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

arXiv: https://arxiv.org/abs/2308.07980 · HTML: https://mlsanity.com/paper/2308.07980/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 51 references that are in the map.
- First posted on arXiv 15 Aug 2023.

## Builds on (most cited first, 9 of 9)

- [Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks](https://mlsanity.com/paper/1703.03400/) (2017)
- [Prototypical Networks for Few-shot Learning](https://mlsanity.com/paper/1703.05175/) (2017)
- [Meta-Learning in Neural Networks: A Survey](https://mlsanity.com/paper/2004.05439/) (2020)
- [Bilevel Programming for Hyperparameter Optimization and Meta-Learning](https://mlsanity.com/paper/1806.04910/) (2018)
- [How to train your MAML](https://mlsanity.com/paper/1810.09502/) (2018)
- [Meta-Learning Update Rules for Unsupervised Representation Learning](https://mlsanity.com/paper/1804.00222/) (2018)
- [Meta-learning curiosity algorithms](https://mlsanity.com/paper/2003.05325/) (2020)
- [Spatio-temporal DeepKriging for Interpolation and Probabilistic Forecasting](https://mlsanity.com/paper/2306.11472/) (2023)
- [Appendix for Nonparametric Multivariate Probability Density Forecast in Smart Grids With Deep Learning](https://mlsanity.com/paper/2210.15463/) (2022)

## Built on by (most cited first, 1 of 1)

- [Few-Shot Load Forecasting Under Data Scarcity in Smart Grids: A Meta-Learning Approach](https://mlsanity.com/paper/2406.05887/) (2024)

## Abstract (arXiv)

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.