A hybrid wind speed forecasting model with rolling mapping decomposition and temporal convolutional networks

Xiangjun Cai, Dagang Li, Yuntao Zou, Zhichun Liu, Ali Asghar Heidari and Huiling Chen

Energy, 2025, vol. 324, issue C

Abstract: Accurate wind speed (WS) forecasting is essential for the effective integration of wind energy into power grids. However, the non-stationary and highly volatile nature of WS data presents significant challenges. Decomposition-based hybrid models have proven effective in addressing these issues, but many suffer from future information leakage during data preprocessing, which limits their real-world applicability. To address this limitation, this paper proposes a novel hybrid forecasting model that eliminates future information leakage while enhancing prediction accuracy. The model incorporates three key innovations: (1) a rolling mapping neural network (MNN) for real-time decomposition, designed to minimize boundary effects; (2) an improved artemisinin optimization (IAO) algorithm to enhance the performance of the MNN; and (3) temporal convolutional networks (TCNs) for precise subseries forecasting. The rolling framework ensures that only current and past data are used, thereby preventing future data from influencing the model during training. Experiments using hourly WS data from multiple locations in China demonstrate that the proposed IAO-MNN-TCN model outperforms traditional and existing hybrid models in terms of both accuracy and robustness. Comparative studies further confirm its superiority for short-term WS forecasting.

Keywords: Rolling decomposition; Wind speed forecasting; Mapping neural network; Temporal convolutional network; Improved artemisinin optimization (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0360544225013155
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:324:y:2025:i:c:s0360544225013155

DOI: 10.1016/j.energy.2025.135673

Access Statistics for this article

Energy is currently edited by Henrik Lund and Mark J. Kaiser

More articles in Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().