Alleviating distribution shift and mining hidden temporal variations for ultra-short-term wind power forecasting

Haochong Wei, Yan Chen, Miaolin Yu, Guihua Ban, Zhenhua Xiong, Jin Su, Yixin Zhuo and Jiaqiu Hu

Energy, 2024, vol. 290, issue C

Abstract: Randomness and non-stationarity are common challenges in wind power forecasting (WPF). Many studies focus on randomness but usually ignore the non-stationarity which leads to distribution shift and affects prediction accuracy. To address the distribution shift problem, an alleviating distribution shift using the recent difference characterization (Dish-RDC) method is proposed as a general neural paradigm for WPF. Dish-RDC categorizes the distribution shift into intra-space and inter-space shifts. By employing the RDC, the method facilitates the mapping of input sequences to learnable distribution coefficients that better estimate the distribution. Furthermore, real-world time series often exhibit multi-periodicity, yet existing models face limitations in capturing this temporal variation. To address this issue, our research introduces the Temporal 2D-Variation Model (TimesNet) in WPF. This innovative model extends time variation analysis into a 2D space based on multi-periodicity. By using 2D kernels to model these variations, TimesNet can effectively incorporate advanced computer vision techniques into WPF. Combining these approaches, we developed Dish-RDC-TimesNet, a hybrid model. Experiments show it reduced mean absolute error (MAE) by 47.10 % and 20.63 % on two datasets compared to benchmark models. Moreover, integrating Dish-RDC with benchmark models decreased MAE by 39.79 % and 17.85 % on these datasets.

Keywords: Wind power forecasting; Distribution shift; Temporal variations; Deep learning; Multi-periodicity (search for similar items in EconPapers)
Date: 2024
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:290:y:2024:i:c:s0360544223034710

DOI: 10.1016/j.energy.2023.130077

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