Title: Long-term wind power prediction based on feature fusion model and temporal pattern attention mechanism

Authors: Li Liu; Ze Wang; Siwen Lei; Shengchi Liu; Hao Wang; Yue Jiang

Addresses: School of Information Science and Engineering, Dalian Polytechnic University, Dalian 116034, China ' School of Information Science and Engineering, Dalian Polytechnic University, Dalian 116034, China ' School of Information Science and Engineering, Dalian Polytechnic University, Dalian 116034, China ' School of Information Science and Engineering, Dalian Polytechnic University, Dalian 116034, China ' School of Information Science and Engineering, Dalian Polytechnic University, Dalian 116034, China ' School of Information Science and Engineering, Dalian Polytechnic University, Dalian 116034, China

Abstract: With the increasing global demand for clean energy, wind power has rapidly expanded as a renewable resource. However, the multidimensionality, long time series, and high volatility of wind power data pose significant challenges for long-term forecasting. This paper proposes a long-term wind power prediction model that utilises a feature fusion method and an attention mechanism. It integrates the strengths of the light gradient boosting machine (LightGBM) and long short-term memory (LSTM) algorithms, employing the temporal attention mechanism for fusion. The LightGBM algorithm handles multidimensional data and selects critical spatial features from wind farm data, while the LSTM network captures long-term dependencies in time-series data. The attention mechanism dynamically assigns weights to predictions based on specific conditions, allowing the model to focus on more relevant features during different periods and fluctuation regions. Experiments on data from multiple regions demonstrate that the proposed model outperforms existing methods, especially in long-term predictions.

Keywords: wind power; long-time series; spatial multi-features; temporal attention mechanism; feature fusion model.

DOI: 10.1504/IJSNET.2026.152040

International Journal of Sensor Networks, 2026 Vol.50 No.2, pp.96 - 108

Received: 03 Jul 2025
Accepted: 22 Jul 2025
Published online: 04 Mar 2026 *

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