{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,29]],"date-time":"2026-01-29T21:47:15Z","timestamp":1769723235066,"version":"3.49.0"},"reference-count":31,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2025,9,5]],"date-time":"2025-09-05T00:00:00Z","timestamp":1757030400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Ministry of Education of China Humanities and Social Sciences Youth Foundation Project","award":["24YJC630298"],"award-info":[{"award-number":["24YJC630298"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.3390\/sym17091469","type":"journal-article","created":{"date-parts":[[2025,9,5]],"date-time":"2025-09-05T14:53:01Z","timestamp":1757083981000},"page":"1469","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Bi-xLSTM-Informer for Short-Term Photovoltaic Forecasting: Leveraging Temporal Symmetry and Feature Optimization"],"prefix":"10.3390","volume":"17","author":[{"given":"Xin","family":"Zhao","sequence":"first","affiliation":[{"name":"School of Business Administration, Liaoning Technical University, Huludao 125105, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tao","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Business Administration, Liaoning Technical University, Huludao 125105, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1979-9057","authenticated-orcid":false,"given":"Yongli","family":"Li","sequence":"additional","affiliation":[{"name":"School of Business Administration, Liaoning Technical University, Huludao 125105, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ruixue","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Business Administration, Liaoning Technical University, Huludao 125105, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,9,5]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Sun, Y., Wang, Z., Wang, J., and Li, Q. 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