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

<p>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 offsetagainst the financial penalty incurred in purchasing time series data for input.</p>