool and help in decision making process.
 The model could substantially benefit from a centralised wind data.
However, this study tries to make predictions, provide insights and
understandings, rather than to prescribe a ‘correct’ solution to the
issue.
 The paper described how the use of WLC together with different
aggregation techniques provides a prediction of land suitability for
wind farm development.

21. THANK YOU!
Simona Petrisor
k1255387
~2014~