Bass, J. H., Rebbeck, M., Landberg, L. , Cabre, M. and Hunter, Andrew (2000) An improved measure-correlate-predict algorithm for the prediction of the long term wind climate in regions of complex environment. Project Report. European Commission.
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| Item Type: | Paper or Report (Project Report) |
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| Item Status: | Live Archive |
Abstract
This final report contains a complete description of the work undertaken in fulfilment of contract number JOR3-CT98-0295.
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.
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.
| Additional Information: | This final report contains a complete description of the work undertaken in fulfilment of contract number JOR3-CT98-0295. 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. 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. |
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| Keywords: | Neural networks, Meausre correlate predict, long term wind climate, complex environment |
| Subjects: | G Mathematical and Computer Sciences > G400 Computer Science |
| Divisions: | College of Science > School of Computer Science |
| ID Code: | 3389 |
| Deposited On: | 26 Sep 2010 16:03 |
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