Work

A novel method for multivariate short-term offshore wind forecasting via time–frequency clustering and inverted attention

Year: 2025

Type: article

Abstract: Short-term offshore wind power forecasting is crucial for stable power system operations. However, accurate forecasting is hindered by multivariate interactions that generate multi-scale repetitive patterns and obscure cross-variate correlations. In this paper, we propose a hierarchical framework, the Time–Frequency Clustering Inverted Transformer, for multivariate offshore wind power forecasting. First, a Time–Frequency Clustering component applies Wavelet Packet Decomposition to each series and quantifies sub-series similarity by overall activity and evolutionary trend, grouping repetitive patterns into structured clusters. Second, an inverted Transformer captures multivariate correlations within clusters by treating time points of individual sub-series as variate tokens, enabling self-attention to focus on multivariate correlations rather than temporal dependencies. On two real-world offshore wind datasets (horizons 8–48 h), our proposed framework reduces MSE/MAE by 14.11% and outperforms 12 recognised baselines (e.g., PatchTST, TimesNet), with the advantage persisting even when the TFC component is applied to the baselines. Moreover, our method demonstrates remarkable generalisability on three public datasets (Solar-Energy, Traffic, and ECL), reducing MSE/MAE by 7.36%. These results indicate that associating repetitive patterns with attention to cross-variate structure materially improves multivariate offshore wind power forecasting.

Source: Energy and AI

DOI: 10.1016/j.egyai.2025.100654

Authors: Keqi Chen, Tianshuai Pei, Lina Yang, Thomas Wu, Yunxuan Dong

Institution: Guangxi University

Language: English

Topic: Energy Load and Power Forecasting

Subfield: Electrical and Electronic Engineering

Field: Engineering

Domain: Physical Sciences

SDGs: Affordable and clean energy, Climate action

Open Access status: gold

Estimated APC paid: $3,630