\
PERGAMON
Renewable Energy 07 "0888# 064Ð078
www[elsevier[com:locate:renene
Potential for wind generation on the Guyana
coastlands
Shashi Persauda\\ Damian Flynnb\ Brendan Foxb
a
b
Department of Electrical En`ineerin`\ University of Guyana\ Geor`etown\ Guyana
School of Electrical and Electronic En`ineerin`\ The Queen|s University of Belfast\
Ashby Buildin`\ Stranmillis Road\ Belfast BT8 4AH
Received 2 September 0887^ accepted 11 October 0887
Abstract
Guyana|s dependence upon imported petroleum fuels can only be o}set by the sustained
exploitation of its indigenous resources[ With its populated coastlands exposed to the northeast
trade winds and a history of small!scale wind energy utilisation wind is one such potential
energy source[ In this study\ the coastal wind regime is analysed and historical data from a
coastal weather station are used to estimate the potential for wind generation[ It is found that
a hybrid Weibull probability density function best describes the annual wind speed frequency
distribution at the reference height of 09[56 m[ With an annual mean wind speed of 4[7 m:s\
an energy pattern factor of 0[30\ and an annual average power density of 048 W:m1\ this
distribution represents a class!2 wind resource\ suitable for most wind turbine applications[
Site analysis and observed trends in coastal wind availability suggest the strong likelihood of a
greater wind resource in more open locations[ In view of its apparent potential for wind farm
operation\ a comprehensive\ wind resource assessment programme is recommended for the
Guyana coastlands[ Þ 0888 Elsevier Science Ltd[ All rights reserved[
0[ Introduction
Guyana is heavily dependent upon imported fossil fuels to meet its energy needs[
Petroleum products currently account for nearly half of the total energy needs and
utilise 19) of the income derived from the export of goods and services ð0Ł[ The single
largest consumer of petroleum products is the electricity sector with the lone utility
Corresponding author
9859!0370:88:, ! see front matter Þ 0888 Elsevier Science Ltd[ All rights reserved
PII] S 9 8 5 9 ! 0 3 7 0 " 8 7 # 9 9 6 8 2 ! 8
065
S[ Persaud et al[ : Renewable Ener`y 07 "0888# 064Ð078
accounting for approximately a third of total fuel imports[ With the exception of
biomass fuels used in the sugar industry\ and on a small scale in sections of the rice
and timber industries\ diesel generation is the norm[ This reliance upon imported
fuels makes Guyana|s fragile economy very vulnerable to price ~uctuations in the
international petroleum market[ To cushion the e}ects of inevitable price increases in
the face of a diminishing resource\ greater utilisation of its indigenous resources must
be e}ected[
A potential resource\ that has received little attention\ is wind energy[ Situated on
the north eastern edge of the South American mainland\ the Guyana coastlands are
exposed to the northeast trade winds\ known for their steady wind potential[ With a
history of small scale wind energy utilisation and established electricity networks\ the
coastal potential for wind generation appears promising[ Historical records suggest a
long!term mean annual wind speed of 4[7 m:s at 09 m ð1Ł[ These winds are moderate
by international standards but many wind farms have been designed to operate under
similar conditions[ Wind predictability and its correlation with load demand may also
allow for high penetration and make wind energy an economic supplement to expens!
ive diesel generation[ However\ _rm decisions regarding the exploitation of the coastal
wind resource must be based upon accurate knowledge of the region|s wind regime\
which can only be gained from analyses of detailed long!term wind speed records[
The available data have\ to date\ limited the e}ectiveness of wind assessment
studies[ All previous studies have restricted themselves to qualitative analyses of
meteorological\ topographical and historical information\ citing an insu.cient dat!
abase and emphasising the need for additional data[ Whilst recognising the apparent
coastal wind potential and providing valuable general information on wind patterns\
availability and utilisation\ they have been unable to quantify the local wind potential
and remain generally inconclusive ð2Ð4Ł[ In the absence of new data\ quanti_cation of
the coastal wind!potential remains elusive\ which is unfortunate\ given the devel!
