NREL is a national laboratory of the U.S. Department of Energy, Office of Energy 

Efficiency & Renewable Energy, operated by the Alliance for Sustainable Energy, LLC.

Contract No. DE-AC36-08GO28308

Kaneohe, Hawaii Wind Resource 

Assessment Report

R. Robichaud, J. Green, and B. Meadows

Technical Report

NREL/ TP-7A30-53430

November 2011
NREL is a national laboratory of the U.S. Department of Energy, Office of Energy 

Efficiency & Renewable Energy, operated by the Alliance for Sustainable Energy, LLC.

National Renewable Energy Laboratory

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Golden, Colorado 80401

303-275-3000 • www.nrel.gov

Contract No. DE-AC36-08GO28308

Kaneohe, Hawaii Wind Resource 

Assessment Report

R. Robichaud, J. Green, and B. Meadows

Prepared under Task Nos. WER9.4570, WER9.5001

Technical Report

NREL/ TP-7A30-53430

November 2011
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i

Executive Summary

The Department of Energy (DOE) has an interagency agreement to assist the Department of 

Defense (DOD) in evaluating the potential to use wind energy for power at residential properties 

at DOD bases in Hawaii. DOE assigned the National Renewable Energy Laboratory (NREL) to 

facilitate this process by installing a 50-meter (m) meteorological (Met) tower on residential 

property associated with the Marine Corps Base Housing (MCBH) Kaneohe Bay in Hawaii.

This report describes the wind resource measured at MCBH Kaneohe Bay. The data-set analyzed 

in this report includes a general validation and summarization of the 10-minute data taken from 

August, 2009 through September, 2010.

The mean annual wind speeds from the data collected at the MCBH Kaneohe Bay Met tower can 

be seen in the first row of Table 1. With the 20-m data at MCBH Kaneohe Bay normalized to the 

long-term wind speeds from the Marine Corps Air Station (MCAS) Kaneohe Bay airport. The 

vertical wind shear factors from the collected data at MCBH Kaneohe Bay were applied to the 

adjusted 20-m data to determine expected wind speeds at the 35 and 10.7-m heights at MCBH 

Kaneohe Bay from August 1998 through July 2010 in an average wind year. The overall net 

change in wind speeds at 20 m was a ~3.7% decrease in the mean wind speed of collected data at 

20 m at MCBH Kaneohe Bay. The adjusted data was used as the basis for the subsequent 

economic analysis.

Table 1 shows the mean annual wind speed from the data collected at MCBH Kaneohe Bay from

August 1, 2009 – July 31, 2010. The row labeled ‘Kaneohe Estimated Long Term Mean Wind 

Speed’ indicates the new mean annual wind speed at each anemometer height after applying the 

Measure Correlate Predict (MCP) method and the vertical wind shear factors from the Met tower 

at MCBH Kaneohe Bay to the long term data from MCAS Kaneohe Bay airport’s 10-m data. 

The net change as a percentage is shown in the next row down. 

Table 1. Mean Annual Wind Speed Before and After Long Term Correlation at MCBH Kaneohe Bay

Met Tower

The mean annual wind-power densities from both the collected data at MCBH Kaneohe Bay and 

the extrapolated data are also shown in Table 1. The mean annual wind power density is the 

average power per-unit area (i.e., power/m2

) over each time-step in the year. A wind power 

density of 182 W/m2 at 10.7 m (expected height of a Skystream 3.7 wind turbine at this location) 

is considered a Class 1+ wind resource. At the other heights of interest, 20 and 35 m, the wind 

power density is 213 and 253 W/m2, or Class 2 winds.

It should be noted that though the wind-power densities are not high, and the power available in 

the wind is generally considered insufficient for an economic wind turbine project, the high cost 

of electricity in Hawaii (~$0.26/kWh) is a significant driver in overall wind turbine economics. 

Measurement Unit 10.7 m 20 m 35 m 50 m

Kaneohe Wind Speed as Collected (m/s) - 6.39 6.78 6.84

Kaneohe Estimated Long Term Mean Wind Speed (m/s) 5.83 6.16 6.49 -

Percent Increase in Wind Speed (%) - -3.6% -4.3% -

Kaneohe Estimated Long Term Mean Power Density (W/m2) 182 213 253 -
ii

In this case, within the realm of current commercial rates, a simple payback of less than 20 years 

is estimated with several mid-size wind turbines. At a US average of ~$0.10/kWh, simple 

paybacks would increase to ~30-50 years.
iii

Table of Contents

Executive Summary....................................................................................................................... i

Table of Contents......................................................................................................................... iii

List of Figures............................................................................................................................... iv

List of Tables................................................................................................................................ vi

Station Location .............................................................................................................................1

Wind in Hawaii ..............................................................................................................................3

Instrumentation and Equipment..................................................................................................5

Data Recovery and Validation......................................................................................................6

Wind Speed Sensor Summary ......................................................................................................6

Wind Resource Summary .............................................................................................................7

Wind Speed Data ...................................................................................................................................... 7

Wind Direction Data ................................................................................................................................. 9

Wind Frequency (Probability) Distribution ............................................................................................ 11

Vertical Wind Shear................................................................................................................................ 13

Turbulence Intensity ............................................................................................................................... 16

Comparison of MCBH Kaneohe Bay Anemometer Data with Long Term Reference Data 19

Marine Corps Air Station Kaneohe Bay....................................................................................19

MCAS Kaneohe Bay Airport Long Term Data ........................................................................20

Correlation of MCBH Kaneohe Bay 20m Met to MCAS Kaneohe Bay Airport Met...........22

Adjusting the Collected Wind Speed Data at MCBH Kaneohe Bay.......................................24

Estimated Wind Turbine Performance .....................................................................................25

Economic Analysis of Wind Turbine Performance ..................................................................25

Summary and Conclusions..........................................................................................................26

Next Steps .....................................................................................................................................27

Appendix A – Other Wind Data from MCBH Kaneohe Bay Met Tower ..............................28

Appendix B – Commissioning Report........................................................................................32

Appendix C – Met Tower Configuration & Components........................................................35

Appendix D – Wind Turbine Spec Sheets .................................................................................40

Appendix E – Electricity Rates...................................................................................................43
iv

List of Figures

Figure 1. MCBH Kaneohe Bay 50-m Met tower location and potential Skystream wind turbine site.1

Figure 2. The view to the north-through-east arc (prevailing wind direction is from the northeast) 

from southwest of the Met tower site. .........................................................................................2

Figure 3. 50-m wind speed map of Oahu, HI. ..........................................................................................3

Figure 4. 50-m wind power density map of Oahu, HI. ............................................................................. 4

Figure 5. Enlarged 50m wind power density map of MCBH Kaneohe Bay Area on Oahu, HI. ............ 5

Figure 6. Box plot of the wind speed data at 20 m at MCBH Kaneohe Bay for 8/1/2009 – 7/31/2010 .7

Figure 7. Seasonal Wind Speed Profile at MCBH Kaneohe Bay for 8/1/2009 – 7/31/2010 ...................8

Figure 8. Diurnal wind speed profile at MCBH Kaneohe Bay for 8/1/2009 – 7/31/2010........................8

Figure 9. Diurnal wind speed profile by month at MCBH Kaneohe Bay for 8/1/2009 – 7/31/2010.......9

Figure 10. Wind Frequency Rose and Mean of Speed Rose at MCBH Kaneohe Bay for 8/1/2009 –

7/31/2010 .........................................................................................................................................9

Figure 11. Total Wind Energy Rose at MCBH Kaneohe Bay for 8/1/2009 – 7/31/2010........................10

Figure 12. Wind Power Density Rose at 20 m by month at MCBH Kaneohe Bay for 8/1/2009 –

7/31/2010 .......................................................................................................................................11

Figure 13. Wind Speed Frequency Distribution at 20 m at MCBH Kaneohe Bay for 8/1/2009 –

7/31/2010 .......................................................................................................................................12

Figure 14. Monthly Wind Speed Distributions at 20 m at MCBH Kaneohe Bay for 8/1/2009 –

7/31/2010 .......................................................................................................................................12

Figure 15. Vertical Wind Shear Profile at MCBH Kaneohe Bay for 8/1/2009 – 7/31/2010 ................... 1

Figure 16. Daily Wind Shear Profile by month at MCBH Kaneohe Bay for 8/1/2009 – 7/31/2010......16

Figure 17. Turbulence Intensity at 20 m at MCBH Kaneohe Bay for 8/1/2009 – 7/31/2010 ................17

Figure 18. Turbulence Intensity vs. Wind Speed at 20 m at MCBH Kaneohe Bay for 8/1/2009 –

7/31/2010 .......................................................................................................................................18

Figure 19. Turbulence Intensity by month at 35 m at MCBH Kaneohe Bay for 8/1/2009 – 7/31/201018

Figure 20. Turbulence Intensity Rose at 20.3 m at MCBH Kaneohe Bay for 8/1/2009 – 7/31/2010 ...19

Figure 21. MCAS Kaneohe Bay airport and MCBH Kaneohe Bay 50-m Met tower sites. ..................20

Figure 22. Long and short term mean wind speeds at MCAS Kaneohe Bay airport Met Tower.......22

Figure 23. Correlation of mean daily wind speed for MCAS Kaneohe Bay airport and MCBH 

Kaneohe Bay Met Towers ...........................................................................................................23

Figure 24. Estimated long term mean monthly wind speeds at MCBH Kaneohe Bay at 20 m..........28

Figure 25. Estimated long term mean annual wind speeds at MCBH Kaneohe Bay at 20 m...........28

Figure 26. Long term monthly mean diurnal profile at MCBH Kaneohe Bay at 20 m ........................29

Figure 27. Probability of Exceedence graph and Probability Table for Long Term at 20 m at MCBH 

Kaneohe Bay ................................................................................................................................29

Figure 28. Wind speed data from MCAS Kaneohe Bay airport Met tower ..........................................31

Figure 29. Anemometer #40 (item 1899) Photo Courtesy of NRG Systems 

http://www.nrgsystems.com/ ......................................................................................................35

4
v

Figure 30. Wind vane NRG #200P (item 1904) Photo Courtesy of NRG Systems 

http://www.nrgsystems.com/ ......................................................................................................36

Figure 31. Boom, Side, 1.53m (60.5"), Galv, with clamps (item: 3390) Photo Courtesy of NRG 

