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Delegates are invited to meet and discuss with the poster presenters in this topic directly after the session 'Wind speed predictions: Are we at the limit of our knowledge or can we improve?' taking place on Wednesday, 12 March 2014 at 11:15-12:45. The meet-the-authors will take place in the poster area.

Dario Patane EREDA, Spain
Co-authors:
Dario Patane (1) F P Mario Benso (1) Cristobal Lopez (1) Fernando De La Blanca (1)
(1) EREDA, madrid, Spain

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Abstract

Accurate long term wind resource assessment using multivariate analysis

Introduction

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 -one- 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 provide a large amount of potential inputs for MCP. In the case of using only one time series, additional information from other sources is usually discarded.

Approach

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.

Main body of abstract

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. First the input variables are analyzed by means of a feature selection algorithm. In this step inputs bringing little or redundant information about the target local wind climate are discarded. Second, a multivariate regression is performed, based on the cross-correlation matrix of the selected variables.
A comparison of the accuracy of the MMCP vs the MCP was carried out, paying special attention not only to the long term mean wind speed estimation but also to the variability of the wind resource. A substantial improvement of the long term analysis was found for the analysed 13 masts dataset.


Conclusion

The MMCP long term wind resource assessment was found to be on average about 20% more accurate than the standard MCP one. Moreover an improvement of about 50% was obtained for the estimation of the wind speed distribution.
The proposed multivariate approach reveals to be effective in improving the precision of long term estimation of wind resource by efficiently processing a large amount of information discarded in the standard analysis.


Learning objectives
The aim of this presentation is to show why and how using all available information, the long term wind resource assessment can be substantially improved.