opments in wind energy technology and utilisation[
This paper attempts to classify the coastal wind resource by correlating the results
of qualitative studies with statistical analysis of available records[ The coastal wind
regime is qualitatively analysed and favourable zones identi_ed[ Data from a par!
ticular site are then used to quantify the wind potential in these areas[ It is assumed
that the length of the observational period is su.cient to accurately identify long!
term means and that\ despite its age\ the data are still relevant and representative of
shoreline coastal sites today[
1[ The Guyana wind resource
Guyana is situated on the north eastern edge of the South American mainland
between the approximate latitudes 0Ð8> N of the equator "see Fig[ 0#[ These latitudes
lie within the ranges of two predominant weather zones\ the North East Trade Winds
and the Equatorial Doldrums or Inter Tropical Convergence Zone "ITCZ#[ The
northeast trade winds are steady winds with good all!year energy potential\ whilst the
ITCZ is an equatorial belt of calm winds resulting from the convergence of the
S[ Persaud et al[ : Renewable Ener`y 07 "0888# 064Ð078
066
Fig[ 0[ Windy regions of Guyana "not to scale#] "0# Northwest coast^ "1# Essequibo coast^ "2# West Demerara
coast^ "3# East Demerara:Berbice coast^ "4# Corentyne coast^ "5# Rupununi[
northern and southern trade winds[ The annual northÐsouth movement of the ICTZ
is held responsible for the general variation in weather and wind patterns experienced
over Guyana[ Distribution of the wind resource is further characterised by topogra!
phy[ The hilly and mountainous regions that constitute the bulk of Guyana|s land
mass exhibit little wind potential\ whilst the ~at lands of the Rupununi savannahs
and the coastal plains are relatively windy[
The Rupununi is a large\ undeveloped and sparsely populated region located in the
country|s southwest[ With an approximate population density of two persons per
square kilometre this geographically remote region has limited access to conventional
resources\ such as roads\ fuel\ electricity\ water\ etc[ Only its capital\ Lethem\ has any
provision for electricity*a few hours daily[ Local knowledge suggests that the average
wind potential of the Rupununi is greater than that of the coastlands\ but veri_cation
is impossible at present due to extremely limited data[ Wind power for water pumping
applications in this region is well established\ however\ with many villages receiving
potable water from multi!vaned wind pumps[ A scarcity of investment capital\ high
transportation costs and limited access to spares and supplies are some of the main
factors that have contributed to the limited utilisation of the wind resource for
electricity generation[
067
S[ Persaud et al[ : Renewable Ener`y 07 "0888# 064Ð078
The coastal plain is a narrow strip of arable land\ some three hundred miles
long and three to _ve miles in depth[ It is segmented into _ve distinct regions*
the Northwest\ Essequibo\ West Demerara\ East Demerara:Berbice and Corentyne
coastlands*by four large rivers "Fig[ 0#[ With the exception of the northwest region\
the coastlands are well developed with established infrastructure*electricity\ roads\
water\ etc[\ and are well populated\ with 74) of the country|s population[ Most of
these regions have a history of wind energy utilisation for both water pumping and
small!scale electricity generation\ but such applications have largely been displaced
by established regional electricity and water distribution networks[
The northeast trade winds predominate across the coastlands\ their in~uence being
greatest at the shoreline and reducing signi_cantly as they progress inland[ The e}ects
of the ITCZ\ greatest in the northwest region\ are generally only experienced for a
