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Accurate Long Term Wind Resource Assessment
through Multivariate Analysis
Dario Patanè, Mario Benso, Carlos Hernández,
Fernando de la Blanca, Cristóbal López
Ereda
Paseo del Marqués de Monistról 11, Madrid, Spain
[email protected], [email protected], [email protected]
[email protected], [email protected]
ABSTRACT
Estimating the long term variability of wind data is a key factor in the precision for wind
resource assessment. The standard approach, i.e. the Measure-Correlate-Predict method (MCP),
consists in extrapolating from a historical wind speed time series, a long term series for the target
mast located at the site under evaluation.
Data from masts with long measurement periods and numerical weather models data provide
a large amount of potential inputs for MCP. In order to process all available data, a novel
Multivariate MCP (MMCP) methodology was developed. This technique takes efficiently into
account all inputs and extracts the maximum amount of information relevant for the local wind
climate of the target mast.
The multivariate MCP was tested against the standard MCP using a database of 13 long term
masts located in complex terrains in different regions of Spain and a substantial improvement in the
long term wind resource assessment was found.
Keywords: wind resource assessment, long term analysis
1. Introduction
Fluctuations of wind resource are one of the major sources of uncertainty on the energy
production estimate of a potential wind park, thus strongly influencing its financial risk [1]. To deal
with this issue, it is customary to resort to MCP analysis [2, 3]. It consists first in identifying among
the available long term reference wind data the series with the highest correlation with the target
one in the concurrent period. Second, the relationship between the target and reference data is
modeled by means of a single variable regression and eventually a long term series for the target
site is extrapolated. Usually a linear regression is employed to fit the data binned into wind
direction sectors. A variety of MCP algorithms have been proposed in the literature [4-11] to
improve the linear MCP using nonlinear techniques or exploring the joint probability distribution of
data.
Usually long term data from masts located close to the target site or weather numerical
models data, such as NCAR/NCEP reanalysis data are considered [12]. In the latter case several
wind and other variables time series relative to a grid of geographical coordinates and height levels
are available. Every variable, even those not highly correlated to the target mast time series, might
provide valuable pieces of information about the target local wind climate that are usually discarded
in the standard MCP analysis. In order to exploit all available data, a multivariate generalization of
the MCP approach was developed. In the following section we describe the novel MMCP
methodology. In order to compare the latter with the standard technique, long term data of a set of
meteorological masts located in Spain were studied (Section 3). The goodness of the MMCP against
the standard MCP is eventually tested using a set of statistical parameters in Section 4.
2. Method
The MMCP methodology consists first in identifying the available long term masts and the
numerical weather model nodes closest to the target mast. The selected variables may be highly
correlated among themselves and some of them may provide no information about the target local
wind climate. In order to take into account these issues, the MMCP is built as a two steps process
(see Fig. 1). First the input variables are analyzed by means of a feature selection algorithm. In this
step variables bringing little or redundant information about the target local wind climate are
discarded. Second, a multivariate regression similar to principal component analysis is performed,
based on the cross-correlation matrix of the selected variables and the target wind time series [13].
As a result a long term wind speed time series at the target site is extrapolated.
Fig.1 Schematic structure of the MMCP method.
3. Validation
In order to compare MCP and MMCP, a set of meteorological masts located in different
regions of Spain with long measurement periods were studied [14]. A higher number of masts were
initially taken into account, but after being scrutinized to reject anomalous data such as gross trends
or step changes (see Fig. 2) a final number of 13 was selected. In particular, masts close to obstacles
(such as buildings or trees) were discarded. The final set of masts considered is shown in Fig. 3 and
their characteristics are shown in Table 1.
Fig. 2 Anomalous trend of wind velocity V and Weibull shape parameter K for a rejected mast. The
running averages shown in the plot are rescaled with respect to their mean values.
Fig. 3 Locations of the selected masts
Table 1. Details of sites used in the analysis.
