Objective In order to be able to predict the energy yield available at a potential wind farm site, accurate predictions of the wind regime at that site are required. Improvements in the wind speed and direction predictions will reduce the uncertainty in the available energy yield, which in turn will reduce the financial risks of the wind farm development. Current wind resource prediction methods have been found to contain errors of up to 10% in wind speed and 60 in direction. This translates to 15 - 20% errors in predicted energy yield. This project will design and implement an improved Measure-Correlate-Predict (MCP) algorithm by using neural network techniques. Neural networks are particularly good at extracting patterns from noisy time series data which is exactly the problem facing MCP techniques. The objectives of the project are: 1. design and develop a model a neural network which will result in a 50% improvement in the accuracy of the predicted long term wind speed compared with conventional measure correlate predict techniques; 2. quantify the uncertainties in wind speed and direction predictions; 3. translate the uncertainties in wind climate in to energy yield. Achievement in these objectives should result in the following benefits: - a substantial reduction in the financial risk of investment in wind power projects - improved understanding of the physical parameters connected to wind speed and direction analysis - transfer of neural network knowledge into the wind energy industry The project will construct a comprehensive database of wind measurements. This will be carefully analysed to ensure the optimal neural network approach is used. Upon completion of the neural network algorithm, a user friendly software tool will be developed that provides easy access to highly accurate wind resource predictions. These predictions will be compared against the current state-of-the-art. The effect of the improved accuracy on energy yield will be calculated. Finally the reduction in financial uncertainty due to the new MCP method will be quantified. Fields of science natural sciencescomputer and information sciencessoftwarenatural sciencescomputer and information sciencesdatabasesengineering and technologyenvironmental engineeringenergy and fuelsrenewable energywind powernatural sciencescomputer and information sciencesartificial intelligencecomputational intelligence Programme(s) FP4-NNE-JOULE C - Specific programme for research and technological development, including demonstration in the field of non-nuclear energy, 1994-1998 Topic(s) 03040104 - R&D on alternative sites for wind turbines arrays; R&D to adapt turbines technologies to the new requirements and relevant sitting and meteorological issues Call for proposal Data not available Funding Scheme CSC - Cost-sharing contracts Coordinator RENEWABLE ENERGY SYSTEMS LTD. EU contribution No data Address 23 Grosvenor Road - Beaufort House AL1 3AW ST ALBANS United Kingdom See on map Total cost No data Participants (2) Sort alphabetically Sort by EU Contribution Expand all Collapse all Ecotecnia Sociedad Cooperativa Catalana Ltd. Spain EU contribution No data Address 23 1,Cl. Amistad 08005 Barcelona See on map Total cost No data RISOE NATIONAL LABORATORY Denmark EU contribution No data Address 399,Frederiksborgvej 399 4000 ROSKILDE See on map Total cost No data
