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<p>Reliable ultra-long-horizon photovoltaic (PV) power forecasting is essential for ensuring grid stability and optimizing energy storage dispatch under high renewable penetration. However, existing paradigms struggle with the efficiency—accuracy paradox in long-range modeling and the systematic peak underestimation inherent in mean squared error (MSE) optimization. This paper proposes MambaFormer-TPEF, a synergistic forecasting framework that reconciles global sequence scanning with local feature refinement. Specifically, we develop a MambaSeq2Seq architecture to achieve <i>O</i>(<i>L</i>) linear complexity for 720-h historical inputs, coupled with attention mechanisms to capture fine-grained fluctuations. To rectify systematic peak bias, a two-stage peak enhancement strategy decouples peak magnitude regression from timing classification using heterogeneous tree-based experts. Furthermore, a meta-learning-based multi-level ensemble fusion (MLEF) framework is introduced to facilitate scenario-adaptive optimal weighting. Validated on real-world datasets for a 168-h prediction horizon, MambaFormer-TPEF achieves state-of-the-art performance with a mean absolute error (MAE) of 0.5521 and a root mean square error (RMSE) of 33.90 kW. Notably, it delivers an 8.15% improvement in <InlineEquation ID="IEq1"><EquationSource Format="TEX">\(R^2\)</EquationSource></InlineEquation> over leading benchmarks and significantly reduces peak prediction bias from −22.1 to −1.5%. This framework provides a scalable and robust solution for next-generation microgrid energy management systems, effectively bridging the gap between high-dimensional sequence modeling and real-time operational requirements.</p>

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MambaFormer-TPEF: a hybrid state space-transformer framework with two-stage peak enhancement for ultra-long sequence photovoltaic power forecasting

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年5月22日

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  • Jiayu Zou,
  • Xixiu Wu,
  • Qingyong Zhang,
  • Zirui Shao

摘要

Reliable ultra-long-horizon photovoltaic (PV) power forecasting is essential for ensuring grid stability and optimizing energy storage dispatch under high renewable penetration. However, existing paradigms struggle with the efficiency—accuracy paradox in long-range modeling and the systematic peak underestimation inherent in mean squared error (MSE) optimization. This paper proposes MambaFormer-TPEF, a synergistic forecasting framework that reconciles global sequence scanning with local feature refinement. Specifically, we develop a MambaSeq2Seq architecture to achieve O(L) linear complexity for 720-h historical inputs, coupled with attention mechanisms to capture fine-grained fluctuations. To rectify systematic peak bias, a two-stage peak enhancement strategy decouples peak magnitude regression from timing classification using heterogeneous tree-based experts. Furthermore, a meta-learning-based multi-level ensemble fusion (MLEF) framework is introduced to facilitate scenario-adaptive optimal weighting. Validated on real-world datasets for a 168-h prediction horizon, MambaFormer-TPEF achieves state-of-the-art performance with a mean absolute error (MAE) of 0.5521 and a root mean square error (RMSE) of 33.90 kW. Notably, it delivers an 8.15% improvement in \(R^2\) over leading benchmarks and significantly reduces peak prediction bias from −22.1 to −1.5%. This framework provides a scalable and robust solution for next-generation microgrid energy management systems, effectively bridging the gap between high-dimensional sequence modeling and real-time operational requirements.