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An improved measure-correlate-predict algorithm for the prediction of the long term wind climate in regions of complex environment

By J. H. Bass, M. Rebbeck, L. Landberg, M. Cabre and Andrew Hunter

Abstract

This final report contains a complete description of the work undertaken in fulfilment of contract number JOR3-CT98-0295.\ud \ud The Project comparises three partners (Renewable Energy Systems Limited, Riso National Laboratory and Ecotecnia) and one sub-contractor (University of Sunderland). Renewable Energy Systems Ltd is the Project Co-ordinator.\ud \ud Teh aim of the project is to investigate the application of neural network techniques to the assessment of wind climate at potential wind farm sites. The current state-of-the-art for such assessment involves 'Measure-Correlate-Predict' (MCP) methods, which are statistical in nature. One of the limiations of MCP methods is that in regions of complex terrain or complex climatology, large prediction errors can result. It is anticipated that he use of neural networks, which are very good at identifying patterns in noisy data, should significantly improve predictions of the wind climatology of a site, so reducing uncertainty in the available energy yield and, in turn, the risk of financial investment in a wind farm

Topics: G400 Computer Science
Publisher: European Commission
Year: 2000
OAI identifier: oai:eprints.lincoln.ac.uk:3389
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