few months of the year ð3Ł[ The resulting seasonal pattern in wind availability is
characterised by high mean wind speeds during the northern winter and low means
during the summer[ Superimposed upon the seasonal periodicity are diurnal cycles\
characterised by early morning lows and afternoon highs[ These cycles\ attributed to
changes in momentum transfer due to solar radiation\ are most distinctive during the
summer when mean wind speeds are low[ Figures 1 and 2 illustrate the seasonal and
diurnal phenomena[ Knowledge of the coastal wind patterns suggests that the most
promising wind sites occur close to the ocean|s edge\ with the populated central and
eastern coastal regions being most favourable[
Fig[ 1[ Monthly mean wind speeds[ Old Ri~e Range\ Georgetown\ 0857Ð0863[
S[ Persaud et al[ : Renewable Ener`y 07 "0888# 064Ð078
068
Fig[ 2[ Annual and seasonal diurnal cycles[ Old Ri~e Range\ Georgetown\ 0857Ð0863[
1[0[ The wind database
The Guyana Hydrometeorological Service has on record\ wind speed measurements
from nine weather stations scattered across the country[ With the exception of two
coastal stations\ however\ wind speeds were only sampled twice daily and are of
limited utility value[ Hourly measurements were made at the Old Ri~e Range and the
Botanic Gardens stations^ both located in the capital city\ Georgetown[ The Old Ri~e
Range is situated on the Atlantic shoreline\ whilst the Botanic Gardens is located
some two miles inland\ in central Georgetown[ Because of its sheltered location\ wind
speed recordings from the latter site are not considered representative of the coastal
wind resource and\ in the absence of a known correction factor\ cannot be used to
estimate the coastal wind potential[
Data from the Old Ri~e Range station\ located very close to\ and in open view of\
the Atlantic Ocean are\ however\ considered indicative of the wind regimes along the
Demerara and Berbice coastlands[ These were recorded during the period 0857Ð0863\
at an anemometer height of 24 ft "09[56 m#\ using a Dines tube anemometer and a
continuous data recorder[ Hourly mean wind speeds were found by {striking| the
average of the previous hour|s continuous recordings[ From the strip chart records\
monthly averages of hourly mean wind speeds "Fig[ 1#\ monthly and annual means
"Figs 2 and 3\ respectively# were computed[
Wind speed and wind direction frequency distribution statistics were only compiled
for the period 0860Ð0862[ These\ published in the Annual Climatological Summaries
S[ Persaud et al[ : Renewable Ener`y 07 "0888# 064Ð078
079
Fig[ 3[ Variation in annual mean wind speeds[
of the Guyana Hydrometeorological Service ð3Ł\ have been used to compute the
seasonal and annual distributions shown in Table 0[ Approximately 7[8) of the
hourly data for this period were lost due to equipment and other failures[ Seasonal
and annual mean wind speeds and standard deviations\ computed for the longer
period of 0857Ð0863\ are shown in Table 1[ Wind speeds were originally recorded in
knots[
Table 0
Wind speed frequencies[ Old Ri~e Range\ Georgetown\ 0860Ð0862
Wind speed bins
"knots#
"m:s#
JanÐMar
")#
AprÐJun
")#
JulÐSep
")#
OctÐDec
")#
Year
")#
9Ð1
1Ð2
2Ð5
5Ð09
09Ð05
05Ð10
10Ð16
16Ð22
9[99Ð0[92
0[92Ð0[43
0[43Ð2[97
2[97Ð4[97
4[97Ð7[11
7[11Ð09[68
09[68Ð02[77
02[77Ð05[81
1[91
9[96
9[88
8[30
51[43
12[96
0[77
9[91
5[10
9[00
1[76
05[19
42[95
19[32
0[00
9[99
02[63
9[06
7[32
16[75
34[46
2[73
9[24
9[92
7[85
9[96
2[84
14[27
44[28
4[70
9[33
9[99
6[62
9[00
3[95
08[60
43[03
02[18
9[84
9[90
S[ Persaud et al[ : Renewable Ener`y 07 "0888# 064Ð078
070
Table 1
Seasonal and annual mean wind speeds[ Old Ri~e Range\ Georgetown\ 0857Ð0863
Mean wind speed "m:s#
Standard deviation "m:s#
JanÐMar
AprÐJun
JulÐSep
OctÐDec
Year
6[03
9[28
4[88
0[92
3[50
9[57
4[31
