Wind energy resource assessment typically requires numerical modelling, but this is too computationally intensive for multi-year timescales. Increasingly, unsupervised machine learning techniques are used to identify small numbers of representative weather patterns that can help simulate long-term behaviour. Here we develop a novel wind energy workflow that for the first time combines the weather patterns from unsupervised clustering with a numerical weather prediction model (WRF) to obtain efficient and accurate long-term predictions of wind farm power and downstream wakes. We use ERA5 reanalysis data and, for the first time, compare clustering on low altitude pressure and wind velocity, a more relevant variable for wind resource assessment. We also compare varying domain sizes for the clustering. A WRF simulation is run at each cluster centre and the results aggregated into a long-term prediction using a novel post-processing technique. We consider two case study regions and show that our long-term predictions achieve excellent agreement with a year of WRF simulations in 2% of the computational time. The most accurate results are obtained when clustering on wind velocity. Clustering over a Europe-wide domain is sufficient for predicting wind farm power output, but clustering over smaller domains is required for downstream wake predictions. Our approach facilitates multi-year predictions of power output and downstream farm wakes, by providing a fast, accurate and flexible methodology applicable to any global region. Moreover, this constitutes the first tool to help mitigate effects of wind energy loss downstream of wind farms, via the identification of optimum wind farm locations.