Systems http://www.nrgsystems.com/ ......................................................................................36

Figure 32. Guy wire degradation due to marine effects at MCBH Kaneohe Bay................................39
vi

List of Tables

Table 1. Mean Annual Wind Speed Before and After Long Term Correlation at MCBH Kaneohe Bay 

Met Tower ........................................................................................................................................i

Table 2. Data Set Summary at MCBH Kaneohe Bay................................................................................2

Table 3. Instrumentation Summary at MCBH Kaneohe Bay...................................................................5

Table 4. Wind Speed Sensor Summary at MCBH Kaneohe Bay ............................................................6

Table 5. Flagged Data Summary at MCBH Kaneohe Bay for 8/1/2009 – 7/31/2010...............................7

Table 6. Power Law Exponent and Surface Roughness Length at MCBH Kaneohe Bay for 8/1/2009 

– 7/31/2010 ....................................................................................................................................14

Table 7. Power Law Exponent and Surface Roughness Length by direction at MCBH Kaneohe Bay 

for 8/1/2009–7/31/2010 .................................................................................................................15

Table 8. Power Law Exponent and Surface Roughness Length by month at MCBH Kaneohe Bay 

for 8/1/2009 – 7/31/2010 ...............................................................................................................16

Table 9. Turbulence Analysis at 20 m at MCBH Kaneohe Bay for 8/1/2009 – 7/31/2010...................17

Table 10. MCAS Kaneohe Bay airport long term wind data at 10 m (3/1/1998 - 9/30/2010) ..............21

Table 11. Mean Annual Wind Speed Before and After Long Term Correlation .................................24

Table 12. Comparison of Wind Turbine Sizes and Output at Several Hub Heights ..........................25

Table 13. Representative Turbine Annual Output, Cost Savings and Simple Payback ....................26

Table 14. Raw Data Recovery Rates at MCBH Kaneohe Bay from 8/1/2009 – 7/31/2010 ...................30
1

Station Location

The monitoring site is located on the southeastern side of the Mokapu Peninsula in Kaneohe Bay. 

The 50-m Met tower was installed adjacent to the school in MCBH Kaneohe Bay. Approximate 

grid coordinates are: N 21° 26’ 32.5”, W 157° 44’ 16.7”. The monitoring location is shown in 

Figure 1. The terrain in close proximity to the Met tower is generally flat and includes some 

vacant lots towards the ocean front, the school, 1-2 story buildings and residences, with some 

ground cover provided by grasses and bushes. The Met tower is ~130 m from the ocean with a 

relatively flat profile from the site to the ocean. The site is on the windward side of Oahu and is 

regularly exposed to the trade winds from the northeast. 

Figure 1. MCBH Kaneohe Bay 50-m Met tower location and potential Skystream wind turbine site.

Source: http://www.Googlemaps.com and with labels by Jim Green, NREL

The Met tower location relative to the Ulupau Crater is shown in Figure 2, with a view to the 

north-through-east arc. The crater ranges in height from ~1 - 200 m along its curved outer wall 

on the north-west-south arc. The crater will cause significant turbulence to the wind going 

through and over it. The MCBH Kaneohe Bay Met tower site is ~1,110 - 1,200 m (~0.7 mi) from 

the southern edge of the crater. Most of the energetic winds come from the 60-75° arc, and come 

into the Met tower site directly from the ocean, not over the crater. The full extent of the 

turbulent path caused by the crater is not known and cannot be discerned from the data at the Met 

tower site.

Prevailing 

Wind Direction

60-75°

Potential site 

for Skystream 

wind turbine

Kaneohe 50 m Met tower 

Lat: 21 ° 26’32.5” N

Lon: 157 ° 44’16.7” W

Elevation: 4 m
2

Figure 2. The view to the north-through-east arc (prevailing wind direction is from the northeast) 

from southwest of the Met tower site.

Source: http://www.GoogleEarth.com and with labels by Robi Robichaud, NREL

Table 2 summarizes the details of the monitoring station over the collection period July 23, 2009 

through September 27, 2010. The data was processed using Windographer1

Table 2. Data Set Summary at MCBH Kaneohe Bay

software.

 1 Web source: http://www.mistaya.ca/

Kaneohe 50 m Met tower 

Lat: 21 ° 26’32.5” N

Lon: 157 ° 44’16.7” W

Elevation: 4 m Prevailing 

Wind Direction

60-75°

Characteristic Value

Latitude N 21° 26' 32.500"

Longitude W 157° 44' 16.700"

Elevation 4 m

Start date 8/1/2009 0:00

End date 7/31/2010 23:40

Duration 12 months

Length of time step 10 minutes

Calm threshold 3 m/s

Mean temperature 25.2 °C

Mean pressure 101.2 kPa

Mean air density 1.182 kg/m3

Power density at 50m 284 W/m2

Wind power class 2

Power law exponent 0.077

Surface roughness 0.0000693 m
3 

Wind in Hawaii

There has been interest in wind energy applications on Hawaii since the 1980’s. As can be seen 

in the 50-m wind speed map below, there is a considerable wind resource at particular locations 

on Oahu. Generally, the high ridges and some coastal areas have the best wind resource. 

Figure 3. 50-m wind speed map of Oahu, HI. 

Source: Hawaii’s Wind Energy Future2 

Wind resources are very site specific. Different sites in close proximity to each other, but with 

varying vegetation (i.e., tall trees vs. grassland or cropland), topographical features (ridges vs. 

valleys; canyons vs. mountains, etc), and surface roughness (i.e., city skyscrapers vs. flat or 

rolling farmland), may have entirely different wind regimes. One may prove to be economic, and 

another may not though reasonably close together. The Ulupau Crater provides a great example 

of a geographical/topographical feature that will cause a significant difference in the wind 

resource on different sides of and distances away from the crater. Wind maps are useful for 

 

2 Hawaii’s Wind Energy Future, High resolution wind resource maps. Web site: 

http://www.hawaiisenergyfuture.com/Articles/Wind_Energy.html#WheresWind. Accessed 12/5/2011
4 

determining from a high level view where the wind usually blows. Wind maps are not used to 

site large wind turbines/farms, as they are not that accurate. They are used to determine where it 

is merited to further investigate the wind with installation of an on-site wind monitoring station. 

Figure 4 indicates a Class 1+ - Class 2 wind resource (100 – 300 W/m2) in the MCBH Kaneohe 

Bay area.

Figure 4. 50-m wind power density map of Oahu, HI. 

Source: Hawaii’s Wind Energy Future3

An enlargement of Figure 4 provides a better view of the mapped wind resource in Figure 5. The 

Met tower and the proposed location for the wind turbine are just to the right of the purple dot 

representing MCBH Kaneohe Bay. As shown, it appears to be in a Class 2 wind regime (200 – 

300 W/m2). It is interesting to note, that based on the wind map, parts of the Ulupau Crater to the 

north-by-northeast of the Met tower site, are considered to have a Class 3 (300 – 400 W/m2) 

wind resource. 

 

3 Hawaii’s Wind Energy Future, High resolution wind resource maps. Web site: 

http://www.hawaiisenergyfuture.com/Articles/Wind_Energy.html#WheresWind. Accessed 12/5/2011
5 

Figure 5. Enlarged 50m wind power density map of MCBH Kaneohe Bay Area on Oahu, HI. 

Source: Hawaii’s Wind Energy Future4

Instrumentation and Equipment

The project instrumentation consisted of an NRG 50 m XHD NRG Tall Tower, six anemometers, 

two wind vanes, temperature sensor, and a data logger. Details of the sensor configuration are 

summarized in Table 3 below. Three of the anemometers, at 50.15, 35.05, and 20.25 m, were 

aligned at 162° (labeled Southeast or SE hereafter) and a redundant anemometer at each height 

was oriented to 342° (labeled Northwest or NW). The nominal height and orientation were used 

for simplicity, so the anemometer at 50.2 m with orientation at 342° was labeled as 50 m NW. 

Table 3. Instrumentation Summary at MCBH Kaneohe Bay

 

4 Hawaii’s Wind Energy Future, High resolution wind resource maps. Web site: 

http://www.hawaiisenergyfuture.com/Articles/Wind_Energy.html#WheresWind. Accessed 12/5/2011

Sensor Type

Sensor 

Label Measurement Height 

Boom 

Orientation 

[m] [degrees - true]

NRG # 40 Calibrated Anemometer 50 m SE Wind Speed (m/s) 50.15 162

NRG # 40 Calibrated Anemometer 50 m NW Wind Speed (m/s) 50.15 342

NRG # 40 Calibrated Anemometer 35 m SE Wind Speed (m/s) 35.05 162

NRG # 40 Calibrated Anemometer 35 m NW Wind Speed (m/s) 35.05 342

NRG # 40 Calibrated Anemometer 20 m SE Wind Speed (m/s) 20.25 162

NRG # 40 Calibrated Anemometer 20 m NW Wind Speed (m/s) 20.25 342

NRG #200 Wind Direction Vane Dir 50 m Wind Direction (deg) 50.35 72

NRG #200 Wind Direction Vane Dir 35 m Wind Direction (deg) 35.25 72

NRG #110S Temperature Sensor Temp Temperature (deg C) 3
6

Data Recovery and Validation

The data logger sampled the sensors every two seconds and recorded the ten-minute average 

value for each sensor. The data was emailed daily to NREL for analysis. The data recovery rates 

were 100% for all sensors and the results are shown in Table 14 in Appendix A.

Wind Speed Sensor Summary

The collected data was analyzed and screened for anomalies and low-quality data. Table 4

provides an overview of a number of key parameters of the collected data.

One screen applied was to determine the effects of tower on the wind speeds recorded at each 

anemometer height. The tower can influence the wind speed that is measured by the 

anemometers through an effect known as tower shading. In some orientations, it has the effect of 

slowing down the free wind speed and in others it can accelerate the free wind speed. The effect 

can most easily be seen mathematically or graphically by comparing the wind speed ratios of the 

redundant anemometers at the same height. The wind speeds for two anemometers at the same 

height are expected to be the same or very close to the same. When they are not, it is cause for 

further investigation. The ratio of the wind speeds of these two anemometers should typically be 

1 or very close to 1. Predictable impacts of the tower can be seen when the wind must go around 

the tower (aka, ‘in the tower shadow’) to reach one of the anemometers. When one anemometer 

is in the tower shadow, the data from the other one is typically used. The data recovery rate 

shown in the final row is after the screening, aka “flagging” has been applied. The data not used 

is “flagged.”

