An Adaptive Approach for Probabilistic Wind Power Forecasting Based on Meta-Learning

TL;DR

This paper introduces an adaptive meta-learning framework for probabilistic wind power forecasting that enhances model flexibility across different locations and lead times through offline and online learning stages.

Contribution

The paper proposes a novel meta-learning based approach combining offline training and online incremental learning for improved probabilistic wind power forecasting.

Findings

Enhanced adaptability to different forecast tasks

Improved accuracy over existing methods

Effective in both temporal and spatial forecasting scenarios

Abstract

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.