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 (EuroSciVoc)
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: The European Science Vocabulary.
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: The European Science Vocabulary.
- natural sciences computer and information sciences software
- natural sciences computer and information sciences databases
- engineering and technology environmental engineering energy and fuels renewable energy wind energy
- natural sciences computer and information sciences artificial intelligence computational intelligence
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Programme(s)
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
Topic(s)
Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.
Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.
Call for proposal
Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
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Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
Funding Scheme
Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
Coordinator
AL1 3AW ST ALBANS
United Kingdom
The total costs incurred by this organisation to participate in the project, including direct and indirect costs. This amount is a subset of the overall project budget.