9[22
4[68
9[31
2[ Method of data analysis
The three!year seasonal and annual wind speed frequency distribution statistics
presented in Table 0 and the seven!year seasonal and annual mean wind speeds
presented in Table 1 are used to estimate the expected long!term distribution of wind
speeds at the Old Ri~e Range site and to quantify its energy potential[ A probability
model is selected that is representative of the recorded frequency distribution data
and its seasonal and annual distribution parameters and mean wind speeds estimated
using the procedure outlined in section 2[0[ Relationships are then identi_ed\ between
the estimated parameters and mean wind speeds\ and used to predict the distribution
parameters corresponding to the long!term mean wind speeds[ The energy pattern
factors and power densities associated with these distributions are then computed as
shown in section 2[1[
2[0[ Wind speed frequency distribution
The probability models that have evolved as standard in wind resource assessment
studies are the Weibull and Raleigh distribution ð5Ł[ The general form of the Weibull
distribution is
fw"v# "k:v#"v:c#k−0 exp "−"v:c#k#
"0#
where fw"v# is the probability of observing wind speed v\ k is a dimensionless shape
factor\ and c\ referenced in the units of wind speed\ is the scale factor[ The Raleigh
distribution is a special case of the Weibull distribution\ with k 1[ The cumulative
Weibull distribution is given by
Fw"v# exp "−"v:c#k#
"1#
A number of methods are available for estimating the factors\ c and k\ depending
upon the available data[ Where sampled wind speed frequency distribution statistics
are available\ the {least!squares| method is preferred ð5Ł[ Here it is assumed that
the recorded cumulative frequencies are noise!corrupted samples of the cumulative
Weibull function\ such that
F"v# Fw"v#¦r"v#
"2#
where F"v# is the recorded cumulative frequency corresponding to the wind speed v
and r"v# is the associated noise residual[ Determination of the parameters of the
071
S[ Persaud et al[ : Renewable Ener`y 07 "0888# 064Ð078
Weibull distribution requires a good _t of eqn "1# to the recorded discrete cumulative
frequency distribution[ Equation "1# is linearised by taking the natural logarithm of
both sides twice\ to give
ln "− ln "F"v### k ln "v#−k" ln "c##
"3#
and k and k" ln "c## are subsequently determined as the least squares solution to the
resulting over!determined system of eqn "4#[ A common approach is to _t a least
squares straight line to a scatter plot of ln "−ln "F"v### vs ln "v# and determine its
slope*k and intercept with the ln "v# axis−k ln "c#[
K ln "− ln "F"v0### L K ln "v0#−0 L
H H
H
H
k
H ln "− ln "F"v1### H H ln "v1#−0 H
H
H
H
H
*
*
k" ln "c##
H H
H
H
kln "− ln "F"vn−0###l kln "vn−0#−0l
$
%
"4#
With c and k determined\ the likelihood of wind speed occurrences within a given
interval "v0 ³ v ³ v1# can be estimated as
Fw"v0 ³ v ³ v1# Fw"v0#−Fw"v1#
"5#
and the probabilities of wind speed occurrences determined from eqn "0#[ The mean
vmw and standard deviation sw of the Weibull distribution can then be computed from
ð6Ł
vmw cG"0¦0:k#
"6#
sw z"c1ðG"0¦1:k#−G1"0¦0:k#Ł#
"7#
and
where G" # is the Gamma function[
The main limitation of the Weibull model is that it does not accurately represent
the probabilities of observing zero and very low wind speeds\ given that fw"9# 9[
This limitation can be overcome\ however\ through the use of a hybrid model of the
form
Fh"v# F"9#¦ð0−F"9#ŁFw"v#
"8#
where F"9# is the cumulative probability of observing very low wind speeds[
The distribution of very low wind speeds can further be modelled separately[
Reference ð6Ł suggests the use of the Dirac Delta function[ For the purposes of
estimating wind potential\ however\ this is usually unnecessary\ as the energies avail!
