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(10)
Complex Terrain
Linear Regression
Linear Regression Model
Performance Metric
Regression Model
Standard Deviation
Wind Power
Wind Speed
Wind Turbine
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Comparison of the performance of four measure–correlate–predict algorithms
Comparison of the performance of four measure–correlate–predict algorithms,10.1016/j.jweia.2004.12.002,Journal of Wind Engineering and Industrial Aero
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Comparison of the performance of four measure–correlate–predict algorithms
(
Citations: 20
)
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Anthony L. Rogers
,
John W. Rogers
,
James F. Manwell
Measure–correlate–predict (MCP) algorithms are used to predict the wind resource at target sites for
wind power
development. This paper describes some of the MCP approaches found in the literature and then compares the performance of four of them, using a common set of data from a variety of sites (complex terrain, coastal, offshore). The algorithms that are compared include a
linear regression
model, a model using distributions of ratios of the wind speeds at the two sites, a vector regression method, and a method based on the ratio of the standard deviations of the two data sets. The MCP algorithms are compared using a set of performance metrics that are consistent with the ultimate goals of the MCP process. The six different metrics characterize the estimation of (1) the correct mean wind speed, (2) the correct
wind speed
distribution, (3) the correct
annual energy production
at the target site, assuming a sample
wind turbine
power curve, and (4) the correct wind direction distribution. The results indicate that the method using the ratio of the standard deviations of the two data sets and the model that uses the distribution of ratios of the wind speeds at the two sites work the best. The
linear regression model
and the vector
regression model
give biased estimates of a number of the metrics, due to the characteristics of linear regression.
Journal:
Journal of Wind Engineering and Industrial Aerodynamics  J WIND ENG IND AERODYN
, vol. 93, no. 3, pp. 243264, 2005
DOI:
10.1016/j.jweia.2004.12.002
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Citation Context
(7)
...These stations are frequently used to generate longterm wind roses when a shortterm measurement session has been done using MeasureCorrelatePredict (MCP) methods [1][2][
3
]...
A. Aguera
,
et al.
Categorization of minimum error forecasting zones using a geostatistic...
...These stations are frequently used to generate longterm wind roses when a shortterm measurement session has been done using MeasureCorrelatePredict (MCP) methods [1][2][
3
]...
A. Aguera
,
et al.
Categorization of minimum error forecasting zones using a geostatistic...
...As is well know in the wind power community [2], [
8
] it is necessary a longterm period data, at least 9 months, to have an accurate estimation of the wind speed when you correlate between two meteorological towers installed in different sites...
J. Beltrán
,
et al.
Detection of Nacelle Anemometers Faults in a Wind Farm
...A variety of MCP algorithms or models have been proposed in the literature [
117
] for modeling the relationship between the concurrent wind data at the two sites...
...The authors [
17
] investigated four of these and the methods of Mortimer [10] and one proposed by the authors, the “Variance” method, were shown to reliably predict a variety of site characteristics important to wind power applications...
Anthony L. Rogers
,
et al.
Uncertainties in Results of MeasureCorrelatePredict Analyses
...e.g. Bunn and Watson, (1996, [42]); Rogers, et al., (2005, [
43
]) . It assumes a linear relationship between wind speed at paired sites where one site with a longterm record acts as predictor and the wind speed at shortterm measurement sites as the predictand...
M. T. Pontes
,
et al.
Integrating Offshore Wind and Wave Resource Assessment
References
(4)
Development of the Measurecorrelatepredict strategy for site assessment
(
Citations: 8
)
A. Derrick
Published in 1993.
A comparison of physical and statistical methods for estimating the wind resource at a site
(
Citations: 8
)
L. Landberg
,
N. G. Mortensen
Published in 1993.
A new matrix method of predicting longterm wind roses with MCP
(
Citations: 13
)
J. C. Woods
,
S. J. Watson
Journal:
Journal of Wind Engineering and Industrial Aerodynamics  J WIND ENG IND AERODYN
, vol. 66, no. 2, pp. 8594, 1997
Applied Regression Analysis
(
Citations: 5633
)
Nathan Jaspen
,
Norman Draper
,
Harry Smith
Journal:
Mathematics of Computation  Math. Comput.
, vol. 22, no. 103, 1968
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Citations
(20)
Assessment of hepatic inflammation after spinal cord injury using intravital microscopy
(
Citations: 1
)
H. Hundt
,
J. C. Fleming
,
J. T. Phillips
,
A. Lawendy
,
K. R. Gurr
,
S. I. Bailey
,
D. Sanders
,
R. Bihari
,
D. Gray
,
N. Parry
,
C. S. Bailey
,
A. Badhwar
Journal:
Injuryinternational Journal of The Care of The Injured  INJURYINT J CARE INJURED
, vol. 42, no. 7, pp. 691696, 2011
Uncertainty analysis of wind energy potential assessment
(
Citations: 12
)
SoonDuck Kwon
Journal:
Applied Energy  APPL ENERG
, vol. 87, no. 3, pp. 856865, 2010
Categorization of minimum error forecasting zones using a geostatistic wind speed model
A. Aguera
,
J. G. Ramiro
,
J. Melgar
,
J. C. Palomares
,
A. Moreno
Conference:
International Conference on Clean Electrical Power  ICCEP
, 2009
Categorization of minimum error forecasting zones using a geostatistic wind model
A. Aguera
,
J. J. G. de la Rosa
,
J. Melgar
,
J. C. Palomares
,
A. Moreno
Conference:
Compatibility in Power Electronics  CPE
, 2009
Detection of Nacelle Anemometers Faults in a Wind Farm
J. Beltrán
,
A. Llombart
,
Juan Jose Guerrero Castillo (Juan José Guerrero Castillo)
Published in 2009.