Table 4. Wind Speed Sensor Summary at MCBH Kaneohe Bay

Variable Units

Speed 50 m 

SE

Speed 50 m 

NW

Speed 35 m 

SE

Speed 35 m 

NW

Speed 20 m 

SE

Speed 20 m 

NW

Measurement height (m) (m) 50.2 50.2 35 35 20.3 20.3

Mean wind speed (m/s) (m/s) 6.9 6.9 6.7 6.8 6.4 6.4

MMM wind speed (m/s) (m/s) 6.9 6.9 6.7 6.8 6.4 6.4

Median wind speed (m/s) (m/s) 7.1 7.2 7 7.1 6.7 6.8

Min wind speed (m/s) (m/s) 0.4 0.4 0.4 0.4 0.4 0.4

Max wind speed (m/s) (m/s) 16.5 16.4 16 16.1 15.2 15

Weibull k 2.511 2.489 2.493 2.475 2.399 2.372

Weibull c (m/s) (m/s) 7.687 7.666 7.484 7.587 7.141 7.136

Mean power density (W/m2) (W/m2) 290 288 268 280 237 237

MMM power density (W/m2) (W/m2) 290 288 268 280 237 237

Mean energy content (kWh/m2/yr) (kWh/m2/yr) 2,539 2,525 2,349 2,454 2,074 2,078

MMM energy content (kWh/m2/yr) (kWh/m2/yr) 2,539 2,525 2,349 2,454 2,074 2,078

Energy pattern factor 1.515 1.515 1.518 1.523 1.534 1.537

Frequency of calms (%) (%) 12.29 12.72 12.94 13.37 14.21 14.59

Possible records (#) 52,558 52,558 52,558 52,558 52,558 52,558

Valid records (#) 51,902 49,224 51,928 49,417 51,939 50,533

Missing records (#) 656 3,334 630 3,141 619 2,025

Data recovery rate (%) (%) 98.75 93.66 98.8 94.02 98.82 96.15
7 

Table 5 shows the data that was flagged for tower shadowing or other anomalies. Unless 

otherwise noted, all graphs and tables have the flagged data removed from the subsequent 

analysis. 

Table 5. Flagged Data Summary at MCBH Kaneohe Bay for 8/1/2009 – 7/31/2010 

Wind Resource Summary 

Wind Speed Data 

Wind speed data was collected at 50, 35, and 20 m with a redundant wind speed sensor at each 

height. The wind speed data from the anemometer designated as the Northwest (or NW) at 20 m 

is predominantly shown in the figures that follow as this was closest to the expected height of a 

wind turbine (e.g., the hub height of a Skystream 3.7 wind turbine is ~13.7 m or 45 ft) for this 

site. A box plot indicating the monthly maximum wind speed, the daily high, the monthly mean, 

the daily low and monthly minimum wind speed of the collected 20 m NW data is shown in 

Figure 6. 

Figure 6. Box plot of the wind speed data at 20 m at MCBH Kaneohe Bay for 8/1/2009 – 7/31/2010 

Label

Unflagged - 

Valid Records

Flagged - 

Tower Shading

Speed 50 m SE 51,759 656

Speed 50 m NW 49,569 2,846

Speed 35 m SE 51,787 628

Speed 35 m NW 49,274 3,141

Speed 20 m SE 51,809 606

Speed 20 m NW 50,266 2,149

Direction 50 m 52,558 0

Direction 35 m 52,558 0

Temperature 52,558 0

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Ann

0

4

8

12

16

Value (m/s)

Monthly Statistics for Speed 20 m NW

Speed 20 m NW

max

daily high

mean

daily low

min
8 

In Figure 7, the wind speeds at each anemometer height are plotted against time to depict the 

seasonal trends. Wind speed at a location typically increases with increased height above the 

ground. The collected data follows that pattern though the difference in wind speeds at 35 m vs. 

50 m is relatively small. 

Figure 7. Seasonal Wind Speed Profile at MCBH Kaneohe Bay for 8/1/2009 – 7/31/2010 

Figure 8 shows the annual average diurnal (daily) profile for the site. In general, the winds 

increase during the morning hours and decrease into early evening. Late evening through early 

morning tend to be the calmest. Overall, there is not a lot of difference between afternoon and 

nighttime winds. The wind is relatively steady throughout the day. 

Figure 8. Diurnal wind speed profile at MCBH Kaneohe Bay for 8/1/2009 – 7/31/2010 

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 0

2

4

6

8

10

Mean Wind Speed (m/s)

Monthly Wind Speed Profile

Speed 50 m SE

Speed 50 m NW

Speed 35 m SE

Speed 35 m NW

Speed 20 m SE

Speed 20 m NW

0 6 12 18 24 0

2

4

6

8

Mean Wind Speed (m/s)

Mean Diurnal Profile

Hour of Day

Speed 50 m SE

Speed 50 m NW

Speed 35 m SE

Speed 35 m NW

Speed 20 m SE

Speed 20 m NW
9 

Figure 9. Diurnal wind speed profile by month at MCBH Kaneohe Bay for 8/1/2009 – 7/31/2010 

Wind Direction Data 

The Wind Frequency Rose in Figure 10 shows the frequency the wind blows from each 

direction as measured at 35 m. As shown, the winds most frequently come from the northeastby-east sector. The Mean Wind Speed Rose on the right indicates the mean (average) wind 

speeds from all directions. As shown, the stronger winds tend to come from both the norththrough-east sector. 

Figure 10. Wind Frequency Rose and Mean of Speed Rose at MCBH Kaneohe Bay for 8/1/2009 – 

7/31/2010 

Mean of Speed 20 m SE

0°

22.5°

45°

67.5°

90°

112.5°

135°

157.5°

180°

202.5°

225°

247.5°

270°

292.5°

315°

337.5°

2.6 m/s

5.2 m/s

7.8 m/s

Wind Frequency Rose

0°

22.5°

45°

67.5°

90°

112.5°

135°

157.5°

180°

202.5°

225°

247.5°

270°

292.5°

315°

337.5° 13% calm

16%

32%

48%

Figure 9 depicts the diurnal (daily) wind pattern by month. 
10 

The Total Wind Energy Rose in Figure 11 indicates that most of the wind energy during the 

course of the year comes from the northeast-by-east. The Met tower site itself, or the proposed 

wind turbine site, are well-situated to take advantage of the high wind power density in the 

prevailing winds. In siting a wind turbine near the Met tower site at MCBH Kaneohe Bay, 

relatively low surface roughness or obstructions between the wind turbine and the shore should 

be a priority. Siting a turbine(s) close to the water will help to maximize the availability of the 

wind resource while minimizing turbulence. Surface obstructions (trees or buildings) between 

the turbine(s) and the water should be avoided as they will increase the turbulence intensity and 

decrease the overall wind resource the wind turbine will experience. 

Figure 11. Total Wind Energy Rose at MCBH Kaneohe Bay for 8/1/2009 – 7/31/2010 

The graphic below shows how the Total Wind Energy Rose varies by month. A “common scale” 

is used in this analysis which means the relative size of the wind resource in January, February, 

and December is considerably smaller than the other months. There is also a considerable 

variance in the prevailing wind direction during both January and December with a significant 

portion of the wind coming from directions other than the northeast. As these winds in these 

months are much less energetic than the winds during the rest of the year, minimizing surface 

obstructions in these other directions are not nearly as critical as minimizing surface obstructions 

in the northeast-by-east sector. 

Total Wind Energy (20.3 m)

0°

22.5°

45°

67.5°

90°

112.5°

135°

157.5°

180°

202.5°

225°

247.5°

270°

292.5°

315°

337.5°

22%

44%

66%
11 

Figure 12. Wind Power Density Rose at 20 m by month at MCBH Kaneohe Bay for 8/1/2009 – 

7/31/2010 

Wind Frequency (Probability) Distribution 

Figure 13 illustrates the frequency (%) of time that the wind (at 20 m) is at a given wind speed. 

This probability distribution function is called a Weibull distribution. There are two commonly 

used factors to describe the characteristics of the distribution function, the Weibull c and Weibull 

k factors. The Weibull c is the scale factor for the distribution related to the annual mean wind 

speed. The Weibull k value is a unitless measure indicating how narrowly/widely the wind 

speeds are distributed about the mean with values ranging from 1.0 – 3.0. 

The best fit Weibull distribution parameters for the measured data are k = 2.37 and c = 7.13 m/s. 

The distribution below shows that the most frequent winds, or mode of the data set, are between 

5-7 m/s as measured by the wind sensor at 20 m. 
12 

Figure 13. Wind Speed Frequency Distribution at 20 m at MCBH Kaneohe Bay for 8/1/2009 – 

7/31/2010 

The figure below illustrates how the wind speed distribution varies throughout the year. 

Figure 14. Monthly Wind Speed Distributions at 20 m at MCBH Kaneohe Bay for 8/1/2009 – 

7/31/2010 

0 4 8 12 16 0

2

4

6

8

Frequency (%)

Probability Distribution Function

Speed 20 m NW (m/s)

Actual data Best-fit Weibull distribution (k=2.37, c=7.13 m/s)
13

Vertical Wind Shear

Vertical wind shear is defined as the change in wind speed with the change in height. Typically, 

wind speed increases as the height above the ground increases. This variation of wind speed with 

elevation is called the vertical profile of the wind speed, or vertical wind shear. In wind turbine 

engineering, the determination of vertical wind shear is an important design parameter, since it 

directly determines the productivity of a wind turbine on a tower of certain height, and it can 

strongly influence the lifetime of a turbine rotor blade as it provides an indication of different 

wind speeds acting on different parts of the rotor (i.e., turbulence).

Analysts typically use one of two mathematical relations to characterize the measured wind shear 

profile: 

• Power law profile, aka power law

• Logarithmic profile, aka log law

The power law equation is shown below. Depending upon what data is known and what is 

sought, the equation can be manipulated to solve for any of the variables.

Power Law Equation

V = wind speed at height of interest (e.g., hub height)

Vref = wind speed measured at height Zref

Z = height of interest (e.g., hub height)

Zref = height of measured data

α = wind shear exponent

The wind shear exponent, α, is often referred to as the vertical wind shear factor. It defines how 

the wind speed changes with height. When the actual wind shear value is not known, a typical 

value used to estimate the wind shear exponent is 0.14 (aka 1/7th power law). When wind speed 

readings are available at multiple heights, the wind shear factor can be calculated using the 

power law equation. That is what was done with the collected data at MCBH Kaneohe Bay. 