able at low wind speeds are negligible and outside the operating range of wind
turbines[
From eqn "8#\ the cumulative frequencies needed for the determination of the
Weibull parameters can be calculated as]
S[ Persaud et al[ : Renewable Ener`y 07 "0888# 064Ð078
Fw"v# ðFh"v#−F"9#Ł:ð0−F"9#Ł
072
"09#
and the mean of the hybrid distribution as
vm F"9# vm9¦ð0−F"9#Ł vmw
"00#
where vm9 and vmw are the means of the low wind speed and Weibull distributions
respectively[
2[1[ Ener`y availability
A key indicator to the size of an available wind resource is its annual average power
density[ Computation of this factor at a reference height of 09 m or 49 m allows for
a general classi_cation of the available resource and the subsequent estimation of its
potential for wind energy applications ð3Ł[ The wind power density\ PD\ associated
with a wind of velocity v m:s and density r kg:m2 is ð5Ł
PD 9[4rv2 W:m1[
"01#
The annual average power density may then be estimated as
PD"avg# 9[4rÐv2f "v# dv 9[4rKev2m W:m1
"02#
where Ke is the annual energy pattern factor that accounts for all variations in wind
strength over the year[ At sea level and assuming an average temperature of 16>C\
the density of air is 0[066 kg:m2 and eqn "02# becomes
PD"avg# 9[4774 Kev2m W:m1
"03#
The energy pattern factor is computed as
Ke energy available in the wind per year
energy available at mean wind speed
g
9
v2f "v# dv
v2m
"04#
where f "v# is the known wind speed probability density function at the site[ Sub!
stituting fw"v#\ from eqn "0#\ for f "v# into eqn "04# gives
Ke "k:ck−0#
v2m
g
vk¦0 exp "−"v:c#k# dv[
"05#
9
Recognising the de_nite integral ð8Ł
g
vx−0 exp "−uvp# dv "0:=p=#u−"x:p#G"x:p#
"06#
9
allows for the simpli_cation
Ke c2G"0¦1:k#
v2m
"07#
S[ Persaud et al[ : Renewable Ener`y 07 "0888# 064Ð078
073
In the case of the hybrid Weibull distribution\ the energy pattern factor can be
approximated as
Ke "0−F9#c2G"0−1:k#
v2m
"08#
From eqn "03# the annual energy availability is determined as
E 7659PD"avg# Wh:m1:yr
"19#
3[ Results
The adoption of irregular wind speed bins in the compilation of wind speed fre!
quency distributions by the Guyana Hydrometeorological Service\ prevents an accu!
rate representation of the data by way of frequency histograms[ To overcome this
problem\ approximate distributions were created\ by regrouping frequencies into
uniform 0[92 m:s "1!knot# intervals[ The frequency histogram corresponding to the
annual distribution presented in Table 0 is shown in Fig[ 4[ Examination of Fig[ 4
and the seasonal histograms reveals an approximately Weibull distribution except for
the signi_cant occurrences of wind speeds in the 9Ð0[92 m:s bin[ A hybrid Weibull
probability density function was selected to represent the continuous wind speed
Fig[ 4[ Hybrid and approximate annual frequency distributions "0860Ð0862#[
S[ Persaud et al[ : Renewable Ener`y 07 "0888# 064Ð078
074
frequency distributions recorded in Table 0\ as it accounted for both the high and
near zero frequencies observed in the 9Ð0[92 m:s and 0[92Ð0[43 bins\ respectively[
In the determination of the Weibull factors in the hybrid function\ frequencies in
the 9Ð0[92 m:s bin were removed from the recorded distributions\ the remaining data
scaled to represent 099) of useful data\ and the factors estimated using the procedure
outlined in section 2[0[ For simplicity\ wind speed occurrences within the 9Ð0[92 m:s
interval have not been modelled separately[ It was found that the best _t of the
cumulative hybrid distribution to the recorded data occurred when {outliers| were
ignored in the Weibull parameter estimation process[ Figure 5 illustrates the goodness!
