This document presents a hybrid Measure-Correlate-Predict (MCP) method for wind resource assessment that combines predictions from multiple nearby meteorological stations. The existing MCP methods only use data from one reference station and do not consider distance or elevation differences between stations. The new hybrid MCP method assigns weights to individual MCP predictions based on distance and elevation differences to the target site. It was evaluated using stations in North Dakota and showed improved accuracy over individual MCP methods based on error metrics and predicted wind farm power generation. The hybrid approach more accurately characterized the long-term wind distribution at the target site.

1. A Hybrid Measure-Correlate-Predict Method for
Wind Resource Assessment
Jie Zhang*, Souma Chowdhury*, Achille Messac# and Luciano Castillo**
* Rensselaer Polytechnic Institute, Department of Mechanical, Aerospace, and Nuclear Engineering
# Syracuse University, Department of Mechanical and Aerospace Engineering
** Texas Tech University, Department of Mechanical Engineering
ASME 2012 6th International Conference on Energy Sustainability
July 23-26, 2012
San Diego, CA

2. Wind Resource Assessment
 Wind resource assessment is the assessment of the potential of
developing a feasible wind energy project at a given site.
 In general, wind resource assessment includes:
 Onsite wind conditions measurement
 Correlations between onsite meteorological towers to fill in missing data
 Correlations between long term weather stations and short term onsite
meteorological towers
 Analysis of the wind shear and its variations
 Modeling of the distribution of wind conditions
 Prediction of the available energy at the site
2

3. Measure-Correlate-Predict (MCP)
• Measure-Correlate-Predict (MCP) method: predicting the long term wind
resource at target sites using the short term (1 or 2 year) onsite data, and the
co-occurring data at nearby meteorological stations.
• The accuracy of long term predictions using MCP methods is subject to:
 The availability of a nearby meteorological station, and its distance from the site
 The length of the correlation time-period
 The uncertainty associated with a specific correlation methodology
3

4. Measure-Correlate-Predict (MCP)
4

5. Research Motivation
 Existing Measure-Correlate-Predict Methods include:
• Linear Regression Method1,2;
• Variance Ratio Method2,3;
• Weibull Scale Method3;
• Mortimer Method5;
• Artificial Neural Networks (ANNs)4,5; and
• Support Vector Regression6,7.
 The existing MCP methods predict the long term wind data at the
farm site using wind data at one reference station.
 Current MCP methods do not consider the distance and the elevation
difference between the target site and the reference stations.
 How to use recorded wind data from multiple nearby reference
stations to better predict the wind conditions at the target site?
5
1: Velázquez et al. 2: Perea et.al 3: Carta and Velázquez 4: Mohandes et al.
5: Sheppard 6: Mohandes et al. 7: Zhao et al.

6. Research Objective
Develop and explore the applicability of a hybrid Measure-Correlate-
Predict method that adaptively combines wind information from
multiple weather stations.
 The contribution of each reference station in the hybrid strategy is based
on: (i) the distance and (ii) the elevation difference between the target farm
site and the reference weather stations.
6

7. Presentation Outline
7
• Development of the Hybrid MCP Method
• Performance Evaluation Metrics
• Case Study: Stations in North Dakota
• Concluding Remarks and Future Work

8. Hybrid MCP Method
A weighted summation of the MCP predictions from individual weather stations:
8
푦 =
푛
푖=1
푤푖푓푖 (푥)
Where: n: is the number of reference sites;
푓푖 (푥): represents the 푖푡ℎ MCP model which estimates the farm site wind
condition using the 푖푡ℎ reference site data; and
푤푖: represents the weight of the 푖푡ℎ MCP model.
푤푖 = 푔 (Δ푑푖 , Δℎ푖 )
Where: Δ푑푖: is the distance between the farm site and the 푖푡ℎ reference site;
Δℎ푖: is the elevation difference between the farm site and the 푖푡ℎ
reference site.

9. Hybrid MCP Method
9
Weights selection method:
푤푖 =
1
2(푛 − 1)
×
푛 Δℎ푖
푗=1,푗≠푖
푛 Δℎ푖
푗=1
+
푛 Δ푑푖
푗=1,푗≠푖
푛 Δ푑푖
푗=1
Weights of each reference station:
 Decreases with increasing distance from the target site
 Decreases with increasing altitude difference from the target site

10. Individual Measure-Correlate-Predict Methods
 Five MCP Methods are investigated:
• Linear Regression Method
• Variance Ratio Method
• Weibull Scale Method
x: reference site; y: target farm site. 10

11. Individual Measure-Correlate-Predict Methods
• Artificial Neural Networks (ANNs)
• Support Vector Regression:
11

12. Accuracy Metrics: Statistical Measures
 The ratio of mean wind speeds:
 The ratio of wind speed variances:
 Root Mean Squared Error (RMSE):
 Maximum Absolute Error (MAE):
12

