A comprehensive review on wind resource extrapolation models applied in wind energy

A review spanning across a 40-year period (1978-2018) and including a total of 332 applications has beenaddressed on theoretical and empirical wind resource extrapolation models applied in wind energy, which canbe grouped into three main families: (i) the logarithmic models; (ii) the Deaves and Harris (DH) model; (iii) thepower law (PL). Applied over 96 very heterogeneous locations worldwide, models have been tested againstobservations at upper extrapolation height and assessed by location characteristics, extrapolation range skills,and application economical advantages.The logarithmic models can nowadays be considered unsuitable for extrapolating wind resource to hub heightof current multi-MW WTs, mainly because exhibiting a limited extrapolation range capability (about 10-50mmedian bin). Finer scores in extrapolating wind resource (mean absolute bias of 3.3%) and in predicting energyoutput (10.1%) were achieved by the DH model, also showing remarkable extrapolation range skills (10-80mmedian bin). However, although among the most economical and forward-looking solutions, its need for accuratez0 assessment and u* observations resulted so far in great limitations to its large-scale application for windenergy purposes (less than 1%). Eventually, the PL confirmed the most reliable - and largely most commonlyused (73.5%) - approach for wind energy applications. Out of the plethora of PL models developed in theliterature, the PL(?)-?lower and the PL(?)-?I were the finest in predicting both extrapolated wind resource (meanabsolute error of 4% and 4.4%, respectively) and energy output (8.9% and 5.5%), also exhibiting extrapolationrange skills meeting modern WTs requirements. By contrast, the PL using ?=1/7 returned among the worstscores, yet resulting - since the simplest - the solution most frequently applied (19.6%). This study also demonstratedthat extrapolation tools requiring the most expensive instrumentation equipment do not necessarilyreturn the finest scores.