  Wind energy plays a critical role in the transition towards renewable energy
sources. However, the uncertainty and variability of wind can impede its full
potential and the necessary growth of wind power capacity. To mitigate these
challenges, wind power forecasting methods are employed for applications in
power management, energy trading, or maintenance scheduling. In this work, we
present, evaluate, and compare four machine learning-based wind power
forecasting models. Our models correct and improve 48-hour forecasts extracted
from a numerical weather prediction (NWP) model. The models are evaluated on
datasets from a wind park comprising 65 wind turbines. The best improvement in
forecasting error and mean bias was achieved by a convolutional neural network,
reducing the average NRMSE down to 22%, coupled with a significant reduction in
mean bias, compared to a NRMSE of 35% from the strongly biased baseline model
using uncorrected NWP forecasts. Our findings further indicate that changes to
neural network architectures play a minor role in affecting the forecasting
performance, and that future research should rather investigate changes in the
model pipeline. Moreover, we introduce a continuous learning strategy, which is
shown to achieve the highest forecasting performance improvements when new data
is made available.
