An Adaptive Approach for Probabilistic Wind Power Forecasting Based on
  Meta-Learning
Zichao Meng Ye Guo Hongbin Sun
This paper studies an adaptive approach for probabilistic wind power
forecasting (WPF) including offline and online learning procedures. In the
offline learning stage, a base forecast model is trained via inner and outer
loop updates of meta-learning, which endows the base forecast model with
excellent adaptability to different forecast tasks, i.e., probabilistic WPF
with different lead times or locations. In the online learning stage, the base
forecast model is applied to online forecasting combined with incremental
learning techniques. On this basis, the online forecast takes full advantage of
recent information and the adaptability of the base forecast model. Two
applications are developed based on our proposed approach concerning
forecasting with different lead times (temporal adaptation) and forecasting for
newly established wind farms (spatial adaptation), respectively. Numerical
tests were conducted on real-world wind power data sets. Simulation results
validate the advantages in adaptivity of the proposed methods compared with
existing alternatives.