Work

Multi-step prediction of wind power ramping based on dynamic historical window adjustment

Year: 2025

Type: article

Abstract: Wind power ramp events, with their sudden nature, can significantly impact grid stability. Traditional forecasting methods often employ fixed-size time windows, which fail to adapt to the intermittent fluctuations characteristic of power ramp scenarios. To address this limitation, this paper proposes an A-TSMixer model incorporating dynamic historical windows for wind power ramp forecasting. Building upon TSMixer's inherent strength in long-sequence prediction, the model introduces an adaptive window mechanism that dynamically adjusts the reference sequence length based on power fluctuation intensity. Additionally, to mitigate the adverse effects of high-frequency noise in raw data, the model employs RTS smoothing algorithm with bidirectional processing for data preprocessing. Experimental results demonstrate that the proposed A-TSMixer model achieves significantly improved forecasting accuracy compared to conventional approaches in wind power ramp prediction. • A novel forecasting method for improving wind power ramp prediction accuracy. • A dynamic time window strategy is introduced to adapt to wind power fluctuations. • The RTS smoothing algorithm is employed for data preprocessing. • The proposed A-TSMixer model significantly improves wind power ramp prediction accuracy.

Source: Energy Reports

DOI: 10.1016/j.egyr.2025.08.004

Authors: Tingqi Zhang, Junjie Sun, Qiang Zhang, Jifeng Cheng, Peng Yuan

Institution: State Grid Liaoning Electric Power Company (China)

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: $3,040