Table 6 shows the calculated wind shear values between the various anemometer heights.

The vertical wind shear factors from several heights with known wind speeds are used to 

estimate both the vertical wind shear factor and wind speed at other heights of interest above the 

measured data (e.g., turbine hub height). Depending upon the type of terrain and surface 

roughness features, the wind shear factor may vary from 0 to 0.4. 

The Logarithmic Law uses a parameter known as the surface roughness length (measured in 

meters) in predicting the wind shear profile. Smooth surfaces, such as calm, open sea have very 

low wind shear values (e.g., 0.0002 m); crops are a little higher at 0.05 m of surface roughness 

length. Areas with a few trees have surface roughness of about 0.1 m while cities with tall 

buildings would be about 3.0 m.

The surface roughness parameter is ‘solved for’ from the existing wind speed data at various 

heights. The resultant surface roughness characterization may not always match the actual 

surface conditions, but it serves as a descriptor of the vertical wind shear profile. The resultant 
14 

surface roughness lengths have been calculated for MCBH Kaneohe Bay and are shown in the 

tables below. The low surface roughness aligns well with the bulk of the wind coming across the 

waters of Pearl Harbor. 

The figures shown for both the power law exponent and surface roughness were calculated 

between valid data for all anemometer heights. In this location, it is assumed that 20 m may be 

near the maximum tower height for this location, so calculations represent a conservative 

approach to the vertical wind shear factor. 

Table 6. Power Law Exponent and Surface Roughness Length at MCBH Kaneohe Bay for 8/1/2009 

– 7/31/2010 

Figure 15 is a graph of the vertical wind shear profile includes anemometer data from all heights 

as the more realistic and conservative profile. As is often the case with ocean winds or winds 

very close to the shore, the vertical wind shear factor is relatively low, meaning that the wind 

speed increases relatively slowly with increased distance above the ground. Using only the wind 

speed readings from the 20.3 and 35 m anemometers results in a higher calculated vertical wind 

shear factor than is actually the case, which is not fully representative of site characteristics. 

Using the higher vertical wind shear factor to extrapolate wind speeds at 50, 60, or 80 m would 

result in estimates being higher than the wind speeds actually are at those heights. 

Speed Sensor Actual Height 

Mean Wind 

Speed

Power Law 

Exponent

Surface 

Roughness

(m) (m/s) (unitless) (m)

50 m SE 35 7.009

50 m NW 35 6.947

35 m SE 35 6.836

35 m NW 35 6.883

20 m SE 20.3 6.548

20 m NW 20.3 6.520

0.0738 0.0000394

02468

0

20

40

60

80

100

Height Above Ground (m)

Vertical Wind Shear Profile

Mean Wind Speed (m/s)

Measured data

Power law fit (alpha = 0.0738)

Log law fit (z0 = 0.0000394 m)

Figure 15. Vertical Wind Shear Profile at MCBH Kaneohe Bay for 8/1/2009 – 7/31/2010 
15 

In Table 7, the mean wind speeds at each height, power law exponent, and surface roughness 

calculation are shown for each direction sector taken from the wind vane at 35 m. It is worth 

noting for the direction sectors 22.5 - 67.5 and 67.5 - 112.5, which comprise 87.1% of the data 

points, the power law exponent is quite low at 0.068 and 0.059 respectively indicating a low 

vertical wind shear factor for winds coming from these directions. The low surface roughness 

lengths for these same direction sectors also speak to winds with low turbulence coming from 

these directions. Both of these factors are good in terms of being an indicator that this site will 

generally have less turbulence than other sites at the height of the wind turbine at ~20 - 30 m and 

above. 

Table 7. Power Law Exponent and Surface Roughness Length by direction at MCBH Kaneohe Bay 

for 8/1/2009–7/31/2010 

The daily wind shear profile for each month of the year can be seen in Figure 16. January and 

December have the highest daily wind shear factors with large swings overnight. Generally, the 

other months show much smaller to minimal wind shear variation during the day. 

Direction 

Sector

Time 

Steps

@ 50 m 

SE

@ 50 m 

NW

@ 35 m 

SE

@ 35 m 

NW

@ 20 m 

SE

@ 20 m 

NW

Power Law 

Exponent

Surface 

Roughness

 [°] [#] [m/s] [m/s] [m/s] [m/s] [m/s] [m/s] [m]

337.5° - 22.5° 1,202 5.569 5.415 5.370 5.256 5.089 5.025 0.0910 0.0005

22.5° - 67.5° 18,757 7.274 7.170 7.064 7.139 6.796 6.802 0.0680 0.0000

67.5° - 112.5° 22,402 7.884 7.841 7.751 7.786 7.490 7.427 0.0590 0.0000

112.5° - 157.5° 689 4.329 4.423 4.289 4.352 4.137 4.149 0.0620 0.0000

157.5° - 202.5° 495 3.458 3.525 3.227 3.260 2.635 2.687 0.3050 1.1261

202.5° - 247.5° 1,882 3.072 3.092 2.784 2.793 2.254 2.262 0.3470 1.6965

247.5° - 292.5° 1,722 1.834 1.818 1.730 1.777 1.266 1.282 0.4130 2.5364

292.5° - 337.5° 886 3.713 3.710 3.390 3.451 2.878 2.877 0.2830 0.8935

Mean Wind Speed Best-Fit
16 

Figure 16. Daily Wind Shear Profile by month at MCBH Kaneohe Bay for 8/1/2009 – 7/31/2010 

In Table 8, the mean wind speeds at each height, power law exponent and surface roughness 

calculation are shown for each month with the 35 m wind vane as the reference. 

Table 8. Power Law Exponent and Surface Roughness Length by month at MCBH Kaneohe Bay 

for 8/1/2009 – 7/31/2010 

Turbulence Intensity 

The figure below shows the representative and mean turbulence intensities (TI) as a function of 

wind speed at 20 m. The TI is defined as the standard deviation of the wind speed within a timestep divided by the mean wind speed over that time step and is a measure of the gustiness of the 

wind. High turbulence can lead to increased turbine wear and potentially increased operations 

and maintenance (O&M) costs. At lower wind speeds, the calculated turbulence intensity is often 

higher as can be seen in Figure 17. At low wind speeds, the turbulence is of little consequence to 

the wind turbine itself as the power in the wind is low. Turbulence at higher winds speeds is of 

0 6 12 18 24

0.00

0.05

0.10

0.15

0.20

0.25

Power Law Exponent

Daily Wind Shear Profile

Hour

All data

Jan

Feb

Mar

Apr

May

Jun

Jul

Aug

Sep

Oct

Nov

Dec

Month Time Steps

@ 50 m 

SE

@ 50 m 

NW

@ 35 m 

SE

@ 35 m 

NW

@ 20 m 

SE

@ 20 m 

NW

Power Law 

Exponent

Surface 

Roughness

[mon] [#] [m/s] [m/s] [m/s] [m/s] [m/s] [m/s] [m]

Jan 3,442 4.27 4.34 4.04 4.03 3.71 3.68 0.167 0.0781

Feb 3,201 4.85 4.81 4.65 4.71 4.38 4.34 0.114 0.0046

Mar 4,187 8.96 8.81 8.68 8.76 8.29 8.28 0.079 0.0001

Apr 3,944 8.55 8.42 8.31 8.39 8.02 7.98 0.067 0.0000

May 4,403 8.17 8.12 8.00 8.06 7.73 7.67 0.064 0.0000

Jun 4,310 7.76 7.69 7.62 7.67 7.37 7.35 0.054 0.0000

Jul 4,433 7.07 7.02 6.94 6.99 6.73 6.76 0.049 0.0000

Aug 4,289 7.70 7.64 7.56 7.61 7.27 7.22 0.064 0.0000

Sep 4,259 7.13 7.06 7.00 7.04 6.74 6.67 0.064 0.0000

Oct 4,167 5.72 5.64 5.61 5.64 5.35 5.32 0.072 0.0000

Nov 4,028 8.69 8.60 8.48 8.53 8.10 8.09 0.074 0.0000

Dec 3,372 3.63 3.65 3.52 3.54 3.27 3.25 0.123 0.0089

Mean Wind Speed Best-Fit
17 

greater interest and concern to wind turbine manufacturers as it causes increased component 

fatigue which can result in increased O&M costs. 

Figure 17. Turbulence Intensity at 20 m at MCBH Kaneohe Bay for 8/1/2009 – 7/31/2010 

Table 9 displays the relevant turbulence parameters per wind speed bin. The orange-shaded box 

indicates that the mean TI at 15 m/s, is below the IEC Category rating for categories A, B, and C. 

Table 9. Turbulence Analysis at 20 m at MCBH Kaneohe Bay for 8/1/2009 – 7/31/2010 

The scatterplot in Figure 18 provides a visual display of the array of data that are averaged to 

produce the discrete curves in Figure 17 above. 

0 5 10 15 20

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

Turbulence Intensity

Turbulence Intensity at 20.3 m

Wind Speed (m/s)

Representative TI

IEC Category A

IEC Category B

IEC Category C

Quantity Value

Records in 15 m/s bin 3

Mean TI at 15 m/s 0.086

Representative TI at 15 m/s 0.11

IEC3 turbulence category C

Bin Bin 

Midpoint

Bin 

Endpoints

Records in 

Bin

Mean TI Standard 

Deviation of TI

Representative TI Peak TI

(#) (m/s) (m/s) (m/s)

1 1 0.5 1.5 2,167 0.498 0.168 0.713 1.222

2 2 1.5 2.5 2,356 0.278 0.128 0.442 0.870

3 3 2.5 3.5 2,676 0.213 0.105 0.347 1.077

4 4 3.5 4.5 3,382 0.188 0.101 0.317 0.762

5 5 4.5 5.5 4,673 0.153 0.087 0.265 0.644

6 6 5.5 6.5 6,260 0.122 0.072 0.214 0.484

7 7 6.5 7.5 7,527 0.102 0.057 0.175 0.492

8 8 7.5 8.5 7,664 0.090 0.041 0.142 0.506

9 9 8.5 9.5 6,247 0.086 0.033 0.129 0.461

10 10 9.5 10.5 3,569 0.087 0.029 0.124 0.396

11 11 10.5 11.5 1,708 0.085 0.024 0.116 0.288

12 12 11.5 12.5 698 0.086 0.022 0.114 0.267

13 13 12.5 13.5 127 0.089 0.017 0.111 0.155

14 14 13.5 14.5 18 0.090 0.019 0.114 0.145

15 15 14.5 15.5 3 0.086 0.019 0.110 0.107

16 16 15.5 16.5 0 0.000 0.000 0.000 0.000
18 

Figure 18. Turbulence Intensity vs. Wind Speed at 20 m at MCBH Kaneohe Bay for 8/1/2009 – 

7/31/2010 

The monthly TI factors are displayed in Figure 19. The fall/winter months of December through 

February experienced the most turbulence during the period of collected data. 

