Abstract
The use of linear correlation techniques to extend monitoring mast records is an important step in developing the energy estimates that underpin wind development business cases. Yet accuracy of the linear correlation technique rapidly degrades in complex conditions, and only allows correlation of a small number of parameters without introducing overwhelming model complexity. This paper argues that artificial neural network (ANN) models offer an alternative that is simple to use, afford greater correlation options and provide improved data extension accuracy. This was explored through a practical data extension example, with the linear and ANN correlation performances directly compared. The case study showed that the ANN correlation method provided superior accuracy compared to the linear correlation method, including a three fold improvement in Annual Energy Prediction (AEP) error.
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