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Article

Bi-xLSTM-Informer for Short-Term Photovoltaic Forecasting: Leveraging Temporal Symmetry and Feature Optimization

School of Business Administration, Liaoning Technical University, Huludao 125105, China
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Symmetry 2025, 17(9), 1469; https://doi.org/10.3390/sym17091469
Submission received: 6 August 2025 / Revised: 24 August 2025 / Accepted: 2 September 2025 / Published: 5 September 2025
(This article belongs to the Special Issue Applications Based on Symmetry in Machine Learning and Data Mining)

Abstract

Exploiting inherent symmetries in data and models is crucial for accurate renewable energy forecasting. To address limited accuracy improvements under complex temporal dependencies, this study proposes a hybrid Bi-xLSTM-Informer model that incorporates temporal symmetry via bidirectional processing of time-flipped sequences. First, key features are screened using the Boruta algorithm, followed by PCA dimensionality reduction to construct an optimal feature subset with orthogonal transformation properties. Second, a Bi-xLSTM-Informer hybrid forecasting model is constructed. In the xLSTM model, the mLSTM is modified into a bidirectional network structure to capture short-term fluctuation patterns via forward and time-reversed propagation; Informer then analyzes global dependencies via ProbSparse attention. Validated on data from the photovoltaic (PV) Power Plant AI Competition, the experimental results demonstrate that the Bi-xLSTM-Informer model achieves the best prediction performance and the lowest error among all compared models, with an R2 of 98.76% and an RMSE of 0.3776. This work proves that explicitly modeling temporal symmetry and feature orthogonality significantly enhances PV forecasting, providing an effective solution for renewable energy utilization.
Keywords: machine learning; xLSTM; informer; time series analysis; PV power prediction; Boruta algorithm machine learning; xLSTM; informer; time series analysis; PV power prediction; Boruta algorithm

Share and Cite

MDPI and ACS Style

Zhao, X.; Yang, T.; Li, Y.; Zhang, R. Bi-xLSTM-Informer for Short-Term Photovoltaic Forecasting: Leveraging Temporal Symmetry and Feature Optimization. Symmetry 2025, 17, 1469. https://doi.org/10.3390/sym17091469

AMA Style

Zhao X, Yang T, Li Y, Zhang R. Bi-xLSTM-Informer for Short-Term Photovoltaic Forecasting: Leveraging Temporal Symmetry and Feature Optimization. Symmetry. 2025; 17(9):1469. https://doi.org/10.3390/sym17091469

Chicago/Turabian Style

Zhao, Xin, Tao Yang, Yongli Li, and Ruixue Zhang. 2025. "Bi-xLSTM-Informer for Short-Term Photovoltaic Forecasting: Leveraging Temporal Symmetry and Feature Optimization" Symmetry 17, no. 9: 1469. https://doi.org/10.3390/sym17091469

APA Style

Zhao, X., Yang, T., Li, Y., & Zhang, R. (2025). Bi-xLSTM-Informer for Short-Term Photovoltaic Forecasting: Leveraging Temporal Symmetry and Feature Optimization. Symmetry, 17(9), 1469. https://doi.org/10.3390/sym17091469

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