Figure 19. Turbulence Intensity by month at 35 m at MCBH Kaneohe Bay for 8/1/2009 – 7/31/2010 

The turbulence intensity rose in Figure 20 illustrates that the representative and mean turbulence 

comes typically comes from directions (i.e., west-northwest and southeast) other than the 

primary wind direction (northeast – east). This provides evidence that the source of the 

turbulence is not from the energetic winds from the primary wind direction which is good. 

0 4 812 16

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

Speed 20 m NW TI

Speed 20 m NW TI vs. Speed 20 m NW

Speed 20 m NW (m/s)
19

Figure 20. Turbulence Intensity Rose at 20.3 m at MCBH Kaneohe Bay for 8/1/2009 – 7/31/2010

Comparison of MCBH Kaneohe Bay Anemometer Data with Long 

Term Reference Data

It is important to consider if the monitoring period data reflects a high, low, or average year in 

terms of wind resource. Different techniques are used to estimate the long term wind resource 

from a short term Met tower study. One common method is Measure Correlate Predict (MCP), 

where the measured data is correlated to a nearby monitoring station (aka the reference site) with 

long term wind data. The correlation relationship is then applied to the long term data set to 

arrive at a comparable long term data set at the site of interest. The MCP method was applied in 

this analysis.

Marine Corps Air Station Kaneohe Bay 

The Marine Corps Air Station (MCAS) Kaneohe Bay airport runway is approximately ~2.5 - 4.0 

km (~1.5 - 2.5 mi) away from the MCBH Kaneohe Bay 50-m Met tower, depending upon which 

part of the runway is being measured to. The terrain between the two sites is relatively flat with

the notable exception of a 100-m hill with a circular base directly between the runway and the 

Met tower which can be seen Figure 21. The area in between the runway and the 50-m Met 

tower has numerous 1 - 2 story commercial, airport, and military buildings, and is heavily built 

with residential housing. The residential areas have heavy tree cover while the more commercial 

and airport sites have few trees.

Northeast-by-east of the airport is the Ulupau Crater which rises ~1 - 200 m (~330 - 660 ft) 

above sea level and is ~3 - 5 km (~2 - 3 mi) away from the MCAS Kaneohe Bay airport. The 

wind at the airport is likely a combination of wind that goes around and/or over the Ulupau 

Crater as it comes in off the ocean. The wind speed and direction data is collected at a single 

height, so it is not possible to determine the extent of the turbulence and the relative influence of 

these two different wind patterns. The relative location of the Met tower for each site is shown in 

Figure 21 below.

Quantity Value

Records in 15 m/s bin 3

Mean TI at 15 m/s 0.086

Representative TI at 15 m/s 0.11

IEC3 turbulence category C

Turbulence Intensity at 20.3 m

0°

30°

60°

90°

120°

150°

180°

210°

240°

270°

300°

330°

0.10

0.2

0.3

0.4

0.5 0.6

Representative TI

Mean TI
20

Figure 21. MCAS Kaneohe Bay airport and MCBH Kaneohe Bay 50-m Met tower sites.

Source: http://www.earth.google.com

MCAS Kaneohe Bay Airport Long Term Data

Wind speed and direction data has been collected at ~7.6 m (25 ft) on an hourly basis at MCAS 

Kaneohe Bay airport from January 1973 through January 1998. It is not known exactly which 

part of the runway hosts the Met tower. The Met tower was updated in February 1998 and the 

data began being collected at 10 m (~33 ft). This ~12.7 year period (1998 – 2010) at 10 m, is 

sufficient for long term correlation and it represents wind speeds at a consistent height, which is 

preferred. The mean annual wind speed from January 1, 1998 – July 31, 2010 at 10 m was 3.66 

m/s and the mean of the monthly wind speeds during this time period was 3.67 m/s. This wind 

data set was used as the long-term data set for comparison and correlation purposes with the data

collected at the MCBH Kaneohe Bay 50-m Met tower site. Long-term data and resultant wind 

profile parameters from MCAS Kaneohe Bay airport Met tower can be seen in Table 10. The 

monthly means have been combined into monthly annual means and can be seen in the column 

labeled “Mean” in the middle of the table.

MCAS Kaneohe Bay 

Airport Runway

Kaneohe 50 m 

Met Tower

100 m hill on 

MCAS base

Pali Crater -

~1-200 m high
21

Table 10. MCAS Kaneohe Bay airport long term wind data at 10 m (3/1/1998 - 9/30/2010)

The graph in Figure 22 below, on an enlarged y-axis scale, shows the long-term wind speed data 

at MCAS Kaneohe Bay airport. The annual mean wind speeds from 8/1/1998 – 7/31/2010 at 

MCAS Kaneohe Bay airport can be seen graphically in the green curved line. The annual mean 

wind speeds displayed here have been segmented to match the time period used for correlation 

with MCBH Kaneohe Bay from August 1st through July 31st each year. The long-term mean at 

MCAS Kaneohe Bay airport for 8/1/1999 – 7/31/2010, segmented as described, is 3.67 m/s as 

shown with the red line. The mean wind speed of 3.77 m/s during 8/1/2009-7/31/2010 is shown 

as the solid blue line and it has been extended as a dashed line for ease of comparison. Overall, 

the August 1, 2009 – July 31, 2010 time period represents a wind year above, by 0.098 m/s or 

2.67%, the average wind year at MCAS Kaneohe Bay airport. Consequently, the MCBH 

Kaneohe Bay wind data should be correspondingly adjusted downward by 2.67% to reflect the 

expected annual wind speeds on a long-term basis. The approach taken for correlation with a 

long term reference site, as explained in the next section, and the subsequent adjustment 

inherently includes the long term data and accounts for the variation in wind speeds during the 

8/1/2009 - 7/31/2010 time period.

Month Possible 

Records

Valid 

Records

Recovery 

Rate

Mean Median Max Std. Dev. Weibull k Weibull c

(%) (m/s) (m/s) (m/s) (m/s) (m/s)

Jan 8,928 8,222 92.1 3.46 3.6 18.0 2.39 1.701 4.0641

Feb 8,136 7,298 89.7 3.63 3.6 13.4 2.26 1.931 4.2637

Mar 8,928 7,919 88.7 3.74 4.1 11.8 2.25 2.013 4.3947

Apr 8,640 7,518 87.0 4.06 4.1 22.6 1.95 2.523 4.6745

May 8,928 8,246 92.4 3.34 3.6 8.8 1.78 2.432 3.9231

Jun 8,640 8,053 93.2 3.94 4.1 18.5 1.49 3.205 4.4470

Jul 8,928 8,273 92.7 4.10 4.1 12.0 1.39 3.580 4.5896

Aug 9,672 8,855 91.6 3.95 4.1 11.8 1.52 3.283 4.4676

Sep 9,360 8,664 92.6 3.56 3.6 39.6 1.68 0.402 3.4229

Oct 9,205 8,431 91.6 3.48 3.6 10.8 1.87 2.397 4.0867

Nov 8,640 7,985 92.4 3.46 3.6 10.8 2.14 1.983 4.0868

Dec 8,928 8,265 92.6 3.29 3.6 13.4 2.30 1.722 3.9075

106,933 97,729 91.4 3.66 4.1 39.6 1.96 2.291 4.2683

Mean of monthly means 3.67
22

Figure 22. Long and short term mean wind speeds at MCAS Kaneohe Bay airport Met Tower

Correlation of MCBH Kaneohe Bay 20m Met to MCAS Kaneohe 

Bay Airport Met 

The period of time used for correlation is August 1, 2009 – July 31, 2010 as there is concurrent 

quality wind data from both sites during this time. The wind speed data from the 10-m Met tower 

at MCAS Kaneohe Bay airport was compared to the 20-m data from the MCBH Kaneohe Bay

50-m Met tower as correlation is generally strongest with comparable heights. The mean hourly 

wind speed from each site was used for consistency. The mean of the collected data at the 

MCBH Kaneohe Bay 20-m Met tower from August 1, 2009 – July 31, 2010 at 20 m NW was 

6.39 m/s. The mean of the MCAS Kaneohe Bay airport data at 10 m during the same time period 

was 3.77 m/s. 

The hourly wind speed data collected at 20 m at the MCBH Kaneohe Bay Met tower from 

August 1, 2009 – July 31, 2010 was compared to the hourly MCAS Kaneohe Bay airport wind 

speed data during the same period to determine the degree of correlation between these two sites. 

The data was graphed in a scatterplot and a linear regression run to find both the resultant linear 

equation and the relative “goodness of fit” of the line to the data. The goodness of fit is typically 

measured by the r2 value of the regression analysis. The higher the r2 value, the higher the 

confidence that the predictions made using the linear regression equation are valid. Generally 

speaking, r2 values greater than 0.8 provide confidence that the two wind sites “see the same 

wind” and hence long term correlation will be valid. Values between 0.7-0.8 and even lower are 

sometimes used as the resultant regression equation still represents the best available predictor of 

future wind speeds. Overall, the r2 value for this data comparison is 0.1824, which is not 

considered high enough to have confidence the predictive results will be valid. 

There can be any number of reasons for the low r2 values. Possible explanations for the low r2

value in comparing these two data sets include: 

2.50

2.75

3.00

3.25

3.50

3.75

4.00

4.25

4.50

Wind Speed (m/s)

Year

Comparison of Kaneohe Bay MCAS Long Term Data

Aug 1998-July 2010 vs. Aug 2009-July 2010 

Mean Annual WS (m/s)

Overall Long Term Mean WS

Mean WS Aug 09-July 10

Extended Mean WS Aug 09-July 10
23

• The two sites are far enough apart in an area with complex terrain. For the MCAS 

Kaneohe Bay airport Met tower, the wind must flow over or around the ~1 - 200 m 

Ulupau Crater to the northeast-by-east and the ~100 m hill to the east. The prevailing 

winds for the MCBH Kaneohe Bay 50-m Met do not have to contend with either feature.

