We have presented a new methodology to identify a set of characteristic wind profile shapes using clustering for estimating site specific airborne wind resource. The wind resource representation is used for fast AEP calculations for pumping AWE systems. This is necessary because the validity of conventional wind profile parametrisations, such as the logarithmic profile, is limited for the full height range in which these systems operate.

A huge number of wind turbines have reached their designated lifetime of 20 years. Most of the turbines installed were overdesigned. In practice, these turbines could potentially operate longer to increase the energy yield. For the use case turbine considered in this work, a simple lifetime extension of 8.7 years increases the energy yield by 43.5 %. When the swept rotor area is increased by means of a blade tip extension, the yield is increased by additional 2.3 %.

An accurate assessment of the wind resource at hub-height is necessary for an efficient and bankable wind farm project. Conventional techniques for wind speed vertical extrapolation include a power law and a logarithmic law. Here, we propose a round-robin validation to assess the benefits that a machine learning-based approach can provide in vertically extrapolating wind speed at a location different from the training site – the most practically useful application for the wind energy industry.