# Evaluation of AEP Predictions for Commercial Wind Farms in Sweden
From these calculations, a P50 value (50 th percentile), which by definition has 50% probability of being exceeded for a real normal wind year, can be acquired. According to the standard [4], the predicted AEP (P50) is calculated as a mean value. This can be explained by the fact that the mean and median values coincide when under a normal distribution of AEP over time, which is reasonable to assume here. From the P50 value and expected uncertainties from different parameters, different \(p\)-values can be estimated. The wind resource evaluation and subsequent AEP prediction is an uncertain process, and a proper assessment of the uncertainty is critical for evaluating the risk with a planned wind energy development [5]. It is reasonable to assume that the accuracy of AEP predictions should improve with time. This is due to improvements such as access to better long-term corrected wind data, improved flow models with more advanced wind-shear calculation models, more detailed terrain data, improved tools for sector-based zero-plane displacement, refined measurement methods, improved standards, and a better understanding of cold-climate effects. In [6], a DNV GL validation between pre-construction AEP predictions and the outcome is presented for Great Britain, Ireland (including Northern Ireland), South Africa, and the UK offshore. For Great Britain, the AEP predictions were shown to have been overestimated by 3.1% based on data from 87 wind farms. Based on a smaller amount of data, Ireland and South Africa had 4% to 5% overestimation, whereas the offshore UK wind farms were only 0.4% under target. The pre-construction predictions were adjusted to comply with the latest methodology, independent of the construction year, making the results valid for projects developed in 2019. According to DNV GL, the overestimations are most likely linked to shortcomings at the pre-construction energy assessment stage. In [7], the neglect of the wind-farm blockage effect (i.e., that not only downstream turbines but also upstream turbines suffer from reduced wind speeds) is suggested to be a key reason for overestimating AEP predictions. Furthermore, AEP predictions have been compared to actual production data for individual wind farms [8; 9; 10], and the uncertainty of wind power AEP predictions have been the subject of several studies [5; 11; 12; 13]. In this study, production data from numerous existing wind farms in Sweden are compared with the pre-construction AEP predictions to evaluate their agreement and to determine whether the overestimations of the British Isles and South Africa cases are representable for Sweden as well. Time trends regarding the installation year have been analyzed to determine whether expected accuracy improvements with time can be observed. The results have also been divided by the terrain type to determine whether the complexity of the terrain affects the accuracy. Few available studies on the accuracy of AEP predictions are based on data from numerous wind turbines. This work is likely unique for the case of Sweden, and it also adds to the general knowledge base of the performance of AEP predictions for wind turbines.