of!_t for the case of the annual distribution[ The results for the seasonal and annual
distributions are presented in Table 2[
Fig[ 5[ Hybrid and recorded annual cumulative frequency distributions "0860Ð0862#[
Table 2
Three!year "0860Ð0862# seasonal and annual distribution parameters at 09[56 m
F9")#
c"m:s#
k
vm"m:s#
JanÐMar
AprÐJun
JulÐSep
OctÐDec
Year
1[91
6[85
3[68
6[05
5[10
6[37
2[82
5[23
02[63
5[05
2[12
3[72
7[85
5[69
2[77
4[45
6[62
6[97
2[69
4[83
S[ Persaud et al[ : Renewable Ener`y 07 "0888# 064Ð078
075
Fig[ 6[ Distribution parameters vs mean wind speed[
Examination of the results\ presented in Table 2\ indicates that the distribution
parameters vary with mean wind speed[ The scatter plots of Fig[ 6 further illustrate
the nature of these relationships\ which have been identi_ed as
c"vm# 9[687vm¦1[147
"10#
k"vm# 9[204vm¦9[143
"11#
F9"vm# exp ð−"vm:2[15#0[53Ł
"12#
F9 was non!linearly modelled as it must satisfy the conditions F9"9# 0 and F9"# 9[
From the relationships identi_ed and the long!term mean wind speeds presented in
Table 0\ seasonal and annual long!term distribution parameters have been estimated[
These are shown in Table 3 along with the corresponding values of energy pattern
factor and average power density[ The seasonal and annual distributions of wind
speeds are shown in Fig[ 7[
4[ Discussion
The foregoing analysis has revealed that a hybrid Weibull distribution best charac!
terises the distribution of wind speeds at 09[56 m[ With an expected annual mean
wind speed of 4[68 m:s at 09[56 m and a corresponding annual average power
S[ Persaud et al[ : Renewable Ener`y 07 "0888# 064Ð078
076
Table 3
Estimated seasonal and annual longÐterm distribution parameters at 09[56 m
vm "m:s#
F9 ")#
c "m:s#
k
Ke
PD"avg# "W:m1#
Wind power class
JanÐMar
AprÐJun
JulÐSep
OctÐDec
Year
6[03
1[56
6[85
3[51
0[11
146[6
4
4[88
5[50
6[93
2[81
0[26
061[0
2
3[50
06[04
4[83
2[96
0[53
82[1
0
4[31
09[95
5[47
2[46
0[36
024[7
1
4[68
6[60
5[77
2[79
0[30
047[5
2
Fig[ 7[ Estimated seasonal and annual frequency polygons "0857Ð0863#[
density of 048 W:m1\ these winds correspond to a class!2 wind resource at 09 m
"049 ³ PD"W:m1# ³ 199# when measured on the Bagattelle wind power scale ð5Ł[ This
available resource exhibits signi_cant seasonal variation and ranges from a class!4
"149 ³ PD"W:m1# ³ 299# resource during winter to a class!0 "9 ³ PD"W:m1# ³ 099#
resource in the summer[ As a consequence\ energy availability during the period\
JulyÐSeptember\ will be minimal "approx[ 03) of annual available energy# providing
a convenient period for wind turbine maintenance[
A class!2 resource is generally considered suitable for most wind turbine appli!
