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A neural network version of the measure correlate predict algorithm for estimating wind energy yield

By J. F. Dale Addison, Andrew Hunter, Jeremy Bass and Matt Rebbeck


We have investigated the feasibility of using neural networks to make predictions of long term energy yield at a potential wind farm site. This paper considers the effectiveness of neural networks in predicting wind speed at a target site from wind speed and direction measurements at a reference site. The technique is compared with the standard Measure Correlate Predict (MCP) algorithm used in the wind energy industry. Improvements of predictive accuracy in the region of 5%-12% can be achieved. Best results are obtained using multilayer perceptron networks with a large number of hidden units, with extensive Quasi-Newton (BFGS) training. Experiments have been conducted using contemporaneous measurements, and time shifted wind speed (previous and next hour) as inputs. Performance is consistently improved by using time-shifted inputs. However, the improvement in performance has to be offset\ud against the financial penalty incurred in purchasing time series data for input

Topics: G730 Neural Computing
Year: 2000
OAI identifier: oai:eprints.lincoln.ac.uk:1892
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