• Non-uniform exposure to buildings and other surface roughness features. All of the 

energetic wind that MCAS Kaneohe Bay sees comes across dense residential building 

zones, military buildings and other ground-cluttering features whereas the energetic 

winds that the MCBH Kaneohe Bay 50-m Met sees come off of the ocean with very low 

surface roughness.

To find a regression equation with a better correlation, a longer time period can be used – daily 

instead of hourly. With this approach, the r2 value was 0.5831 indicating a stronger correlation 

than with the hourly comparison, though still not in the preferred range. This correlation is 

between daily average wind speeds, not the more specific hourly mean wind speed. However, as 

the MCAS Kaneohe Bay data represents the best available long term data for comparison to the 

MCBH Kaneohe Bay Met tower, it was used.

Figure 23. Correlation of mean daily wind speed for MCAS Kaneohe Bay airport and MCBH 

Kaneohe Bay Met Towers

The trendline in the figure above is in the form of: Y = mx + b

The correlation equation relates wind speeds at the long term wind data site, MCAS Kaneohe 

Bay airport at 10 m, to the MCBH Kaneohe Bay site at 20 m during the same period. 

Equation 1 Correlation equation for normalizing the long term data (MCAS Kaneohe Bay

airport) to short term data (MCBH Kaneohe Bay).

Y = 1.3635x + 1.1566

Y = Expected wind speed at 31

m = 1.3635x = Slope of correlation trendline

y = 1.3635x + 1.1566

R2 = 0.5831

0

2

4

6

8

10

12

14

0 1 2 3 4 5 6 7 8 9

Kaneohe Wind Speed (m/s)

MCAS Kaneohe Bay Airport Wind Speed (m/s)

Correlation: Mean Daily WS of Kaneohe 20m and MCAS Kaneohe 

Bay 10m Met Towers

Daily WS at Kaneohe 20m & 

Kbay MCAS 10m Mets

Linear (Daily WS at Kaneohe 

20m & Kbay MCAS 10m Mets)
24

x = wind speed at 10m reference site

b = 1.1566= Y intercept of correlation trendline

r

2 = 0.5831

With an acceptable correlation, the equation above was applied to all the daily data from MCAS 

Kaneohe Bay airport at 10 m from August 1, 1998 – July 31, 2010 to MCBH Kaneohe Bay at 20 

m to derive an equivalent 20 m long term data set for MCBH Kaneohe Bay. The long term mean 

wind speed at 20 m was estimated to be ~6.16 m/s. The vertical wind shear factors for MCBH 

Kaneohe Bay from August 1, 1998 – July 31, 2010 were applied to this equivalent long term data 

set to create a full equivalent long term data set at 35 m and 10.7 m for performance and 

economic modeling of wind turbines.

Adjusting the Collected Wind Speed Data at MCBH Kaneohe Bay

With the 20-m data at MCBH Kaneohe Bay normalized to the long term wind speeds from 

MCAS Kaneohe Bay airport, the vertical wind shear factors from the collected data at MCBH 

Kaneohe Bay were applied to the adjusted 20-m data to determine expected wind speeds at the 

35 and 10.7-m heights at MCBH Kaneohe Bay from August 1998 through July 2010 in an 

average wind year. The overall net change in wind speeds at 20 m was a ~3.7% decrease in the 

mean wind speed of collected data at 20 m at MCBH Kaneohe Bay. 

It should be recalled from the graphic in Figure 22 that Aug 2009 – July 2010 represented a wind 

year that was 2.7% above the average wind year and the wind speed data should be adjusted 

downward to do this. The 3.7% represents that downward adjustment. As applied, it is 1% overadjusted. This discrepancy implies the long term wind speed estimate at 20 m at MCBH Kaneohe

Bay might be 0.06 m/s slower than expected. This may be the result of the low r2 value. Due to 

the small magnitude of this adjustment relative to the mean wind speed, no further adjustment 

was made. The adjusted data was used as the basis for the subsequent economic analysis.

Table 11 shows the mean annual wind speed from the data collected at MCBH Kaneohe Bay

from August 1, 2009 – July 31, 2010. The row labeled ‘Kaneohe Estimated Long Term Mean 

Wind Speed’ indicates the new mean annual wind speed at each anemometer height after 

applying the MCP method and the vertical wind shear factors from the Met tower at MCBH 

Kaneohe Bay to the long term data from MCAS Kaneohe Bay airport’s 10 m data. The net 

change as a percentage is shown in the next row down. Below that are the calculated wind power 

densities at the heights of interest for a wind turbine at this site. 

Table 11. Mean Annual Wind Speed Before and After Long Term Correlation

Measurement Unit 10.7 m 20 m 35 m 50 m

Kaneohe Wind Speed as Collected (m/s) - 6.39 6.78 6.84

Kaneohe Estimated Long Term Mean Wind Speed (m/s) 5.83 6.16 6.49 -

Percent Increase in Wind Speed (%) - -3.6% -4.3% -

Kaneohe Estimated Long Term Mean Power Density (W/m2) 182 213 253 -
25

Estimated Wind Turbine Performance

The normalized and correlated wind speed data, with slightly decreased wind speeds overall, was 

used to estimate the performance of several small-size wind turbines. Some of the key outputs 

are shown in Table 12. Note the rated power for the turbines varies from 1.8 kW to 10 kW, so 

the output should likewise vary by comparable ratios. The hub height for the Skystream was 

realistic for use in this project. All of the other models have comparable output at 20 and 30 m. 

Table 12. Comparison of Wind Turbine Sizes and Output at Several Hub Heights

Economic Analysis of Wind Turbine Performance

The economics of any particular wind turbine depend upon a number of factors and costs – not 

all of which can be known accurately a priori. Items such as the: wind turbine cost, geotechnical 

requirements and excavation costs, installation costs, tower height, shipping/freight costs, 

permitting, etc. are all important cost components and specific costs may not be publically 

available. Costs estimates were made to conduct the analyses with an added ~20% factor to 

account for a generally higher cost of living in Hawaii. The request for proposal (RFP) will yield 

more concrete pricing for installed turbine costs at MCBH Kaneohe Bay. 

Commercial electricity rates from the DOE’s Energy Information Agency2 (EIA) were used to 

determine the expected annual value of the electricity produced and the estimated years to 

payback. The average retail rate for electricity in the commercial sector was $0.2665/kWh in 

November 2010 vs. $0.1007/kWh in the US as a whole. The EIA data indicated that Hawaiian 

electricity rates increased by ~10.2% from November 2009 to November 2010 vs. an overall 

increase of 2.2% across the country, a nearly five-fold increase in Hawaii. As electricity prices 

are subject to change, rather than analyze with a single static price, a range of prices were used to 

provide greater insight to the effect of price on simple payback in years in Table 13.

 2 Average Retail Price of Electricity to Ultimate Customers by End-Use Sector, by State,

http://www.eia.doe.gov/cneaf/electricity/epm/table5_6_a.html

Turbine Model Rated 

Power

Hub 

Height

Hub Height 

WS

Time At Zero 

Output

Mean Net 

Power Output

Mean Net 

Energy Output

Net Capacity 

Factor

Turbine (kW) (m) (m/s) (%) (kW) (kWh/yr) (%)

Southwest Skystream 3.7 1.8 10.7 5.83 14.66 0.5 4,638 29.4

Southwest Skystream 3.7 1.8 20 6.16 12.97 0.6 5,229 33.2

Southwest Skystream 3.7 1.8 30 6.39 13.01 0.6 5,621 35.6

Bergey Excel-S 10 18 6.10 12.97 2.0 17,743 20.3

Bergey Excel-S 10 20 6.16 12.97 2.1 18,151 20.7

Bergey Excel-S 10 30 6.39 12.99 2.3 19,867 22.7

Endurance S-250 5 20 6.16 16.44 1.0 9,056 20.7

Endurance S-250 5 30 6.39 16.29 1.1 10,034 22.9
26

Table 13. Representative Turbine Annual Output, Cost Savings and Simple Payback

The economics of small wind turbines are challenging – they do not align with the utilitycompetitive megawatt-scale turbines that comprise large wind farms. However, the high cost of 

electricity is a significant driver in overall wind turbine economics. In this case, within the realm 

of current commercial rates, a simple payback of less than 16 years is estimated. At an average 

rate of ~$0.10/kWh, simple paybacks would increase to the ~25 - 40 year range.

The economics would improve with larger wind turbines and taller towers. 

Summary and Conclusions 

The wind resource assessment for a 50-m Met tower at MCBH Kaneohe Bay has been completed 

using collected wind data at the site, adjusting it to long term trends from the MCAS Kaneohe 

Bay airport at 10 m, then normalizing it to concurrent data from the airport using the MCP 

method. 

The Met tower location has very good exposure to the wind with minimal surface roughness and 

other impediments to the prevailing wind. Siting a wind turbine at the proposed site or at the Met

tower site would provide good exposure to the energetic winds off of the ocean. The long-term 

mean adjusted wind speed at 20 m at MCBH Kaneohe Bay is ~6.16 m/s corresponding to a mean 

power density of 253 W/m2

. This is considered a solid Class 2 wind resource. 

The overall turbulence at the site is low which helps to minimize component fatigue issues and 

repairs. The vertical wind shear factor is low which indicates it is not necessary to have a really 

tall tower to access better winds. There is a smaller advantage to going higher than at site with a 

high vertical wind shear factor.

For this site, a “behind-the-meter” wind turbine application with a small-size wind turbine aimed 

at reducing the amount of electricity purchased annually from the utility can provide a simple 

payback of less than 20 years when the retail cost of electricity is greater than $0.26/kWh.

The wind resource assessment provides the framework for the subsequent economic analysis. 

There may be other factors to consider for a wind turbine project beyond purely economics. 

Considerations of factors such as base operations, visual impacts, environmental impacts, etc. are 

necessary in scoping a wind turbine project, but are beyond the scope of this wind resource 

assessment.