S[ Persaud et al[ : Renewable Ener`y 07 "0888# 064Ð078
077
cations but for economic operations of large wind turbines a greater resource at its
operating hub height is required ð5Ł[ In the absence of measured data\ the available
resource at an extended height can be estimated from the Hellman|s wind shear
formula
v v9"h:h9#a
"13#
where v9 is the known velocity at the reference height h9\ v is the desired value at the
new height h and a is Hellman|s exponent\ usually 9[032 for ~at coastal regions[
On the Guyana coastlands very little is known about the distribution of wind speeds
with regard to height and extrapolation of mean wind speeds cannot be performed
with con_dence[ Two factors suggest\ however\ a larger value for the Hellman|s
exponent at the Old Ri~e Range site than the general coastal _gure of 9[032[ Firstly\
the Guyana coastlands are approximately 0[4 m below sea level and separated from
the Atlantic Ocean by sea defence structures that project another metre[ With the Old
Ri~e Range station situated only a very short distance from the ocean\ it is likely to
have been partially shielded from the predominant northeasterly trade winds\ blowing
inland from the Atlantic[ Secondly\ this site is located on the outskirts of the city of
Georgetown\ whose pro_le must serve to restrict wind ~ow at low altitudes[
The foregoing discussion suggests that higher wind speeds are likely at shoreline
locations backed by ~at open spaces\ and that the wind resource may exceed a
minimum class!2 rating at the hub height of a large wind turbine[ A similar phenom!
enon has been observed in Ref[ ð7Ł[ Such an eventuality should make wind farm
generation an economically viable proposition\ especially when compared to the
expensive diesel generation that currently prevails[ The fact that diurnal and seasonal
patterns in wind availability correlate strongly with load demands and are very
predictable\ will also serve to enhance wind penetration levels and improve economy[
5[ Conclusions
The coastal potential for wind generation has been qualitatively assessed and
quanti_ed using the available historical records[ This investigation has revealed that
the most promising wind sites are located along the Atlantic shoreline in the regions
of the Essequibo\ Demerara:Berbice and Corentyne coasts[ At such locations\ a
minimum class!2 wind resource can be expected at 09 m[ This establishes the potential
for small!scale wind generation and water pumping applications\ but such utilisation
of the wind resource is likely to remain minimal\ due to the presence of electricity and
water networks[
Wind farm generation\ on the other hand\ can contribute signi_cantly to national
energy needs by feeding directly into the existing electricity networks[ Such a scenario
would only be possible\ however\ if the coastal wind regimes were strong enough to
support and make wind farm operation viable[ Extrapolation of the available data
suggests that this is the case\ but such analysis is questionable and the results cannot
be used with con_dence[ Given the likely bene_ts that could result from economic
S[ Persaud et al[ : Renewable Ener`y 07 "0888# 064Ð078
078
wind farm operation it is important that the potential for such operation be accurately
established[ A comprehensive wind resource assessment programme is therefore rec!
ommended for the Guyana coastlands\ the results of which can be made available to
potential interest groups[
Acknowledgements
The authors wish to thank the Guyana Hydrometeorological Service for infor!
mation provided[ S[ Persaud thanks the Commonwealth Scholarship Commission for
funding[
References
ð0Ł Government of Guyana[ National development strategy[ Part V\ 0886[
ð1Ł Guyana Hydrometeorological Service[ Annual climatological summary Ministry of Works and Com!
munication[ Guyana\ 0860Ð0862[
ð2Ł Raghunandan S[ Solar and wind energy potential in Guyana[ A paper presented at the training
workshop on meteorological data for solar and wind energy applications[ Barbados\ December 0873[
ð3Ł Lamming SD[ Report on a fact _nding mission to Guyana[ Consultancy Services Wind Energy
Developing Countries\ October 0878[
ð4Ł Liu N[ Requirements for the development of wind energy in Guyana[ Guyana National Resources
Agency\ 0877[
ð5Ł Frost W\ Aspliden C[ Characteristics of the wind[ In] Wind turbine technology[ ASME Press\ 0883[ p[
260Ð289[
ð6Ł Eastern Caribbean wind resource assessment project[ Regional summary[ Caribbean Development
Bank\ vol[ 0\ 0883[
ð7Ł Hossain A[ A preliminary account of wind speed data in the coastal regions of Bangladesh[ Wind
Energy Conversion\ 0885[ p[ 284Ð8[
ð8Ł Gradshteyn IS\ Ryzhik IM[ Table of integrals\ series and products[ 4th ed[ Academic Press\ 0883[ p[
275[