A case based reasoning data fusion scheme: application to offshore wind energy resource mapping

A data fusion scheme is proposed for wind energy resource mapping at high spatial resolution. The resource assessment is based on wind speed and direction measurements. Remotely sensed data is a solution to get wind observations offshore. However, high spatial resolution data do not have a sufficient repetitiveness to establish reliable wind energy resource maps. The scheme proposed in this paper uses these measurements as typical situations which have to be merged with low spatial resolution data having a sufficient temporal repetitiveness. The fusion process builds a library of typical cases. To these typical cases are associated typical fields representing the information to be merged with the corresponding low spatial resolution data. In this paper, we give, firstly, the general fusion scheme. Then, we present the different tools needed by this process. We focus particularly on the definition of the typical situations. The retrieval of these situations is achieved by a classification process. Finally, some prospects are given

[1]  Y. Quilfen,et al.  A High Precision Wind Algorithm For The Ers1 Scatterometer And Its Validation , 1991, [Proceedings] IGARSS'91 Remote Sensing: Global Monitoring for Earth Management.

[2]  Hartmut Kapitza,et al.  Statistical-dynamical downscaling of wind climatologies , 1997 .

[3]  R. Barthelmie,et al.  Can Satellite Sampling of Offshore Wind Speeds Realistically Represent Wind Speed Distributions? Part II: Quantifying Uncertainties Associated with Distribution Fitting Methods , 2004 .

[4]  Richard K. Moore,et al.  Seasat—A 25-year legacy of success , 2005 .

[5]  R. Vautard,et al.  Weather Regimes: Recurrence and Quasi Stationarity , 1995 .

[6]  Nicolas Fichaux,et al.  Evaluation du potentiel eolien offshore et imagerie satellitale , 2003 .

[7]  Stéphane Houzelle,et al.  Contribution to multisensor fusion formalization , 1994, Robotics Auton. Syst..

[8]  W John,et al.  Backscattering from Capillary Waves with Application to Sea Clutter , 1966 .

[9]  R. Barthelmie,et al.  Can Satellite Sampling of Offshore Wind Speeds Realistically Represent Wind Speed Distributions , 2003 .

[10]  Rudolf O. Weber,et al.  Automated Classification Scheme for Wind Fields , 1995 .

[11]  Climatology of Regional Flow Patterns around Basel , 1998 .

[12]  Hans Hersbach,et al.  395 CMOD 5 An improved geophysical model function for ERS C-band scatterometry , 2003 .

[13]  N. Mortensen,et al.  The numerical wind atlas - the KAMM/WAsP method , 2001 .

[14]  Ramond Eeli Aa Ndrenelaprise,et al.  Forecasting Skill Limits of Nested, Limited-Area Models: A Perfect-Model Approach , 2002 .

[15]  Robert A. Shuchman,et al.  Wind vector retrieval using ERS-1 synthetic aperture radar imagery , 1996, IEEE Trans. Geosci. Remote. Sens..

[16]  Erik Lundtang Petersen,et al.  The European Wind Atlas , 1985 .