 Installed Cost Cost Savings/Year Simple Payback in Years

Turbine Model

Rated 

Power

Hub 

Height

WS at 

Hub

Annual 

Energy 

Output

Capacity 

Factor

Estimate Electricity Cost Range Electricity Cost Range

Turbine (kW) (m) (m/s) (kWh/yr) (%) ($) $0.20 $0.25 $0.30 $0.20 $0.25 $0.30

Southwest Skystream 3.7 1.8 10.7 6.58 5,435 34.5 $22,000 $1,087 $1,359 $1,631 20.2 16.2 13.5

Southwest Skystream 3.7 1.8 20 7.00 6,217 39.4 $22,000 $1,243 $1,554 $1,865 17.7 14.2 11.8

Bergey Excel-S 10 10 6.53 18,130 20.7 $75,000 $3,626 $4,533 $5,439 20.7 16.5 13.8

Bergey Excel-S 10 18 6.93 21,124 24.1 $75,000 $4,225 $5,281 $6,337 17.8 14.2 11.8
27

Next Steps

NREL will work with MCBH Kaneohe Bay to develop an RFP and review submitted proposals 

in selecting a contractor for installing a Skystream wind turbine at the MCBH Kaneohe Bay site 

already selected for one. NREL will, once all necessary permits and permissions are in place, 

contract for the installation of a Skystream at MCBH Kaneohe Bay.
28 

Appendix A – Other Wind Data from MCBH Kaneohe Bay Met 

Tower 

Figure 24. Estimated long term mean monthly wind speeds at MCBH Kaneohe Bay at 20 m 

Figure 25. Estimated long term mean annual wind speeds at MCBH Kaneohe Bay at 20 m 

1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010

3

4

5

6

7

8

9

WS (m/s)

WS

1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010

5.5

6.0

6.5

7.0

WS (m/s)

WS
29 

Figure 26. Long term monthly mean diurnal profile at MCBH Kaneohe Bay at 20 m 

The Probability of Exceedence graph in Figure 27 analyzes long term data and estimate the 

probability that the annual mean speed will exceed certain values in a given year. The results can 

be seen graphically and in the table below. 

Figure 27. Probability of Exceedence graph and Probability Table for Long Term at 20 m at MCBH 

Kaneohe Bay 

Actual 

Distribution

Normal 

Distribution

Percentile (m/s) (m/s)

P99 5.156

P95 5.467 5.462

P90 5.595 5.624

P75 5.869 5.896

P50 6.211 6.199

P25 6.573 6.501

P10 6.773 6.773

P5 6.837 6.936

P1 7.241

4.5 5.0 5.5 6.0 6.5 7.0 7.5 8.0

0

20

40

60

80

100

Probability Of Exceedence (%)

Probability of Exceedence

Annual Mean (m/s)

Annual Means Best-Fit Normal Distribution
30 

Table 14. Raw Data Recovery Rates at MCBH Kaneohe Bay from 8/1/2009 – 7/31/2010 

Figure 28 on the next page provides an indication that in the 2001 - 2003 timeframe, the MCAS 

Kaneohe Bay airport Met station was not recording wind speed data as consistently and reliably 

as usual over the past 12 years. The cause for this is unknown at this time, but the result may be 

lower wind speeds reported (as averages) during some of that time than was actually the case. 

Label Units Height

Possible 

Records

Valid 

Records

Recovery 

Rate (%) Mean Min Max Std. Dev

Speed 50 m A m/s 50.2 m 52,558 52,558 100.0 6.81 0.4 16.5 2.93

Speed 50 m A SD m/s 50.2 m 52,558 52,558 100.0 0.69 0 4.5 0.374

Speed 50 m A Max m/s 50.2 m 52,558 52,558 100.0 8.47 0.4 20.2 3.31

Speed 50 m A Min m/s 50.2 m 52,558 52,558 100.0 5.02 0.4 13.7 2.65

Speed 50 m B m/s 50.2 m 52,558 52,558 100.0 6.73 0.4 16.4 2.9

Speed 50 m B SD m/s 50.2 m 52,558 52,558 100.0 0.704 0 4.5 0.374

Speed 50 m B Max m/s 50.2 m 52,558 52,558 100.0 8.41 0.4 19.5 3.28

Speed 50 m B Min m/s 50.2 m 52,558 52,558 100.0 4.9 0.4 13.3 2.58

Speed 35 m A m/s 35 m 52,558 52,558 100.0 6.63 0.4 16 2.86

Speed 35 m A SD m/s 35 m 52,558 52,558 100.0 0.701 0 4.5 0.375

Speed 35 m A Max m/s 35 m 52,558 52,558 100.0 8.34 0.4 20.6 3.26

Speed 35 m B SD m/s 35 m 52,558 52,558 100.0 0.706 0 4.5 0.377

Speed 35 m B Max m/s 35 m 52,558 52,558 100.0 8.38 0.4 19.9 3.28

Speed 20 m A SD m/s 20.3 m 52,558 52,558 100.0 0.724 0 4.1 0.378

Speed 20 m A Max m/s 20.3 m 52,558 52,558 100.0 8.13 0.4 20.2 3.22

Speed 20 m B SD m/s 20.3 m 52,558 52,558 100.0 0.719 0 4.1 0.381

Direction 35 m Max ° 35.3 m 52,558 52,558 100.0 311.6 72 429 141.2

Direction 35 m Min ° 35.3 m 52,558 52,558 100.0 72.0 72.0 72.0 0.0

Temperature °C 52,558 52,558 100.0 25.2 16.8 31.0 2.0

Temperature SD °C 52,558 52,558 100.0 0.023 0 1.5 0.069

Temperature Max °C 52,558 52,558 100.0 25.5 17.0 31.4 2.1

Temperature Min °C 52,558 52,558 100.0 25.0 16.7 31.0 2.0

Speed 50 m A TI 52,558 52,558 100.0 0.126 0 1.167 0.103

Speed 50 m B TI 52,558 52,558 100.0 0.129 0 1.167 0.103

Speed 35 m A TI 52,558 52,558 100.0 0.132 0 1.143 0.109

Speed 35 m B TI 52,558 52,558 100.0 0.132 0 1.125 0.105

Speed 20 m A TI 52,558 52,558 100.0 0.144 0 1.25 0.118

Speed 20 m B TI 52,558 52,558 100.0 0.145 0 1.222 0.123

Speed 50 m A WPD W/m2 52,558 52,558 100.0 287 0 2678 282

Speed 50 m B WPD W/m2 52,558 52,558 100.0 277 0 2629 273

Speed 35 m A WPD W/m2 52,558 52,558 100.0 265 0 2442 259

Speed 35 m B WPD W/m2 52,558 52,558 100.0 270 0 2488 266

Speed 20 m A WPD W/m2 52,558 52,558 100.0 234 0 2093 225

Speed 20 m B WPD W/m2 52,558 52,558 100.0 231 0 2012 223
31 

Figure 28. Wind speed data from MCAS Kaneohe Bay airport Met tower 
32

Appendix B – Commissioning Report 

Relevant excerpts from the commissioning report are shown below.

DATE

FIELD INSTALLER CONTACT INFO

SITE# COMPANY Harmer Communications

SITE NAME DATUM CONTACT PERSON Mark Harmer

LATITUDE

LONGITUDE

TIME ZONE PHONE NUMBER 808-877-8082

FAX NUMBER 808-877-8084

EMAIL ADDRESS harmer@flex.com

CREW MEMBERS MS, MH, PT, JP

TOWER MAKE & MODEL UNITS SCF QC'd BY Mark Harmer

TOWER DIAMETER 8-10in

SUMMARY OF SERVICES PERFORMED (use the box below) - entry required

Guidelines

If a new tower has been installed, please explicitly state this.

If a pre-existing tower has been dropped to perform any services on it , please clearly describe the services performed.

If a pre-existing tower has been decommissioned, please explicitly state this. Additionally, please:

1) indicate if the tower has failed requiring wreckage to be cleared

2) indicate storage plans of tower and components (e.g. where, or with whom)

3) indicate whether any tower parts or components have been left on site and why

4) note damaged tower sections or components identified during decommissioning (for a tower which had not failed)

6/17/2009

2

SITE DESCRIPTION

N 21 26.542

W 157 44.279

Hawaii-Aleutian Standard (GMT -10:00)

Simi flat rolling bumps, on sandy beach with 

scrub vegiatation 

NRG 50m XHD w/out tower extension

WGS84

TOWER

310-9 Alamaha Street, Kahului, Hawaii, 

96732 ADDRESS

SITE INFORMATION

Kaneohe Bay

Install new tower with marker balls and bird diverters on guy wires
33

UNITS

342 342 162 162 342 342 162 162 342 342 162 162 degrees 

TRUE

meters

1.53m 1.53m 1.53m 1.53m 1.93 1.93 meters

0.5in 0.5in 0.5in 0.5in 0.75 0.75 inches

8in 8in 8in 8in 8 8 inches

inches

inches

PRIMARY OR REDUNDANT

BOOM ORIENTATION 

twr raised (GOT) / twr lowered

BOOM HEIGHT

LOGGER CHANNEL NUMBER

SENSOR MAKE & MODEL

ACTUAL MONITORING HEIGHT

BOOM MAKE & MODEL

BOOM LENGTH

BOOM DIAMETER

OFFSET HEIGHT

ANEM SERIAL NUMBER

0

2

PASS FUNCTIONAL TEST?

COMMENTS

BASE HEIGHT

BASE PLATE DISPLACEMENT

Yes Yes

East ano

53339

West ano

Yes

NRG 3752

53338

NRG #40C NRG #40C

35.05 m

NRG #40C

50.15 m 35.05 m

49.9

1

NRG #40C

50.15 m

Primary

49.9

Primary

ANEMS - all anem calibration certification sheets must be supplied to UPC

2 3 4 5

NRG 3752

Primary

NRG 3752

Primary

NRG 3752

34.8 34.8

53342

East ano West ano

Yes Yes

53341

NRG 3991

20

53331

20.25 m

NRG #40C

Primary

NRG 3991

53336

20

East ano

Yes

West ano

6

NRG #40C

20.25 m

Primary
34

UNITS

72 72 72 72 degrees 

TRUE

meters

1.53m 1.53m inches

0.5in 0.5in inches

8in 8in inches

meters

PASS FUNCTIONAL TEST?

