基于长短时Mamba的风电功率多步预测

景惠甜; 韩 丽; 王晓静; 袁志鑫

doi:10.19783/j.cnki.pspc.260099

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基于长短时Mamba的风电功率多步预测

应用研究 | 更新时间：2026-10-15

- 基于长短时Mamba的风电功率多步预测
- Multi-step wind power forecasting based on long-short-term Mamba
- 电力系统保护与控制 2026年54卷第20期
- 作者机构：
  
  中国矿业大学电气工程学院,江苏,徐州,221116
- 作者简介：
- 基金信息：
- DOI：10.19783/j.cnki.pspc.260099
  中图分类号：
- 纸质出版：2026
- 稿件说明：
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景惠甜, 韩丽, 王晓静, 等. 基于长短时Mamba的风电功率多步预测[J]. 电力系统保护与控制, 2026,54(20).

JING Huitian, HAN Li, WANG Xiaojing, et al. Multi-step wind power forecasting based on long-short-term Mamba[J]. Power System Protection and Control, 2026, 54(20).
景惠甜, 韩丽, 王晓静, 等. 基于长短时Mamba的风电功率多步预测[J]. 电力系统保护与控制, 2026,54(20). DOI： 10.19783/j.cnki.pspc.260099.

JING Huitian, HAN Li, WANG Xiaojing, et al. Multi-step wind power forecasting based on long-short-term Mamba[J]. Power System Protection and Control, 2026, 54(20). DOI： 10.19783/j.cnki.pspc.260099.

摘要

准确的风电功率预测对风电场运行与电网调度优化至关重要。针对风电功率时间序列蕴含长期趋势与短期波动等多尺度动态特性，长期趋势与短期波动对模型记忆能力的要求不同，现有方法难以同时高效捕捉的问题，提出一种基于长短时Mamba(long-short-term Mamba

LSTMA)的风电功率多步预测方法。首先，将Mamba结合两种高阶多项式投影算子(high-order polynomial projection operator

HiPPO)初始化策略构建双通道状态空间模块，分别实现对长期趋势的记忆保持与对短期波动的精细化跟踪。然后，引入专家混合(mixture of experts

MoE)机制构建动态路由网络，自适应选择最优patch大小以处理短期波动模式。最后，设计门控函数动态融合双通道输出，实现多尺度预测结果的自适应加权。基于中国某风电场实际数据的实验表明，所提模型在4个季节的预测平均绝对百分比误差(mean absolute percentage error

MAPE)稳定在8%以下，决定系数保持在97%以上，较对比模型精度有所提升，验证了其在多尺度特征捕捉上的优越性。

Abstract

Accurate wind power forecasting is crucial for wind farm operation and grid dispatch optimization. Wind power time series exhibit multi-scale dynamic characteristics

including long-term trends and short-term fluctuations

which impose different requirements on the memory capacity of the model. Existing methods struggle to efficiently capture both types of dynamics simultaneously. To address this issue

a multi-step wind power forecasting method based on long-short-term Mamba (LSTMA) is proposed. First

Mamba is combined with two initialization strategies based on high-order polynomial projection operator (HiPPO) to construct a dual-channel state-space module

enabling the retention of long-term patterns and the fine-grained tracking of short-term fluctuations

respectively. Then

a mixture of experts (MoE) mechanism is introduced to build a dynamic routing network that adaptively selects the optimal patch size to process short-term fluctuation patterns. Finally

a gating function is designed to dynamically fuse the dual-channel outputs

achieving adaptive weighting of multi-scale forecasting results. Experiments based on real data from a wind farm in China show that the proposed model maintains a mean absolute percentage error (MAPE) below 8% and a coefficient of determination above 97% across all four seasons. Compared with the benchmark models

the proposed model improves forecasting accuracy

validating its superiority in multi-scale feature capturing.

关键词

Keywords

references

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