Id
Mast
1 Gorramendi
2
Aralar
3
Trinidad
4
Carrascal
5
Tafalla
6 Carcastillo
7
Bardenas
8 LomaNegra
9
Corrubedo
10 CIS Ferrol
11
Muralla
#
UTMAvailability
Available
UTM-y Region
x
%
Years
626330 4785520 Nav
85
16
584586 4756273 Nav
76
16
583286 4740665 Nav
86
16
609766 4726608 Nav
93
16
608897 4708597 Nav
87
16
626578 4692352 Nav
92
16
617067 4673673 Nav
93
16
634369 4658997 Nav
91
16
497771 471638
Gal
97
9.7
560578 4815885 Gal
99
9.7
518427 4732806 Gal
93
8.5
Mean
Speed m/s
7.97
7.32
7.22
6.4
3.26
2.93
5.45
7.27
4.18
3.1
7.05
12
13
Ancares 669916 4743224
Marroxo 623277 4703623
Gal
Gal
94
99
8.8
9.7
4.92
2.34
For each valid mast, NCAR/NCEP reanalysis data relative to the closest geographical nodes
were considered as historical data set. For the MCP technique the reanalysis wind speed time series
with the highest correlation with the mast was considered as input. On the other hand, for the
MMCP analysis, all time series relative to different height levels and geographical coordinates were
used as inputs. Mast data relative to a short time window (a ‘reference period’) were used to
construct a relationship between the mast and the NCAR data. This relationship was then applied to
the long term NCAR data to extrapolate a mast wind speed time series for the entire measurement
period. The estimated series was then compared with the real one by evaluating the parameters
described below. The process was repeated sliding the time window along the measurement period
and averaging the results.
4. Results
In order to compare the goodness of the MMCP regression with respect to the MCP one, the
following parameters were calculated: the Root Mean Squared Error (RMSE) of the daily mean
wind speed and the coefficient of determination R2 between the measured and extrapolated time
series. Moreover, the wind speed distribution obtained from the extrapolated time series was
analyzed by means of the RMSE of the shape factor K of the Weibull distribution. Finally, the
RMSE of the mean wind speed (<V>) with respect to the entire measurement period was addressed,
too.
The results obtained for a meteorological mast by varying the length of the reference period
are shown in Fig 4. Results relative to the entire set of analyzed masts for a reference period of one
year are summarized in Fig. 5. A substantial improvement of the MMCP results with respect to the
standard MCP was found for all parameters considered, especially for the shape factor estimation.
Fig. 4 Results obtained for mast #1. RMSEs of daily wind speed and Weibull K parameter (upper
panel) and long term wind speed (lower panel) as a function of the reference period length used for
the regression. RMSEs are relative to the corresponding long term mean values. Also the correlation
between extrapolated and measured long term time series is shown.
Fig. 5 Results obtained averaging all one year reference periods for the masts considered.
5. Conclusions
Summarizing, a novel multivariate approach to long term resource assessment was proposed.
The MMCP regression (as indicated by RMSE_V and R2) was found on average to be about 16%
more accurate with respect to the standard MCP; this led to an increase of the quality of the
estimation of the long term wind resource about 19% better than the MCP one (RMSE_<V>).
Moreover, a remarkable improvement of the estimation of wind speed distribution (about 51%
RMSE_K) was found.
MMCP can be exploited also within measurement campaigns in complex terrains. In this
case, in fact, the measurement period of an additional mast can be extended extrapolating data from
close masts. Moreover the multivariate approach proposed could be a valuable tool to perform
statistical downscaling of weather numerical model wind forecasts to wind park sites.
MMCP
reveals to be an effective approach in the improvement of precision of long term estimation of wind
resource and hence a tool to reduce the wind resource uncertainty and reduce the risk of financing
wind farms.
REFERENCES
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[11]
Rogers AL, Rogers JW, Manwell JF, Comparison of the Performance of Four MeasureCorrelate-Predict Algorithms, JWEIA 2005; 93, 3, 243-264.
[12]
The NCEP/NCAR Reanalysis Project, NOAA/ESRL Physical Sciences Division,
http://www.cdc.noaa.gov/cdc/reanalysis/reanalysis.shtml
[13]
Izenman A J, Modern Multivariate Statistical Techniques: Regression, Classification, and
Manifold Learning, Springer Texts in Statistics, 2008.
[14]
Daily wind speed and direction time series of the analyzed masts were provided by the
Consellería de Medio Ambiente, Xunta de Galicia, and the Departamento de Desarrollo Rural,
Gobierno de Navarra.

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