NRG 3752

BOOM DIAMETER

BOOM MAKE & MODEL

VANES & OTHER SENSORS - any calibration sheets for non-vane sensors must be supplied to UPC

7

Vane Vane Temperature

8

COMMENTS

MOUNTING ORIENTATION

SERIAL NUMBER (if available)

BOOM LENGTH

OFFSET HEIGHT

MOUNTING HEIGHT

50.1

ACTUAL MONITORING HEIGHT

NRG #200P

35.25 m

V

A

N

E

S

50.35 m

BOOM ORIENTATION 

twr raised (GOT) / twr lowered

NRG 3752

35

9

N

NRG #200P NRG #110S

3

Yes Yes Yes

AWAY from tower AWAY from tower

LOGGER CHANNEL NUMBER

SENSOR TYPE

SENSOR MAKE & MODEL

DEADBAND ORIENTATION

BOOM HEIGHT

O

T

H

E

R
35

Appendix C – Met Tower Configuration & Components

Met tower components for MCBH Kaneohe Bay

50m tower:

• 6- NRG #40 anemometers (item 1899)

• 2- #200P wind vanes (item 1904)

• 1- #110S temperature sensor (item 1906)

• 2- 2C cables for 50m level (item 1934)

• 1- 2C cable for 40m level (item 1933)

• 1- 2C cable for 30m level (item 1932)

• 1- 3C cable for 50m level (item 1939)

• 1- 3C cable for 40m level (item 1938)

• 12- Hose clamps for side mount booms (item 2712)

NRG #40 Anemometer (item: 1899) – The NRG #40 Maximum anemometer is the industry standard 

anemometer used worldwide. NRG #40 anemometers have recorded wind speeds of 96 m/s (214 

mph). Their low moment of inertia and unique bearings permit very rapid response to gusts and lulls. 

Because of their output linearity, these sensors are ideal for use with various data retrieval systems. A 

four-pole magnet induces a sine wave voltage into a coil producing an output signal with a frequency 

proportional to wind speed. The #40 is constructed of rugged Lexan cups molded in one piece for 

repeatable performance. A rubber terminal boot is included.

Figure 29. Anemometer #40 (item 1899)

Photo Courtesy of NRG Systems http://www.nrgsystems.com/
36

NRG #200P Wind Direction Vane, 10K (item: 1904) – The NRG #200P wind direction vane is the 

industry standard wind direction vane used worldwide. The thermoplastic and stainless steel 

components resist corrosion and contribute to a high strength-to-weight ratio. The vane is directly 

connected to a precision conductive plastic potentiometer located in the main body. An analog voltage 

output directly proportional to the wind direction is produced when a constant DC excitation voltage is 

applied to the potentiometer. A rubber terminal boot is included.

Figure 30. Wind vane NRG #200P (item 1904)

Photo Courtesy of NRG Systems http://www.nrgsystems.com/

NRG Mounting Booms – NRG side mounting booms allow you to easily mount sensors to your 

tower or mast at any height. Mounting hardware is included. 

Figure 31. Boom, Side, 1.53m (60.5"), Galv, with clamps (item: 3390)

Photo Courtesy of NRG Systems http://www.nrgsystems.com/
37

Figure 32. 50m and XHD Tower configuration3

 3

http://www.nrgsystems.com/FileLibrary/3bd448ea64fa4f38b624e694f9229453/NRG%2060m%20and%2050m%20XHD%

20TallTower%20Installation%20Manual%20-%20Rev%202.03.pdf
38

Figure 33. 60m XHD Tower configuration4

 4

http://www.nrgsystems.com/FileLibrary/3bd448ea64fa4f38b624e694f9229453/NRG%2060m%20and%2050m%20XHD%

20TallTower%20Installation%20Manual%20-%20Rev%202.03.pdf (pg 88)
39

Figure 34. Guy wire degradation due to marine effects at MCBH Kaneohe Bay. 

Source: Mark Harmer, Harmer Communications
40

Appendix D – Wind Turbine Spec Sheets

Southwest WindPower: Skystream 3.7

Source: http://www.windenergy.com/documents/spec_sheets/3-CMLT-1338-01_Skystream_spec.pdf
41

Bergey Wind Power: BCW Excel-S 10 kW

Source: http://www.bergey.com/pages/technical

WindCad Turbine Performance Model

BWC EXCEL-S, Grid - Intertie Tier/neo-SH3055-23-BWC

Prepared For: SWCC

Site Location: Reference

Data Source: AWEA Standard

Date: 3/8/2011

Inputs: Results:

Ave. Wind (m/s) = 5 Hub Average Wind Speed (m/s) = 5.00

Weibull K = 2 Air Density Factor = 0%

Site Altitude (m) = 0 Average Output Power (kW) = 1.51

Wind Shear Exp. = 0.200 Daily Energy Output (kWh) = 36.2

Anem. Height (m) = 30 Annual Energy Output (kWh) = 13,227

Tower Height (m) = 30 Monthly Energy Output = 1,102

Turbulence Factor = 0.0% Percent Operating Time = 67.9%

Weibull Performance Calculations

Wind Speed Bin (m/s) Pow er (kW) Wind Probability (f) Net kW @ V

1 0.00 6.14% 0.000

2 0.00 11.17% 0.000

3 0.14 14.29% 0.020

4 0.43 15.27% 0.066

5 0.88 14.35% 0.126

6 1.51 12.15% 0.183

7 2.35 9.39% 0.221

8 3.43 6.67% 0.229

9 4.80 4.38% 0.210

10 6.42 2.67% 0.171

11 8.21 1.51% 0.124

12 10.02 0.79% 0.080

13 11.37 0.39% 0.044

14 11.76 0.18% 0.021

15 12.06 0.08% 0.009

16 12.14 0.03% 0.004

17 12.15 0.01% 0.001

18 12.10 0.00% 0.000

19 11.92 0.00% 0.000

20 11.44 0.00% 0.000

2008, BWC Totals: 99.47% 1.510

Weibull Calculations:

Wind speed probability is calculated as a 

Weibull curve defined by the average 

wind speed and a shape factor, K. To 

facilitate piece-wise integration, the wind 

speed range is broken down into "bins" of 

1 m/s in width (Column 1). For each wind 

speed bin, instantaneous wind turbine 

power (W, Column 2)) is multiplied by the 

Weibull wind speed probability (f, Column 

3). This cross product (Net W, Column 4) 

is the contribution to average turbine 

power output contributed by wind speeds 

in that bin. The sum of these 

contributions is the average power output 

of the turbine on a continuous, 24 hour, 

basis.

Best results are achieved using annual or 

monthly average wind speeds. Use of 

daily or hourly average speeds is not 

recommended.

Instructions:

Inputs: Use annual or monthly Average Wind speeds. If Weibull K is not known, use K = 2 for inland sites, use 3 for coastal sites, and 

use 4 for island sites and trade wind regimes. Site Altitude is meters above sea level. Wind Shear Exponent is best assumed as 0.18. 

For rough terrain or high turbulence use 0.22. For very smooth terrain or open water use 0.11. Anemometer Heightis for the data used 

for the Average Wind speed. If unknown, use 10 meters. Tower Height is the nominal height of the tower, eg.: 24 meters. Turbulence 

Factoris a derating for turbulence, site variability, and other performance influencing factors -- typical turbulence has already been 

incorporated into the model. Use 0.00 (0%) for level sites with limited obstructions. Use -0.10 ( negative 10%) for flat, clear sites on open 

water. Use 0.05 to 0.15 (5% to 15%) for rolling hills or mountainous terrain. 

Results: Hub Average Wind Speed is corrected for wind shear and used to calculate the Weibull wind speed probability. Air Density 

Factoris the reduction from sea level performance. Average Power Output is the average continuous equivalent output of the turbine. 

Daily Energy Output is the average energy produced per day. Annual and Monthly Energy Outputs are calculated using the Daily 

value. Percent Operating Time is the time the turbine should be producing some power.

Limitations: This model uses a mathmatical idealization of the wind speed probability. The validity of this assumption is reduced as the 

time period under consideration (ie, the wind speed averaging period) is reduced. This model is best used with annual or monthly average 

wind speeds. Use of this model with daily or hourly average wind speed data is not recommended because the wind will not follow a 

Weibull distribution over short periods. The data used in creating the power curve was generated at the BWC test site in Norman, OK. 

Consult Bergey Windpower Co. for special needs. Your performance may vary.

10 kW
42

Endurance Wind Power

Source: http://www.endurancewindpower.com/s343.html
43

Appendix E – Electricity Rates

Average Retail Price of Electricity to Ultimate Customers by End-Use Sector, by State 

 Electric Power M onthly with data for November 2010

 Report Released: February 14, 2011

Table 5.6.A. Average 

R t il P i f 

(Cents per kilowatthour)

Census Division Residential Commercial1 Industrial1 Transportation[1] All Sectors

and State 10-Nov 9-Nov 10-Nov 9-Nov 10-Nov 9-Nov 10-Nov 9-Nov 10-Nov 9-Nov

Pacific 

Noncontiguous 23.69 22.21 21 19.41 20.36 18.89 -- -- 21.66 20.15

Alaska 16.76 16.73 14.54 14.27 14.08 15.14 -- -- 15.22 15.29

Hawaii 28.91 26.23 26.65 23.92 22.56 20.15 -- -- 25.81 23.24

U.S. Total 11.7 11.33R 10.07 9.85R 6.59 6.44R 10.42 11.13R 9.62 9.43R

[1] See Technical notes for additional information on the Commercial, Industrial, and Transportation sectors.

R = Revised

Source: U.S. Energy Information Administration, Form EIA-826, "Monthly Electric Sales and Revenue Report w ith State Distributions Report."

Notes: • See Glossary for definitions. • Values for 2009 are final. Values for 2010 are preliminary estimates based on a cutoff model sample. See 

Technical Notes for a 

discussion of the sample design for the Form EIA-826. • Utilities and energy service providers may classify commercial and industrial customers based 

on either NAICS codes or demands or usage falling w ithin specified limits by rate schedule. • Changes from year to year in consumer counts, sales and 

revenues, particularly involving the commercial and industrial consumer sectors, may result from respondent implementation of changes in the 

definitions of consumers, and reclassifications. • Retail sales and net generation may not correspond exactly for a particular month for a variety of 

reasons (i.e., sales data may include imported electricity). • Net generation is for the calendar month w hile retail sales and associated revenue 

accumulate from bills collected for periods of time (28 to 35 days) that vary dependent upon customer class and consumption occurring in and outside