13. Accuracy Metrics: Wind Farm Output
 We compare the annual averaged power generation estimated from the
actual long-term wind data and the wind data predicted by the MCP methods.
 9-turbine wind farm
 3x3 (7D/3D) array layout
 We use the power generation model from the Unrestricted Wind Farm Layout
Optimization (UWFLO) methodology*.
13
Features of the GE-1.5MW-XLE and GE-2.5MW-XL turbines
*Chowdhury et al., Renewable Energy 2012, and ES-FuellCell 2011

14. Case Study: Selection of Stations
14
Station Latitude (deg) Longitude (deg) Elevation (m)
Dazey 47.183 -98.138 439
Galesburg 47.21 -97.431 331
Hillsboro 47.353 -96.922 270
Mayville 47.498 -97.262 290
Pillsbury 47.225 -97.791 392
Prosper 47.002 -97.115 284

15. Accuracy of Predicted Wind Data
15
Maximum Absolute Error (MAE)
The length of correlation period (hours) The length of correlation period (hours)
RMSE MAE
Root Mean Squared Error (RMSE)
 The average RMSE value of hybrid MCP methods is approximately 35%
smaller than that of traditional MCP methods.
 The average MAE value of hybrid MCP methods is approximately 21%
smaller than that of traditional MCP methods.

16. Accuracy of the Overall Distribution of the Predicted
Wind Data
16
The ratio of wind speed variances
The length of correlation period (hours) The length of correlation period (hours)
Variance Ratio
Mean Ratio
The ratio of mean wind speeds
 The closer the value of the ratio is to one, the more accurate is the estimated
wind pattern
 The four hybrid MCP methods perform best when the correlation period is
between 6000-8500 hours (approx. 8-12 months).

17. Results and Discussion: Wind Farm Metrics
17
The length of correlation period (hours)
Power generation: GE-2.5MW-XL
Farm Layout
Power generation (W)
 The hybrid curves (solid lines) are closer to the actual power generation
curve (black line) than the individual MCP prediction curve (dashed lines).
 In most cases, the power generation is overestimated when using the wind
data predicted by MCP methods.

18. Concluding Remarks
18
 This paper developed a hybrid MCP strategy to predict the long term
wind resource information at a farm, using the recorded data of
multiple reference stations.
 The contribution of each weather station depends on the distance and
elevation difference of the site from the reference station.
 Two primary sets of performance metrics are used to evaluate the
hybrid MCP method: (i) statistical metrics, and (iii) wind farm
performance metrics.
 The results showed that:
 Using wind data from multiple reference stations has the potential to better
predict the long term wind condition at the targeted farm site; and
 The power generation is generally overestimated using the data predicted
by MCP methods.

19. Future Work
 A more comprehensive hybrid strategy should include
wind direction information.
 The uncertainty in the MCP method should also be
characterized and analyzed.
19

20. Acknowledgement
• I would like to acknowledge my research adviser
Prof. Achille Messac, and my co-adviser Prof.
Luciano Castillo for their immense help and
support in this research.
• I would also like to thank my friend and colleague
Souma Chowdhury for leading this research.
• I would also like to thank NSF for supporting this
research.
20

21. Questions
and
Comments
21
Thank you

22. Existing Measure-Correlate-Predict Methods
 Existing Measure-Correlate-Predict Methods include:
• Linear Regression Method;
• Variance Ratio Method;
• Weibull Scale Method;
• Mortimer Method; and
• Artificial Neural Networks (ANNs)
• Vector Regression Method (two-dimensional linear regression)
 Limitations of existing MCP methods include:
• Do not consider topography;
• Do not include distance between monitoring stations; and
• Only use one reference station to predict the target farm site wind
condition.
22

23. Performance Metrics
 Wind Distribution Metrics
 Weibull distribution
 Multivariate and Multimodal Wind Distribution (MMWD)
23
• Kernel Density Estimation
• Multivariate Kernel Density Estimation
• Optimal Bandwidth Matrix Selection

24. Results and Discussion: Wind Distribution Metrics
24
Normalized Weibull k parameter Normalized Weibull c parameter
 The solid red&blue&pink lines (hybrid
linear regression method, hybrid variance
ratio, and hybrid SVR) agrees more with
the record data distribution (black line)
than the dashed red&blue&pink lines
(linear regression method, variance ratio,
and SVR).
Wind distribution

25. Results and Discussion: Mixing Combinations
• Each station can be combined into the hybrid MCP method with:
• linear regression;
• variance ratio;
• neural network; or
• SVR method
• 1024 (which is equal to 45) different combinations are investigated.
25
Mean Ratio Variance Ratio

26. Results and Discussion: Mixing Combinations
RMSE MAE
Power generation: GE-1.5MW-XLE Power generation: GE-2.5MW2-6XL
 The average value of
RMSE over the
length of correlation
period varies 4.91%
over the 1024 hybrid
MCP models.
 The average value of
the power generation
with GE-1.5MW-XLE
turbines over
the length of
correlation period
varies 5.57% over the
1024 hybrid MCP
models.